The document discusses predictive maintenance in oil refineries using analytics. It notes that most refinery shutdowns are unplanned and due to mechanical failures. Traditional maintenance methods are reactive or preventive. Predictive analytics uses real-time equipment data and historical maintenance records to monitor equipment health and estimate remaining lifespan. This allows refineries to schedule maintenance more efficiently to avoid breakdowns and reduce downtime. The document provides examples of predictive algorithms and dashboards that can integrate data for predictive maintenance to optimize operations and supply chain processes. It estimates that a typical 100,000 bpd refinery could save over $3.5 million annually through predictive maintenance.
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.
This presentation introduces the concept of Machine Learning and then discusses how Machine Learning is being used in the Predictive Maintenance domain.
A study of Machine Learning approach for Predictive Maintenance in Industry 4.0Mohsen Sadok
I am delighted to share with you my graduation project presentation submitted for the award of Bachelor degree in #electromechanical_engineering.
#subject : A study of machine Learning approach for predictive maintenance in industry 4.0
>> the aim of the project is to Build and Develop Machine Learning models to predict Time-To-Failure (TTF) or Remaining Useful Life (RUL) of in-service equipment in order to pre-emptively trigger a maintenance visit to avoid adverse machine performance and minimizing the number and cost of unscheduled machine failures.
Technologies used: #Python, #TensorFlow, #Keras, #Sklearn, #RNN_LSTM, #XGboost, #LightGBM, #CATboost, #KNN, #SVM, #GaussianNB
>>> The Global Predictive Maintenance Market size is expected to reach $12.7 billion by 2025, rising at a market growth of 28.4% CAGR during the forecast period
#July_2019
#machine_learning
#deep_learning
#predictive_maintenance
#industry_4.0
(confidential details not presented)
LinkedIN : https://www.linkedin.com/posts/mohsen-sadok-254b0a110_a-study-of-machine-learning-approach-for-activity-6550815214206627840-Pq3G
Predictive Maintenance vs Preventive MaintenanceMobility Work
Whether your business is small, medium or large, effective equipment maintenance is crucial. The CMMS market is flooded with solutions promising cost and time savings but also demanding a solid investment. Every maintenance professional will tell you that predictive and preventive maintenance are absolutely worth it and of highest importance for asset management.
But how to choose the right maintenance strategy for your equipment according to your budget?
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.
This presentation introduces the concept of Machine Learning and then discusses how Machine Learning is being used in the Predictive Maintenance domain.
A study of Machine Learning approach for Predictive Maintenance in Industry 4.0Mohsen Sadok
I am delighted to share with you my graduation project presentation submitted for the award of Bachelor degree in #electromechanical_engineering.
#subject : A study of machine Learning approach for predictive maintenance in industry 4.0
>> the aim of the project is to Build and Develop Machine Learning models to predict Time-To-Failure (TTF) or Remaining Useful Life (RUL) of in-service equipment in order to pre-emptively trigger a maintenance visit to avoid adverse machine performance and minimizing the number and cost of unscheduled machine failures.
Technologies used: #Python, #TensorFlow, #Keras, #Sklearn, #RNN_LSTM, #XGboost, #LightGBM, #CATboost, #KNN, #SVM, #GaussianNB
>>> The Global Predictive Maintenance Market size is expected to reach $12.7 billion by 2025, rising at a market growth of 28.4% CAGR during the forecast period
#July_2019
#machine_learning
#deep_learning
#predictive_maintenance
#industry_4.0
(confidential details not presented)
LinkedIN : https://www.linkedin.com/posts/mohsen-sadok-254b0a110_a-study-of-machine-learning-approach-for-activity-6550815214206627840-Pq3G
Predictive Maintenance vs Preventive MaintenanceMobility Work
Whether your business is small, medium or large, effective equipment maintenance is crucial. The CMMS market is flooded with solutions promising cost and time savings but also demanding a solid investment. Every maintenance professional will tell you that predictive and preventive maintenance are absolutely worth it and of highest importance for asset management.
