Prognostics and Health Management (PHM) is being widely applied in many industrial systems to ensure high system availability over their life cycle. This web seminar will present key steps of PHM: data processing, feature extraction, fault diagnostics, and failure prognostics. The fundamental algorithms, models and techniques for each step will be discussed. Time domain, frequency domain and time frequency data analysis are introduced, and the corresponding feature extraction technologies presented. Mode-based and data-driven-based approaches are described in fault diagnostics and failure prognostics.
IoT is reshaping the manufacturing and industrial processes, effectively changing the paradigm from one of repair and replace to more of predict and prevent. Using data streaming from connected equipment and machinery, organizations can now monitor the health of their assets and effectively predict when and how an asset might fail. However, without the right data management strategy and tools, investments in IoT can yield limited results. Join Cloudera and Tata Consultancy Services (TCS) for a joint webinar to learn more about how organizations are using advanced analytics and machine learning to drive IoT enabled predictive maintenance.
In 2012, The Economist claimed we were entering the third industrial revolution based on the digitization of manufacturing, also referred to as the “smart factory.” The development and adoption of the Internet of Things is a critical element of smart manufacturing as reduced sensor device cost, and increased connectivity and in-memory processing give manufacturers the ability to gather and use data to increase product quality and transform operations. IT organizations are increasingly involved with the management, security and governance of this data as equipment and products are connected to the internet. This session will provide a practical framework for evaluating ways to improve sensor enablement, transaction processes and analytics based on real-world customer examples.
Nerospec IIoT is part of a group of companies, Nerospec Core is a group of key individuals specialising in Industrial Networks, Mining, Machine Automation and of course IIoT. The Internet of Things (IoT) industry has come about due to the need for effective tools to transform outdated processes into the Industrial 4.0 revolution and streamline machine data.
This document discusses approaches to implementing Manufacturing Execution Systems (MES). It begins by defining MES and describing the ISA-95 manufacturing operations model. It then contrasts two approaches: the "big bang" implementation of all MES functionality at once versus incremental implementation by selecting individual capabilities. The document advocates for the incremental approach, arguing it has advantages in terms of cost, change management, implementation complexity and return on investment analysis. It provides guidance on developing a long-term roadmap for MES implementation, including establishing goals, identifying opportunities and building implementation plans in a collaborative manner.
Synthetic Data Generation for Statistical TestingLionel Briand
1) The document describes an approach for automatically generating synthetic test data that is both logically valid and statistically representative of real data for testing data-centric systems.
2) The approach takes as input a data schema, statistical characteristics of the data elements, and data validity constraints. It then generates an initial valid data sample before improving representativeness through "corrective constraints".
3) An evaluation on generating test data for a tax management system found the approach could produce samples of up to 1000 instances in under 10 hours, and that the generated data was both valid and statistically representative, outperforming the state-of-the-art.
Exploring Best Practises in Design of Experiments: A Data Driven Approach to ...JMP software from SAS
Learn about best practises in the
design of experiments and a data-driven approach to DOE that increases robustness, efficiency and effectiveness. This was presented at a JMP seminar in the UK.
IoT is reshaping the manufacturing and industrial processes, effectively changing the paradigm from one of repair and replace to more of predict and prevent. Using data streaming from connected equipment and machinery, organizations can now monitor the health of their assets and effectively predict when and how an asset might fail. However, without the right data management strategy and tools, investments in IoT can yield limited results. Join Cloudera and Tata Consultancy Services (TCS) for a joint webinar to learn more about how organizations are using advanced analytics and machine learning to drive IoT enabled predictive maintenance.
In 2012, The Economist claimed we were entering the third industrial revolution based on the digitization of manufacturing, also referred to as the “smart factory.” The development and adoption of the Internet of Things is a critical element of smart manufacturing as reduced sensor device cost, and increased connectivity and in-memory processing give manufacturers the ability to gather and use data to increase product quality and transform operations. IT organizations are increasingly involved with the management, security and governance of this data as equipment and products are connected to the internet. This session will provide a practical framework for evaluating ways to improve sensor enablement, transaction processes and analytics based on real-world customer examples.
Nerospec IIoT is part of a group of companies, Nerospec Core is a group of key individuals specialising in Industrial Networks, Mining, Machine Automation and of course IIoT. The Internet of Things (IoT) industry has come about due to the need for effective tools to transform outdated processes into the Industrial 4.0 revolution and streamline machine data.
This document discusses approaches to implementing Manufacturing Execution Systems (MES). It begins by defining MES and describing the ISA-95 manufacturing operations model. It then contrasts two approaches: the "big bang" implementation of all MES functionality at once versus incremental implementation by selecting individual capabilities. The document advocates for the incremental approach, arguing it has advantages in terms of cost, change management, implementation complexity and return on investment analysis. It provides guidance on developing a long-term roadmap for MES implementation, including establishing goals, identifying opportunities and building implementation plans in a collaborative manner.
Synthetic Data Generation for Statistical TestingLionel Briand
1) The document describes an approach for automatically generating synthetic test data that is both logically valid and statistically representative of real data for testing data-centric systems.
2) The approach takes as input a data schema, statistical characteristics of the data elements, and data validity constraints. It then generates an initial valid data sample before improving representativeness through "corrective constraints".