But how to choose the right maintenance strategy for your equipment according to your budget?
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.
One of the major challenges for Gas Turbine users is to ensure high level of engine availability and reliability, and efficient operation during their complete life-cycle. For this purpose, Various maintenance approaches have been introduced over the years for the gas turbine maintenance: Breakdown Maintenance or Run to Failure, Preventive Maintenance or Scheduled Maintenance and Condition-Based Maintenance (CBM). Here the focus is on CBM or predictive maintenance.
Ecolibrium Energy provides predictive maintenance system. Predictive maintenance technologies and sensors help in smoother functionality. Visit us for more info on predictive maintenance software.
The evolution of machine learning and IoT have made it possible for manufacturers to build more effective applications for predictive maintenance than ever before. Despite the huge potential that machine learning offers for predictive maintenance, it's challenging to build solutions that can handle the speed of IoT data streams and the massively large datasets required to train models that can forecast rare events like mechanical failures. Solving these challenges requires knowledge about state-of-the-art dataware, such as MapR, and cluster computing frameworks, such as Spark, which give developers foundational APIs for consuming and transforming data into feature tables useful for machine learning.
Faststream’s Predictive maintenance platform uses condition-monitoring equipment to evaluate an asset’s performance in real-time in the manufacturing Industries. Using our Predictive maintenance for industry 4.0 we were able to prevent asset failure of various Factories and Manufacturing Companies. By analyzing production data to identify patterns and predict issues before they happen.
Using our IoT based Predictive Maintenance Solutions the Factory managers and machine operators can carry out scheduled maintenance and regularly repaired machine parts to prevent downtime. Our IoT allows for different assets and systems to connect, work together, share, analyze, and make actionable decisions on the data.
Faststream's Predictive Maintenance on Factory Maintenance helps maintenance managers and manufacturers to predict the likelihood of future failures and determine asset failure factors that could impact elevator operations. By incorporating Faststream Technologies IoT gateway, sensor node, and Microsoft Azure/AWS/Thinkspace Cloud platform, our solution can entitle with data statistics, state monitoring, fault alarming, remote management, maintenance supervision, emergency response advertisement placement, and other features.
It saves more then it costs....
Preventive maintenance and reactive maintenance are an extremely critical part of any fleet operations. By creating a Preventive Maintenance program to decrease the incidents of equipment arriving late for the PM’s they are due for, this program can be an integral part of cost savings and reduction of equipment downtime for repairs.
Reliability Centered Maintenance (RCM) and Total Productive Maintenance (TPM)...Flevy.com Best Practices
More Information:
https://flevy.com/browse/business-document/reliability-centered-maintenance-rcm-and-total-productive-maintenance-tpm--2-day-presentation-1081
BENEFITS OF DOCUMENT
Improve reliability of plant & equipment
Measure the machine performance losses and understand better
Introduce autonomous maintenance
DOCUMENT DESCRIPTION
Reliability Centered Maintenance and Total Productive Maintenance presentation is intended to help as a 2-day workshop material for Operations and Maintenance personnel.
This presentation consists of over 200 slides and comprises of the following:
Group Activity - Define Maintenance Excellence
Maintenance Excellence - Activity
What is RCM?
Objective & goal of RCM
Techniques employed by RCM
Primary RCM Principles
Types of Maintenance Tasks
RCM Considerations, Applicability + Benefits
Steps in RCM Implementation
TPM vision, definition, origins, principles
8 Pillars of TPM
TPM Self-Assessment
Autonomous maintenance
Equipment & Process Improvement
Equipment Losses, Manpower & Material Losses
OEE - what it is & Calculations
Activity OEE Calculation
Other pillars of TPM
TPM Implementation - 12 steps
Benefits & OEE Tracker
Proactive Maintenance Analysis
Liaison with Ops, Communicating OEE,
Group Activity - OEE Communication/Importance
Ops. Skills, Cleanliness,
Monitoring - Gauges, Lubrication, Contamination, Vibration, One point Lesson
Activity - Maintenance / Operations
Analysis of Maintenance History, MTBF and its calculation
Activity - MTBF Calculation
Improving Equipment performance
FMEA, Types, Calculating RPN
John Day developed a proactive maintenance process in 1978 and manage maintenance and engineering at Alumax Mt. Holly and later at Alcoa Mt Holly for over 20 years. These are the slides he presented at the 1997 SMRP Conference. Great slides with great information. If you would like the slides and not PDF send me an email at rsmith@maintenancebestpractices.com. I worked for John Day back in the early 1980s which started my journey in Proactive Maintenance.
International competition, shorter product life cycles and faster technological leaps forward – these are only a few of the challenges the production of a company is facing in the 21st century. In order to survive in an environment like this, resource-efficient and secure planning of production processes are necessary to guarantee a consistent and high quality output. Unforeseeable machine failures as well as performance drops or deterioration in quality because of defective system components can lead to shortness of supplies which will eventually weaken the market position of the entire organization.
To meet these requirements organizations are increasingly focusing on the improvement of maintenance, repair and operations of their machinery. In the previous years, the industry shifted their focus away from only reactive repair mechanisms towards the predictive coordination of machine maintenance.
Predictive Maintenance falls under the category of the future of maintenance developments. Originally developed in the course of the “Industrie 4.0” high-tech strategy of the German government, today Predictive Maintenance represents the informatization of production processes - intelligent IT-based production systems on the path towards a Smart Factory. Through the generation and analysis of different machine data, the predictive power of the state of industrial plants is not only enhanced, but also provides the basis for an improved planning certainty as well as the efficient planning of repair and maintenance work.
[Tutorial] building machine learning models for predictive maintenance applic...PAPIs.io
This talk introduces the landscape and challenges of predictive maintenance applications in the industry, illustrates how to formulate (data labeling and feature engineering) the problem with three machine learning models (regression, binary classification, multi-class classification) using a publicly available aircraft engine run-to-failure data set, and showcases how the models can be conveniently trained and compared with different algorithms in Azure ML.
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.
One of the major challenges for Gas Turbine users is to ensure high level of engine availability and reliability, and efficient operation during their complete life-cycle. For this purpose, Various maintenance approaches have been introduced over the years for the gas turbine maintenance: Breakdown Maintenance or Run to Failure, Preventive Maintenance or Scheduled Maintenance and Condition-Based Maintenance (CBM). Here the focus is on CBM or predictive maintenance.
Ecolibrium Energy provides predictive maintenance system. Predictive maintenance technologies and sensors help in smoother functionality. Visit us for more info on predictive maintenance software.
The evolution of machine learning and IoT have made it possible for manufacturers to build more effective applications for predictive maintenance than ever before. Despite the huge potential that machine learning offers for predictive maintenance, it's challenging to build solutions that can handle the speed of IoT data streams and the massively large datasets required to train models that can forecast rare events like mechanical failures. Solving these challenges requires knowledge about state-of-the-art dataware, such as MapR, and cluster computing frameworks, such as Spark, which give developers foundational APIs for consuming and transforming data into feature tables useful for machine learning.
Faststream’s Predictive maintenance platform uses condition-monitoring equipment to evaluate an asset’s performance in real-time in the manufacturing Industries. Using our Predictive maintenance for industry 4.0 we were able to prevent asset failure of various Factories and Manufacturing Companies. By analyzing production data to identify patterns and predict issues before they happen.
Using our IoT based Predictive Maintenance Solutions the Factory managers and machine operators can carry out scheduled maintenance and regularly repaired machine parts to prevent downtime. Our IoT allows for different assets and systems to connect, work together, share, analyze, and make actionable decisions on the data.
Faststream's Predictive Maintenance on Factory Maintenance helps maintenance managers and manufacturers to predict the likelihood of future failures and determine asset failure factors that could impact elevator operations. By incorporating Faststream Technologies IoT gateway, sensor node, and Microsoft Azure/AWS/Thinkspace Cloud platform, our solution can entitle with data statistics, state monitoring, fault alarming, remote management, maintenance supervision, emergency response advertisement placement, and other features.