3) An evaluation on generating test data for a tax management system found the approach could produce samples of up to 1000 instances in under 10 hours, and that the generated data was both valid and statistically representative, outperforming the state-of-the-art.
Exploring Best Practises in Design of Experiments: A Data Driven Approach to ...JMP software from SAS
Learn about best practises in the
design of experiments and a data-driven approach to DOE that increases robustness, efficiency and effectiveness. This was presented at a JMP seminar in the UK.
Catarata é um embaçamento do cristalino que causa perda da visão. É causada principalmente pelo envelhecimento, mas também por ferimentos, doenças ou medicamentos. Tratamentos caseiros incluem aplicar argila verde ou bentonita nos olhos e usar gotas de mel da abelha jataí ou vinagre de maçã.
This document discusses how smart manufacturing and artificial intelligence of things (AIoT) can help drive digital transformation. It provides examples of how IoT solutions have helped various companies reduce costs and improve operations. It then discusses key concepts in smart manufacturing like the intelligent edge, cloud computing, and different waves of innovation with IoT, edge, and AI. The document outlines Microsoft's IoT portfolio and reference architecture for smart manufacturing. It also describes various Azure IoT capabilities and solutions like IoT Hub, IoT Edge, Time Series Insights, and preconfigured solutions for predictive maintenance, remote monitoring and connected factories. Finally, it discusses how machine learning can address supply chain optimization, predictive maintenance, anomaly detection, production scheduling and demand
The document discusses Industry 4.0 and smart manufacturing. It describes how integrating cyber-physical systems can enable real-time monitoring, response, execution, and decision making. This allows for self-organizing production, self-maintaining assets, and intelligent reporting. The document also outlines questions around modeling, order execution, data analytics, and achieving autonomous operations.
Predictive Maintenance in the Industrial Internet of ThingsTibbo
Predictive maintenance uses sensors and remote monitoring to analyze equipment performance data over time. This allows maintenance to shift from a schedule-based to a condition-based model. By monitoring key performance indicators and detecting patterns in the data, predictive systems can estimate time to failure and remaining useful life. This enables repairs to be performed proactively before equipment actually fails.
You’ve heard about Weibull Analysis, and want to know what it can be used for, OR you’ve used Weibull Analysis in the past, but have forgotten some of the background and uses….
This webinar looks at giving you the background of Weibull Analysis, and its use in analyzing failure modes. Starting from basics and giving examples of its uses in answering the questions:
• How many do I test, for how long?
• Is our design system wrong?
• How many more failures will I have in the next month, year, 5 years?
Sit in and listen and ask your questions … not detailed “How to” but “When & Why to”!
The document discusses Taguchi screening designs, which are a type of experimental design used in product development to identify the main factors affecting a process using a minimal number of tests. It explains key terms like experimental design, screening design, and Taguchi method. The document compares screening designs to full factorials and lists advantages and disadvantages of each. It provides details on how to set up and analyze Taguchi screening designs, including determining variables and levels, selecting a screening design, setting up the test matrix, analyzing main effects plots, and confirming results. Resources on experimental design are also listed.
The Predictive Maintenance solution accelerator is an end-to-end solution for a business scenario that predicts the point at which a failure is likely to occur. Use this solution accelerator proactively to optimize maintenance and to create automatic alerts and actions for remote diagnostics, maintenance requests, and other workflows. The solution combines key Azure IoT services like IoT Hub and Stream analytic.
Artificial Intelligence (AI) is shaping and reshaping every industry under the sun. The Healthcare industry is not any exception.
In this presentation, I have discussed the basics of AI as well as how it is being used in various branches of the healthcare industry. I presented this topic in my departmental seminar in October 2021 and received appreciation as well as positive feedback in this regard.
Failure Mode and Effect Analysis (FMEA) Manual.
- The role and function of FMEA.
- Concepts and techniques of Design FMEA and how to apply it.
- Concepts and techniques of Process FMEA and how to apply it.
- The role and function of FTA.
- Concepts of Zero Quality Control and Mistake Proofing and its implications for FMEA.
Pharmaceutical companies are increasingly recognizing the value of real-world evidence and digital health technologies. Real-world data from electronic health records, wearable devices, and other sources can provide insights into drug effectiveness outside of controlled clinical trials. This data has the potential to transform drug development and delivery of personalized healthcare. It allows evaluation of treatments using broader and longer-term patient data. Pharma is exploring applications of real-world evidence such as improving clinical trial design and identifying new drug targets and uses based on unanticipated real-world findings. Widespread collection and use of real-world data may help address industry challenges like rising development costs and ensuring drug safety.
THE PATH TOWARDS THE US BATTERY SUPPLY CHAIN INDEPENDENCEiQHub
This document summarizes a presentation on global battery supply chains and the implications of the US Inflation Reduction Act. It discusses growing demand for electric vehicle batteries and the need for increased production capacity of battery components like cathodes, anodes, lithium and nickel. It notes most battery production currently occurs in China and outlines requirements in the Inflation Reduction Act aimed at establishing more domestic US and allied country supply chains. The Act provides tax credits for electric vehicles meeting certain battery mineral and component sourcing standards from the US and allies.
Deep Learning for industrial Prognostics & Health Management (PHM) Michael Giering
The document discusses United Technologies Corporation's application of deep learning techniques to problems in aerospace and building systems. Specifically, it discusses using deep belief networks for aircraft sensor diagnostics at Pratt & Whitney and Otis elevators prognostic health monitoring. It also discusses using deep autoencoders for chiller power estimation at Carrier Climate Control systems. The approaches analyzed sensor data using deep learning models to provide diagnostics, predict health issues, and estimate power usage.
Prognostic Health Management (PHM) uses health monitoring and prognostics to predict product failures by assessing degradation from normal operating conditions. Traditional reliability predictions are inaccurate, while PHM is more suitable as it considers actual usage. Research is being conducted to improve PHM models, sensors, communication and decision making to make reliability predictions more realistic. PHM is expected to become a cost-effective solution for predicting electronics reliability due to increasing electronics usage and demand for more reliable products.
Applying a New Generation of Prognostics Across the Industrial InternetSentient Science
Sentient Science provides model-based prognostics and computational services to help extend the remaining useful life of mechanical systems. They apply these services across the industrial internet by connecting internal machine data to external analytics and using predictive models to optimize maintenance, reduce costs, and improve performance for clients in industries like wind energy, manufacturing, and aviation. The presentation outlines how Sentient Science's prognostics approach can generate savings and efficiencies for asset-intensive companies participating in the emerging industrial internet.
This document summarizes Louis Redding's doctoral research on Integrated Vehicle Health Management (IVHM). The research aims to map high-value IVHM applications in the UK and create a decision tool to evaluate benefits of new applications. Key objectives are to understand awareness and adoption of IVHM in UK manufacturing, identify capabilities needed for IVHM development, and examine examples of success and failure. The research framework involves a literature review identifying gaps, a survey to understand industry awareness, and case studies. The goal is to construct a validated simulation model or decision framework to inform UK manufacturers on IVHM evaluation and adoption.
Hospital Facility Maintainance - Why & HowMatt Hicks
The University of Michigan Hospitals and Health Centers has a policy to provide an environment of care that is safe, functional, supportive and effective in accomplishing their mission. The Environment of Care Committee is responsible for managing the environment of care according to existing plans and policies and reporting to institutional leadership. The environment of care considers elements that contribute to how space feels and works for patients, families, visitors, and staff, including factors beyond physical comfort and safety like supporting patient needs and dignity.
The document discusses next steps for improving plant hospital maintenance at a hospital through better planning and scheduling. It recommends analyzing maintenance history, establishing key performance indicators, moving to reliability-centered maintenance through improved planning and scheduling, and process mapping. Appropriate staffing levels for planners and supervisors are discussed based on trade skills. The vision and mission focus on maintenance excellence and customer service. Anticipated outcomes include improved patient environment and satisfaction through more efficient, integrated maintenance management.
The document discusses the IMS Center, which aims to transform traditional "fail and fix" maintenance practices to "predict and prevent" through technologies like embedded monitoring, prognostics, and decision support tools. The Center develops the Watchdog Agent toolbox containing analytical tools to assess machine performance and predict failures using sensor data. It also provides decision support tools to prioritize maintenance work orders. The Center works with industry and academic partners on projects applying these methods to manufacturing, energy, vehicles and other areas.
PHM - Risk Minimisation [Airforce Institute Presentation]zoomdust
The document discusses various issues related to risk minimization and prognostics and health management (PHM) technologies. It covers key drivers of risk like safety, reliability and cost. It also discusses integrated logistics support (ILS), performance-based logistics (PBL) and reliability-availability-maintainability (RAM) approaches. Barriers to effective condition-based maintenance include incomplete failure analysis and a lack of standardized failure taxonomy. Model-based failure analysis can help enhance prognostics and health management systems.
Catarata é um embaçamento do cristalino que causa perda da visão. É causada principalmente pelo envelhecimento, mas também por ferimentos, doenças ou medicamentos. Tratamentos caseiros incluem aplicar argila verde ou bentonita nos olhos e usar gotas de mel da abelha jataí ou vinagre de maçã.
This document discusses how smart manufacturing and artificial intelligence of things (AIoT) can help drive digital transformation. It provides examples of how IoT solutions have helped various companies reduce costs and improve operations. It then discusses key concepts in smart manufacturing like the intelligent edge, cloud computing, and different waves of innovation with IoT, edge, and AI. The document outlines Microsoft's IoT portfolio and reference architecture for smart manufacturing. It also describes various Azure IoT capabilities and solutions like IoT Hub, IoT Edge, Time Series Insights, and preconfigured solutions for predictive maintenance, remote monitoring and connected factories. Finally, it discusses how machine learning can address supply chain optimization, predictive maintenance, anomaly detection, production scheduling and demand
The document discusses Industry 4.0 and smart manufacturing. It describes how integrating cyber-physical systems can enable real-time monitoring, response, execution, and decision making. This allows for self-organizing production, self-maintaining assets, and intelligent reporting. The document also outlines questions around modeling, order execution, data analytics, and achieving autonomous operations.
Predictive Maintenance in the Industrial Internet of ThingsTibbo
Predictive maintenance uses sensors and remote monitoring to analyze equipment performance data over time. This allows maintenance to shift from a schedule-based to a condition-based model. By monitoring key performance indicators and detecting patterns in the data, predictive systems can estimate time to failure and remaining useful life. This enables repairs to be performed proactively before equipment actually fails.