It saves more then it costs....
Preventive maintenance and reactive maintenance are an extremely critical part of any fleet operations. By creating a Preventive Maintenance program to decrease the incidents of equipment arriving late for the PM’s they are due for, this program can be an integral part of cost savings and reduction of equipment downtime for repairs.
Reliability Centered Maintenance (RCM) and Total Productive Maintenance (TPM)...Flevy.com Best Practices
More Information:
https://flevy.com/browse/business-document/reliability-centered-maintenance-rcm-and-total-productive-maintenance-tpm--2-day-presentation-1081
BENEFITS OF DOCUMENT
Improve reliability of plant & equipment
Measure the machine performance losses and understand better
Introduce autonomous maintenance
DOCUMENT DESCRIPTION
Reliability Centered Maintenance and Total Productive Maintenance presentation is intended to help as a 2-day workshop material for Operations and Maintenance personnel.
This presentation consists of over 200 slides and comprises of the following:
Group Activity - Define Maintenance Excellence
Maintenance Excellence - Activity
What is RCM?
Objective & goal of RCM
Techniques employed by RCM
Primary RCM Principles
Types of Maintenance Tasks
RCM Considerations, Applicability + Benefits
Steps in RCM Implementation
TPM vision, definition, origins, principles
8 Pillars of TPM
TPM Self-Assessment
Autonomous maintenance
Equipment & Process Improvement
Equipment Losses, Manpower & Material Losses
OEE - what it is & Calculations
Activity OEE Calculation
Other pillars of TPM
TPM Implementation - 12 steps
Benefits & OEE Tracker
Proactive Maintenance Analysis
Liaison with Ops, Communicating OEE,
Group Activity - OEE Communication/Importance
Ops. Skills, Cleanliness,
Monitoring - Gauges, Lubrication, Contamination, Vibration, One point Lesson
Activity - Maintenance / Operations
Analysis of Maintenance History, MTBF and its calculation
Activity - MTBF Calculation
Improving Equipment performance
FMEA, Types, Calculating RPN
John Day developed a proactive maintenance process in 1978 and manage maintenance and engineering at Alumax Mt. Holly and later at Alcoa Mt Holly for over 20 years. These are the slides he presented at the 1997 SMRP Conference. Great slides with great information. If you would like the slides and not PDF send me an email at rsmith@maintenancebestpractices.com. I worked for John Day back in the early 1980s which started my journey in Proactive Maintenance.
International competition, shorter product life cycles and faster technological leaps forward – these are only a few of the challenges the production of a company is facing in the 21st century. In order to survive in an environment like this, resource-efficient and secure planning of production processes are necessary to guarantee a consistent and high quality output. Unforeseeable machine failures as well as performance drops or deterioration in quality because of defective system components can lead to shortness of supplies which will eventually weaken the market position of the entire organization.
To meet these requirements organizations are increasingly focusing on the improvement of maintenance, repair and operations of their machinery. In the previous years, the industry shifted their focus away from only reactive repair mechanisms towards the predictive coordination of machine maintenance.
Predictive Maintenance falls under the category of the future of maintenance developments. Originally developed in the course of the “Industrie 4.0” high-tech strategy of the German government, today Predictive Maintenance represents the informatization of production processes - intelligent IT-based production systems on the path towards a Smart Factory. Through the generation and analysis of different machine data, the predictive power of the state of industrial plants is not only enhanced, but also provides the basis for an improved planning certainty as well as the efficient planning of repair and maintenance work.
[Tutorial] building machine learning models for predictive maintenance applic...PAPIs.io
This talk introduces the landscape and challenges of predictive maintenance applications in the industry, illustrates how to formulate (data labeling and feature engineering) the problem with three machine learning models (regression, binary classification, multi-class classification) using a publicly available aircraft engine run-to-failure data set, and showcases how the models can be conveniently trained and compared with different algorithms in Azure ML.