You’ve heard about Weibull Analysis, and want to know what it can be used for, OR you’ve used Weibull Analysis in the past, but have forgotten some of the background and uses….
This webinar looks at giving you the background of Weibull Analysis, and its use in analyzing failure modes. Starting from basics and giving examples of its uses in answering the questions:
• How many do I test, for how long?
• Is our design system wrong?
• How many more failures will I have in the next month, year, 5 years?
Sit in and listen and ask your questions … not detailed “How to” but “When & Why to”!
The document discusses Taguchi screening designs, which are a type of experimental design used in product development to identify the main factors affecting a process using a minimal number of tests. It explains key terms like experimental design, screening design, and Taguchi method. The document compares screening designs to full factorials and lists advantages and disadvantages of each. It provides details on how to set up and analyze Taguchi screening designs, including determining variables and levels, selecting a screening design, setting up the test matrix, analyzing main effects plots, and confirming results. Resources on experimental design are also listed.
The Predictive Maintenance solution accelerator is an end-to-end solution for a business scenario that predicts the point at which a failure is likely to occur. Use this solution accelerator proactively to optimize maintenance and to create automatic alerts and actions for remote diagnostics, maintenance requests, and other workflows. The solution combines key Azure IoT services like IoT Hub and Stream analytic.
Artificial Intelligence (AI) is shaping and reshaping every industry under the sun. The Healthcare industry is not any exception.
In this presentation, I have discussed the basics of AI as well as how it is being used in various branches of the healthcare industry. I presented this topic in my departmental seminar in October 2021 and received appreciation as well as positive feedback in this regard.
Failure Mode and Effect Analysis (FMEA) Manual.
- The role and function of FMEA.
- Concepts and techniques of Design FMEA and how to apply it.
- Concepts and techniques of Process FMEA and how to apply it.
- The role and function of FTA.
- Concepts of Zero Quality Control and Mistake Proofing and its implications for FMEA.
Pharmaceutical companies are increasingly recognizing the value of real-world evidence and digital health technologies. Real-world data from electronic health records, wearable devices, and other sources can provide insights into drug effectiveness outside of controlled clinical trials. This data has the potential to transform drug development and delivery of personalized healthcare. It allows evaluation of treatments using broader and longer-term patient data. Pharma is exploring applications of real-world evidence such as improving clinical trial design and identifying new drug targets and uses based on unanticipated real-world findings. Widespread collection and use of real-world data may help address industry challenges like rising development costs and ensuring drug safety.
THE PATH TOWARDS THE US BATTERY SUPPLY CHAIN INDEPENDENCEiQHub
This document summarizes a presentation on global battery supply chains and the implications of the US Inflation Reduction Act. It discusses growing demand for electric vehicle batteries and the need for increased production capacity of battery components like cathodes, anodes, lithium and nickel. It notes most battery production currently occurs in China and outlines requirements in the Inflation Reduction Act aimed at establishing more domestic US and allied country supply chains. The Act provides tax credits for electric vehicles meeting certain battery mineral and component sourcing standards from the US and allies.
Deep Learning for industrial Prognostics & Health Management (PHM) Michael Giering
The document discusses United Technologies Corporation's application of deep learning techniques to problems in aerospace and building systems. Specifically, it discusses using deep belief networks for aircraft sensor diagnostics at Pratt & Whitney and Otis elevators prognostic health monitoring. It also discusses using deep autoencoders for chiller power estimation at Carrier Climate Control systems. The approaches analyzed sensor data using deep learning models to provide diagnostics, predict health issues, and estimate power usage.
Prognostic Health Management (PHM) uses health monitoring and prognostics to predict product failures by assessing degradation from normal operating conditions. Traditional reliability predictions are inaccurate, while PHM is more suitable as it considers actual usage. Research is being conducted to improve PHM models, sensors, communication and decision making to make reliability predictions more realistic. PHM is expected to become a cost-effective solution for predicting electronics reliability due to increasing electronics usage and demand for more reliable products.
Applying a New Generation of Prognostics Across the Industrial InternetSentient Science
Sentient Science provides model-based prognostics and computational services to help extend the remaining useful life of mechanical systems. They apply these services across the industrial internet by connecting internal machine data to external analytics and using predictive models to optimize maintenance, reduce costs, and improve performance for clients in industries like wind energy, manufacturing, and aviation. The presentation outlines how Sentient Science's prognostics approach can generate savings and efficiencies for asset-intensive companies participating in the emerging industrial internet.
This document summarizes Louis Redding's doctoral research on Integrated Vehicle Health Management (IVHM). The research aims to map high-value IVHM applications in the UK and create a decision tool to evaluate benefits of new applications. Key objectives are to understand awareness and adoption of IVHM in UK manufacturing, identify capabilities needed for IVHM development, and examine examples of success and failure. The research framework involves a literature review identifying gaps, a survey to understand industry awareness, and case studies. The goal is to construct a validated simulation model or decision framework to inform UK manufacturers on IVHM evaluation and adoption.
Hospital Facility Maintainance - Why & HowMatt Hicks
The University of Michigan Hospitals and Health Centers has a policy to provide an environment of care that is safe, functional, supportive and effective in accomplishing their mission. The Environment of Care Committee is responsible for managing the environment of care according to existing plans and policies and reporting to institutional leadership. The environment of care considers elements that contribute to how space feels and works for patients, families, visitors, and staff, including factors beyond physical comfort and safety like supporting patient needs and dignity.
The document discusses next steps for improving plant hospital maintenance at a hospital through better planning and scheduling. It recommends analyzing maintenance history, establishing key performance indicators, moving to reliability-centered maintenance through improved planning and scheduling, and process mapping. Appropriate staffing levels for planners and supervisors are discussed based on trade skills. The vision and mission focus on maintenance excellence and customer service. Anticipated outcomes include improved patient environment and satisfaction through more efficient, integrated maintenance management.
The document discusses the IMS Center, which aims to transform traditional "fail and fix" maintenance practices to "predict and prevent" through technologies like embedded monitoring, prognostics, and decision support tools. The Center develops the Watchdog Agent toolbox containing analytical tools to assess machine performance and predict failures using sensor data. It also provides decision support tools to prioritize maintenance work orders. The Center works with industry and academic partners on projects applying these methods to manufacturing, energy, vehicles and other areas.
PHM - Risk Minimisation [Airforce Institute Presentation]zoomdust
The document discusses various issues related to risk minimization and prognostics and health management (PHM) technologies. It covers key drivers of risk like safety, reliability and cost. It also discusses integrated logistics support (ILS), performance-based logistics (PBL) and reliability-availability-maintainability (RAM) approaches. Barriers to effective condition-based maintenance include incomplete failure analysis and a lack of standardized failure taxonomy. Model-based failure analysis can help enhance prognostics and health management systems.
The document describes a Prognostic and Health Management (PHM) system for railway bridges that uses 3S technology, Bridge Information Modeling (BIM), and integrated monitoring of trains, tracks, and bridges. The goals are to improve reliability, availability, maintainability, and safety. Key aspects include:
1) Real-time monitoring of tracks, bridges and environments and periodic inspections to collect multi-source data.
2) BIM is used to manage bridge design, construction, and inspection/repair data and associate it with 3D bridge models.
3) Intelligent inspections electronically capture and upload defects using BIM for location/orientation.
4) Diagnostics analyze monitoring data histories
The document discusses several issues related to implementing condition-based maintenance (CBM) and prognostics and health management (PHM) programs, including:
1) Performing a thorough risk assessment using techniques like FMECA is important to understand how a system can fail and inform sensor placement and diagnostic rule development.
2) Model-based failure analysis considering failure dependencies is better than spreadsheet-based FMECA for knowledge retention and risk assessment.
3) Clear definitions of failure concepts and taxonomies are needed to improve understanding of risk assessments.
4) Diagnostic rules and sensor selection should be based on dependencies between failure modes revealed through risk assessments.
H2O Machine Learning and Kalman Filters for Machine Prognostics - Galvanize SFSri Ambati
Hank Roark's presentation at Galvanize SF, 02.23.16
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Business opportunities in private hospital sector in indiaBusiness Finland
The document summarizes business opportunities in India's private hospital sector. It notes that the healthcare industry in India is growing rapidly at 20% annually and is expected to reach $100 billion by 2015. The private sector accounts for 65% of hospital beds and 80% of healthcare spending, and is growing at 24.1% annually. Opportunities exist for foreign companies in areas like medical equipment, diagnostic devices, health IT, and green building technologies. The largest private hospital chains in India are outlined. Fast growing specialty areas include oncology, orthopedics, and cardiology.
Accelerated life testing (ALT) is widely used to expedite failures of a product in a short time period for predicting the product’s reliability under normal operating conditions. The resulting ALT data are often characterized by a probability distribution, such as Weibull, Lognormal, Gamma distribution, along with a life-stress relationship. However, if the selected failure time distribution is not adequate in describing the ALT data, the resulting reliability prediction would be misleading. In this talk, we provide a generic method for modeling ALT data which will assist engineers in dealing with a variety of failure time distributions. The method uses Erlang-Coxian (EC) distributions, which belong to a particular subset of phase-type (PH) distributions, to approximate the underlying failure time distributions arbitrarily closely. To estimate the parameters of such an EC-based ALT model, two statistical inference approaches are proposed. First, a mathematical programming approach is formulated to simultaneously match the moments of the EC-based ALT model to the ALT data collected at all test stress levels. This approach resolves the feasibility issue of the method of moments. In addition, the maximum likelihood estimation (MLE) approach is proposed to handle ALT data with type-I censoring. Numerical examples are provided to illustrate the capability of the generic method in modeling ALT data.
Smart machines -presentation, January 2015Immo Salo
Smart machines are becoming more prevalent and capable due to advancements in technologies like cloud computing, big data, and robotics. They are able to make autonomous decisions, solve complex problems, and understand contexts. Examples mentioned include self-driving cars, industrial robots, personal assistants like IBM Watson, and consumer robots. While smart machines will disrupt many industries and eliminate some jobs, they will also create new types of work and greatly accelerate machine learning through cloud-sharing of data and models. Their development relies on combinations of existing technologies working together in new ways.