The Science of Predictive Maintenance: IBM's Predictive Analytics SolutionSenturus
Overview of IBM’s Predictive Maintenance and Quality (PMQ) solution. View the webinar video recording and download this deck: http://www.senturus.com/resources/science-predictive-maintenance/.
We show you the PMQ solution can keep manufacturing processes, infrastructure and field equipment running to maximize use and performance, while minimizing costs.
We show how you can use powerful analytics and data integration to help: Anticipate asset maintenance and product quality problems, Reduce unscheduled asset downtime, Spend less time solving production machinery and field asset problems, Improve asset productivity and process quality, Monitor how assets are performing in real-time and predict what will happen next.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Using the Industrial Internet to Move From Planned Maintenance to Predictive ...Sentient Science
Sentient Science provides Prognostics Health Management using the Industrial Internet and will show practical examples of driving down O&M costs by moving from Planned Preventative Maintenance (PPM) to Predictive Health Maintenance (PHM) for distributed assets. This presentation will outline how the IIC, and the practical benefits of integrating your distributed assets with prognostics, predictive models for life extension.
Why predictive maintenance should be a combined effortWouter Verbeek
Predictive maintenance is an extremely promising maintenance strategy, but implementation often turns out to be way more complicated than expected. A lot of attempts to implement predictive maintenance strand at the same departments as where they were initiated. The key towards successful implementation of predictive maintenance is to combine the knowledge of all departments in making decisions. In this presentation we start by explaining, based on the subject of sensor selection, why involving your entire organization is so important. Afterwards we give advice on how to implement predictive maintenance, give examples based on the Strukton Worksphere case and discuss how to get your entire organization on board.
Incorporating a predictive maintenance strategy has been proven to provide savings. Learn how to use your pressure and temperature gauges as part of your initiatives.
At the end of the year or month what reports would you like to see from your CMMS/EAM? Would you like a few failure reports? Check out this presentation. It may make your day.
Why do people not understand the P-F Curve? At a recent maintenance function, I asked 70 maintenance and reliability professionals how many of them had heard of the P-F Curve and only about 10% stated they had. From that 10%, only 1% felt like they truly understood it. This was shocking to me. I assumed everyone had heard about the P-F Curve and its intent.
The intent of the P-F Curve is to illustrate how equipment fails and how early detection of a failure provides time to plan and schedule the replacement or restoration of a failing part without interruption to production or operations.
Once you understand the P-F or PF Curve you will have a better awareness of how equipment fails.
Maintenance Prédictive, personnalisation des interactions clients, optimisation de la supply chain : quand Spark et Hadoop s'immiscent dans les activités opérationnelles de l'entreprise
Presentation by Dr. Peter Bruce, Statistics.com. Presented on April 27, 2012 at the MRA Spring Research Symposium hosted by the Mid-Atlantic Chapter of the Marketing Research Association.
Ibm ofa ottawa_analytics_in_gov _campbell_robertsondawnrk
Opportunity for Analytics Ottawa event. Presentation by Campbell Robertson, Analytics in Government. Results based outcomes with IBM Predictive Analysis for Cost Avoidance and Beyond.
Witekio presented an introduction to predictive maintenance allowed by software systems embedded into smart connected devices. The session covers definitions, when to plan for it, what tools and technologies to choose (existing, custom, machine learning). From basic to advanced predictive maintenance it gives hints about how to do and what choices have to be made.
Déjeuner Conférence - La maintenance à l'ère du prédictifagileDSS
Depuis plusieurs années déjà, la maintenance d’équipements a souvent été une source de problèmes pour bien des entreprises. D’un côté, si vous ne faites pas assez de maintenance, vous aurez une perte de productivité et d’un autre côté, si vous le faites trop fréquemment, vous aurez une explosion de coûts. Comment trouver un juste milieu permettant de maximiser la longévité des équipements tout en diminuant les coûts d’entretien? La réponse se trouve plus près que vous ne le croyez!
"Maximiser la production tout en diminuant les coûts de maintenance; comment les données d’aujourd’hui permettent de prédire la maintenance de demain?"