Aviation MRO IT: Emergence of SaaS and Convergence of BPOguesta9496c4
White paper presentation on the current state, trends and future scenarios for aviation maintenance technologies, taking into account disruptive technological trends in Autonomics, Diagnostics, Prognostics, virtualization, SaaS, Web 2.0 collaboration.
The automobile industry in India has grown significantly over the past few decades. It began with only two major players and licensed manufacturing in the initial years. In the 1980s, more companies entered the market and government support increased. In the 1990s, delicensing led to more players and easier financing, creating a buyers market. Domestic sales and exports have grown strongly in recent years, with India now among the top global markets. Motorcycles dominate two-wheeler sales while passenger vehicles lead exports. Key players hold major shares in different vehicle segments. The industry faces challenges around infrastructure and customer expectations but benefits from favorable demographics and policy support focused on areas like product development, manufacturing, and supply chains.
The quad flat pack no lead or quad flat non-leaded (QFN) is one of the fastest growing package types in the electronics industry today. While the advantages of QFNs are well documented, concerns arise with its reliability and manufacturability. Acceptance of this package, especially in long-life, severe-environment, high-reliability applications, is currently limited. One of the most common drivers for reliability failures is inappropriate adoption of new technologies, such as the case with QFN. In this presentation, we will review and discuss QFN related reliability concerns and challenges, and propose Physics-of-Failure (PoF) based approaches to allow the confident introduction of QFN components into electronics products.
Jobs Opportunities and Skills Required in E&E SectorNorAzmi Alias
The document summarizes an upcoming seminar on skills required in the electrical and electronic industry in Malaysia. The seminar will cover an overview of Malaysia's E&E sector and key focus clusters, emerging technologies that require new knowledge, the types of jobs available in the sector from 2012-2020, and what skills are needed to perform effectively in teams in the workplace. The seminar aims to provide guidance to students on the right career paths and jobs matching their fields of study.
An engineering perspective on biometric sensor integration in wearablesValencell, Inc
This document discusses challenges in integrating biometric sensors into wearable devices from engineering perspectives. It addresses questions product managers, mechanical engineers, and software engineers may have around sensor placement, form factor considerations, electrical design, software integration, testing, and validation. The document provides recommendations on sensor size and positioning, attachment methods, interface choices, power supplies, metrics, algorithms, and production testing protocols to optimize sensor performance for different use cases.
Enabling Technologies is an engineering consultancy that provides structural design, analysis, testing, and certification services across multiple industries including aerospace, marine, renewable energy, and more. It has expertise in advanced composites and metallic structures. Services include product development, testing, failure analysis, and product support. The company has significant investment in analysis tools and works with clients to develop applications for new technologies.
Digital Processes with PowerPath Barcodes, Scanning and Digital ImagingChris Godin✪
• Presented to approximately 40 healthcare professionals at the Sunquest Info Systems National User Group Meeting 2012. – “Digital Processes with PowerPath Barcodes Scanning and Digital Imaging”
Complying with safety security certification standards and requirements in any market is an expensive, tedious, and time-consuming task, but it saves lives in the friendly skies and can be a force multiplier on the battlefield. Meeting those requirements is often more efficient through open architecture designs and the use of common standards much the way the Future Airborne Capability Environment (FACE) consortium is doing in the military avionics realm. New certification benchmarks such as DO-178C are also enhancing the safety compliance process. This e-cast of industry experts will discuss how designers can manage today's aerospace and defense software safety and certification requirement demands through improved modeling tools, common computing platforms, code analysis tools, and more.
Watch webinar: http://ecast.opensystemsmedia.com/384
The DLSU Physics Department presents the different programs offered by the department - BS Pre-Med Physics, BS Physics with specialization in Medical Instrumentation, BS Physics with specialization in Material Science, BS Physics minor in Economics or Finance
Discover a completely integrated surgical imaging workflow through the use of Stryker SDC surgical cameras and DataMediatOR with Cerner Millennium. Using these tools, surgeons have an automated and secure way to capture images during procedures, and then share the images with patients and other providers. During this panel, we will discuss the value and simplicity of a surgical imaging project and provide detailed information specific to the implementation and integration of these two systems and its hardware.
Apek Mulay has over 15 years of experience in business analytics, failure analysis, and technology consulting. He holds an M.S. in Business Analytics from UT Dallas and M.S. degrees in Electrical Engineering and Electronics Engineering. He has worked as a failure analyst and business consultant for various companies and published over 150 thought leadership articles and 4 books. He has extensive experience with failure analysis tools and semiconductor processing equipment.
Intertek provides the fastest PV module testing and certification in the industry, allowing manufacturers to get products to market faster. Their safety and performance testing can start within two weeks and provide results within 4 months. Intertek also conducts testing to international standards to help manufacturers gain market share and verify product claims.
Tablets and Smartphones are the key accelerators to mobilize your business. This presentation shows, how you can mobilize your business towards your employees, partners and customers.
Daegu-Gyeongbuk is a place where industry thrives. The birthplace of Samsung, LG Electronics and POSCO, as well as, leading multinationals such as Exxon Mobil, Dassault Systemes and Acciona, all have made sizable investments in Daegu-Gyeongbuk.
- The company has over 300 major industrial customers, including working with 32 of the CAC 40 companies, with 45% of turnover from its top 10 customers.
- It offers product and process engineering services across the entire product lifecycle, including R&D, project management, engineering, manufacturing, and customer service.
- The company has 24 years of experience in engineering services and experience with global projects in industries like automotive, shipbuilding, aerospace, and power.
- The company has over 300 major industrial customers, including working with 32 of the CAC 40 companies, with 45% of turnover from its top 10 customers.
- It offers product and process engineering services across the entire product lifecycle, including R&D, project management, engineering, manufacturing, and customer service.
- The company has 24 years of experience in engineering services and experience with global projects in industries like automotive, shipbuilding, aerospace, and power.
Company presentation from Christian Michelsen Research (CMR) on their Prototech organization:
[1] Prototech is a technology company established in 1988 that is 100% owned by CMR. It has 35 employees across four departments: Technology, Solutions, Parts, and Services.
[2] The Technology department has 20 years of experience developing fuel cell and electrolyser systems, with expertise in fuel cells, energy conversion, and energy optimization.
[3] Prototech serves customers in space, green energy, oil and gas, and other industries, applying its expertise in areas like advanced design, analysis, machining, testing, repair and integration.
MicroLOGIX is an electronics company established in 1997 that offers manufacturing, design, and testing services. It has over 120 employees and facilities across 20,000 square feet. MicroLOGIX works with industries like defense, automotive, industrial, and medical to provide services like electronics and mechanical design, prototype manufacturing, and product validation.
Intertek offers testing services for inverters and other renewable energy equipment at their state-of-the-art laboratory or customer sites. Their services include testing inverters, charge controllers, and related components to standards like UL 1741. Intertek also provides certification services and has accreditations from various organizations. They support manufacturers in gaining market access and compliance with standards in places like Europe, Australia, and North America.
Health Care Capability 2010 Final Smallhamadagkhater
Meinhardt is a multi-disciplinary design and engineering firm providing services for architecture, engineering, and healthcare projects. They have significant experience in the hospital and healthcare sector, offering a full range of engineering services including mechanical, electrical, fire protection, and more. Some of their past healthcare projects include the Siloam Specialist Hospital cancer hospital in Jakarta and the Defence Medical Research Institute in Singapore.
Frank P. Modica is a multi-disciplinary engineer with experience in new product development, line management, and project management. He has worked in various roles developing medical diagnostic devices and has experience in mechanical engineering, opto-mechanical engineering, and systems engineering. Modica holds patents and design awards related to medical diagnostic instruments and received his BSME from MIT.
This document discusses duty cycle concepts in reliability engineering. It begins with definitions of time-based and stress-condition-based duty cycles. Time-based duty cycle is the proportion of time a system is active, while stress-condition-based duty cycle considers the level of stress applied. The document then discusses how duty cycle manifests differently across various industries and how it is used to calculate reliability, with duty cycle affecting mission time, failure mechanisms, and characteristic life. Examples are provided for hard disk drives to illustrate the effects of duty cycle on acceleration factors and mean time to failure.
The document discusses potential issues with using MTBF/MTTF as the primary reliability metric for the defense and aerospace industries. It argues that MTBF/MTTF provides an incomplete view of reliability across the entire product lifecycle and can result in overly optimistic assessments. The document proposes using an alternative metric called Bx/Lx, which specifies the life point where no more than a certain percentage (like 10%) of failures have occurred. This provides a more comprehensive view of reliability focused on early failures. Overall, the document advocates updating reliability metrics and practices to better reflect physical failure mechanisms.
This document provides an overview of a talk on thermodynamic reliability given by Dr. Alec Feinberg. The talk covers using thermodynamics and non-equilibrium thermodynamics to assess damage in systems and components. It discusses how the second law of thermodynamics can be applied to describe aging damage. Examples are provided to show calculating entropy damage and aging ratios for simple resistor aging and complex systems. The talk also discusses measuring entropy damage over time and modeling degradation paths. Overall, the document introduces the concept of using thermodynamics to assess reliability and aging in engineered systems.
This document outlines key elements for establishing a sustainable root cause analysis program. It discusses the importance of having an involved sponsor, a clear resourcing plan with defined roles and responsibilities, formal triggers for when analyses should be conducted, protocols for collecting and preserving evidence, standardized reporting, and a system for tracking action items to completion. It also emphasizes tracking the financial value of the program and conducting audits to ensure the program's sustainability over the long term (minimum of 3 years). The overall message is that root cause analysis requires a formal, long-term commitment and cultural change, not just a one-time effort, to truly solve problems and prevent their recurrence.
Dynamic vs. Traditional Probabilistic Risk Assessment Methodologies - by Huai...ASQ Reliability Division
The document compares dynamic and traditional probabilistic risk assessment methodologies. Traditional methodologies like fault trees, event sequence diagrams, and FMECA require analysts to assess possible system failures. Dynamic methodologies like Monte Carlo simulation use executable models to simulate system behavior probabilistically over time and automatically generate event sequences. Dynamic methods can address limitations of traditional approaches that rely heavily on analyst judgment.
This document discusses efficient reliability demonstration tests that can reduce sample sizes and test times compared to conventional methods. It presents principles for test time reduction using degradation measurements during testing. Methods are provided for calculating optimal test plans that minimize costs while meeting reliability requirements and risk constraints. Decision rules are given for terminating tests early based on degradation measurements and risk estimates. An example application demonstrates how the approach can significantly reduce testing costs.