C’est pour bien comprendre l’impact de la maintenance prédictive que nous vous invitons à ce déjeuner-conférence le 20 mai prochain. En plus d’échanger avec vos pairs vous aurez l’occasion d’assister à la conférence de Thierry Desjardins sur les différentes applications de la maintenance prédictive et comment vous pourriez l’intégrer au sein de votre entreprise.
Des sujets et thèmes à ne pas manquer!
- Internet of Thing, iBeacon, GPS… Comment les technologies impacteront la maintenance de vos équipements?
- Comment savoir quels appareils ont besoin de maintenance, à quel moment et quel en est la cause?
- Comment évolueront les processus de maintenance? Est-ce que la maintenance just in time est accessible?
- Comment certaines entreprises ont réussi à opérationnaliser efficacement un modèle de maintenance prédictive?
Reliability Centered Maintenance for minimizing integrity failure by Bhavesh Shukla at APAC 2015 Process Safety Management Conference 9th March 2015 Singapore.
Introduction to IBM Watson IoT for Predictive Maintenance and Optimization with Maximo
Speaker: Andrew Condos, IBM
Overview: Optimizing asset performance is crucial to the cost effective and efficient operation of asset-intensive organizations. Scheduled maintenance procedures help to achieve this goal, but the key to unlocking real productivity improvements is in predicting how an asset operates over its entire life-cycle.
This session will introduce you to how companies are using IBM Watson IoT Predictive Maintenance and Optimization with Maximo to identify and manage asset reliability risks that could adversely affect plant or business operations. IBM PMO with Maximo applies machine learning to prescribe actions based on predictive scoring, identifies factors that positively and negatively influence asset health and delivers a detailed comparison of historical factors affecting asset performance.
TOMS built on the award-winning Cuecent RTDSS platform is a high-performance solution which facilitates the monitoring and efficient management of your wind turbines for optimal performance. It provides real-time information which helps in reducing the cost of operations and maintenance.
Reliability-centered Maintenance is a maintenance philosophy that includes a systematic approach to determining how to maintain equipment safely and economically. RCM is an invaluable business solution for companies
In situations where equipment failure is inevitable, the structured RCM process will ensure a maintenance strategy that will minimise or eliminate the consequences.
The central problem addressed by the RCM process is how to determine which scheduled maintenance tasks, if any, should be assigned to equipment, and how frequently
Value of solar remote monitoring and analytics for operational intelligenceMachinePulse
MachinePulse attended India Solar Week 2016 from June 2-3 in New Delhi. Our keynote speaker talked about how remote monitoring and analytics helps in increasing the operational efficiency of a solar plant. The presentation includes information about the company, our products and case studies.
Facility personnel often face the choice of maintaining aging equipment or buying new. Now there is another, more cost-effective, option to increase equipment reliability, efficiency and productivity….modernization. Learn key considerations and advantages of upgrading existing equipment to current technology.
A survey by Schneider Electric in the US revealed that predictive maintenance services can lead to 25% reduction in cost. Learn about industrial IoT framework that enables PdM
Artificial intelligence in Pharma by Malai SankarasubbuSaama
Malai Sankarasubbu, VP of AI Research at Saama Technologies, speaks about Artificial Intelligence in Pharma at the ExL AI Innovation Summit in Philadelphia in 2019.
Building a Next Generation Clinical and Scientific Data Management SolutionSaama
Srini Anandakumar, Senior Director of Clinical Analytics Innovations for Saama Technologies, discussions next-generation data management solutions at the Drug Development Networking Summit on April 11, 2019, in Bridgewater, New Jersey.
Karim Damji, SVP Product Management and Marketing at Saama discusses how to spend less time wrangling your data. Learn about the latest technological advances have enabled a platform-based approach to help solve complex problems in a data source-agnostic manner. Improve data processing, standardization and creation of analytics-ready datasets. Introduce machine learning capabilities to improve predictions of risks. Enhance the user experience through the use of a conversational experience. Create executive summaries embedded with relevant persona-based insights.