This document discusses using degradation data to model reliability and predict failure times. It begins by explaining how failures can be caused by degradation over time in mechanical components and integrated circuits. Examples of degradation mechanisms like creep, fatigue, and corrosion are provided. The document then discusses using non-destructive and destructive inspection of degradation parameters to build models and predict reliability. Accelerated degradation testing is also covered as a way to quickly generate degradation data under elevated stress conditions. Overall, the document provides an overview of modeling reliability using degradation data and predicting failure times based on degradation paths.
The webinar discusses innovation and the innovation process. It defines innovation as the successful conversion of new concepts and knowledge into new products and processes that deliver new customer value. The innovation process involves 4 steps: 1) finding opportunities, 2) connecting to conceptual solutions, 3) making solutions user-friendly, and 4) getting to market. Different personality types play different roles in innovation, including creators, connectors, developers, and doers. Reliability is also an important consideration in innovation to ensure solutions work well for customers. The webinar encourages participants to get involved in their company's innovation efforts or help establish an innovation process.
Objectives
To provide an introduction to the statistical analysis of
failure time data
To discuss the impact of data censoring on data analysis
To demonstrate software tools for reliability data analysis
Organization
Reliability definition
Characteristics of reliability data
Statistical analysis of censored reliability data
Objectives
To understand Weibull distribution
To be able to use Weibull plot for failure time analysis and
diagnosis
To be able to use software to do data analysis
Organization
Distribution model
Parameter estimation
Regression analysis
This document summarizes an ASQ webinar on reliably solving intractable problems. It outlines 8 principles for producing breakthroughs: 1) use divergent problem solving, 2) generate paradigm shifts, 3) agree on success criteria, 4) start with a strong commitment, 5) separate creative and analytical thinking, 6) involve stakeholders, 7) use consensus decision making, and 8) anticipate issues. It then describes a 13-step conversation process to resolve obstacles following these principles in 4 phases: establishing foundations, envisioning the future, establishing solutions, and ensuring support. The document provides tips for facilitating each step of the process.
With the increase in global competition, more and more costumers consider reliability as one of their primary deciding factors, when purchasing new products. Several companies have invested in developing their own Design for Reliability (DFR) processes and roadmaps in order to be able to meet those requirements and compete in today’s market. This presentation will describe the DFR roadmap and how to effectively use it to ensure the success of the reliability program by focusing on the following DFR elements.
Improved QFN Reliability Process by John Ganjei. John will talk about the improvements in the reliability process in this webinar.
It is free to attend - see www.reliabilitycalendar.org/webinars/ to register for upcoming events.
Data Acquisition: A Key Challenge for Quality and Reliability ImprovementASQ Reliability Division
The document discusses challenges with data acquisition for quality and reliability analysis. It presents a 5-step process called DEUPM for targeted data acquisition: 1) Define the problem, 2) Evaluate existing data, 3) Understand data acquisition opportunities and limitations, 4) Plan data acquisition and analysis, 5) Monitor, clean data, analyze and validate. An example of using this process to validate the reliability of a new washing machine design within 6 months is provided to illustrate the steps. The process aims to ensure data acquisition is disciplined and sufficient to answer reliability questions.
The document discusses applying Failure Mode and Effects Criticality Analysis (FMECA) to software engineering. It describes FMECA as a structured method to anticipate failures and their causes. The document outlines how FMECA was originally used in industries like aerospace and nuclear engineering but has expanded to other domains. It then discusses applying FMECA at different levels of a software project, from requirements to architecture to design to code. The document advocates an "enlightened approach" to using FMECA across all representations and abstractions of software.
Astr2013 tutorial by mike silverman of ops a la carte 40 years of halt, wha...ASQ Reliability Division
This document summarizes a presentation titled "40 Years of HALT: What Have We Learned?" by Mike Silverman. The presentation discusses the evolution of Highly Accelerated Life Testing (HALT) over the past 40 years, including what HALT is and is not, basic HALT methodology, links between HALT and design for reliability, new advances in HALT, current adoption rates of HALT, and the future of HALT. The presentation aims to share lessons learned from thousands of engineers who have used HALT techniques over the past 40 years to improve product design and reliability.
Comparing Individual Reliability to Population Reliability for Aging SystemsASQ Reliability Division
This document discusses the differences between individual reliability (IndRel) and population reliability (PopRel) for aging systems. IndRel provides the reliability of a single system at a given age, while PopRel provides the probability that a randomly selected system from a population will work at a given time, taking into account the age distribution of systems in the population. The document outlines methods to estimate both IndRel and PopRel, including using Weibull and probit models on failure data. Examples are provided to demonstrate estimating IndRel and PopRel for projects using different statistical models and failure data.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
2. ASQ Reliability Division
ASQ Reliability Division
Chinese Webinar Series
Chinese Webinar Series
One of the monthly webinars
One of the monthly webinars
on topics of interest to
reliability engineers.
To view recorded webinar (available to ASQ Reliability
Division members only) visit asq.org/reliability
) /
To sign up for the free and available to anyone live
webinars visit reliabilitycalendar.org and select English
Webinars to find links to register for upcoming events
http://reliabilitycalendar.org/The_Re
liability_Calendar/Webinars_
liability Calendar/Webinars ‐
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