Artificial Intelligence in Life Sciences: Friend or Foe? by Luke StewartSaama
Luke Stewart was keynote at the AI and Big Data conference hosted by Innovation Enterprises in December 2018 in New York City. Luke talked about Artificial Intelligence in Life Sciences.
Saama Presents Is your Big Data Solution Ready for StreamingSaama
Amit Gulwadi and Karim Damji presented at Panagora's IoT in Clinical Trials Summit in Boston in November 2018. Using the right analytic solution that can incorporate your unstructured IoT data provides tremendous benefits including faster time to commercialization and better business and patient outcomes.
Destroy Data Siloes at Digital Innovations to Advance Clinical TrialsSaama
Digital Innovations, or Dpharm, organized by The Conference Forum was held in Boston in September 2018. Amit Gulwadi, Senior Vice President, Clinical Innovations, Saama Technologies, will be speaking on the topic Destroy Clinical Data Silos. Data Silos are a major roadblock in the clinical trial development process. Disparate data sources and multiple teams working on them increase complexities and delays. One solution to this can be building a comprehensive platform that will streamline clinical data to derive valuable insights.
CBI Gain Cross-Industry Insights to Uncover Improvements and Optimize Trial P...Saama
Leon Surgeon spoke at CBI's Direct to Patient conference in August 2018 in Philadelphia about how technology can be used in practical applications to improve clinical trial performance.
Saama and Pharmacyclics Present at CTI: Clinical Data Analytics – A Solutions...Saama
Nikhil Gopinath from Saama and Pawan Parihar from Pharmacyclics talk about a framework to implement trial management analytics at Pharmacyclics at the Clinical Trial Innovation conference held in Boston in May 2018.
Natural Language Understanding at AI and Machine Learning in Clinical Trials ...Saama
Karim Damji, SVP of Products and Marketing, and Malaikannan Sankarasubbu, VP of AI Research at Saama Technologies spoke at the AI and Machine Learning in Clinical Trials Summit 2018 on Accelerating Clinical Trials using Natural Language Understanding.
Pharma has a big text problem. Lots of useful information buried in unstructured data formats that is difficult to use. Natural Language Understanding will help to turn what was once unusable data into meaningful insights that can be applied to the clinical trial development continuum. NLU engines also open up the possibility for users to have a more interactive relationship with their vast data stores using speech or chat messaging in a conversational experience.
Bridging Health Care and Clinical Trial Data through TechnologySaama
Karim Damji, SVP of Product and Marketing, presented at the Bridging Clinical Research and Clinical Health Care conference held at the Gaylord in National Harbor on April 4-5, 2018.
SCOPE 2018 - Clinical Data Analytics – A Solutions Approach in the Cloud Saama
Karim Damji, SVP at Saama, and Amit Gulwadi, Executive Director at Celgene, discuss how Celgene leveraged new technologies to improve data quality, evidence generation, and time-to-insights.
Build a Next-Generation Clinical Operational Metrics SolutionSaama
Srinivasan Anandakumar spoke at ExL's Clinical Performance Metrics Summit in Philadelphia on December 5, 2017. His talk includes discussion around how to manage and control both trial effectiveness and submission pathway through clinical metrics collected from operational data and patient data, how to get a 360-degree view of trial operations with a next-generation clinical operations repository, that contains all clinical domains—based on a Big Data platform with multiple varieties of data– sponsor, syndicated, and external data, and how to enable the AI to allow high levels of self-service and to provide predictive analytics on clinical operations data.
Enabling Better Clinical Operations through a Clinical Operations StoreSaama
Srini Anandakumar, Senior Director of Clinical Analytics Innovation for Saama, presented at the Big Data and Analytics in Pharma in Philadelphia, November 1, 2017.
Supporting a Collaborative R&D Organization with a Dynamic Big Data SolutionSaama
Nikhil Gopinath presents regarding big data solutions at the Big Data and Analytics for Healthcare and Life Sciences Summit on October 18, 2017 in San Francisco, CA.
Centralizing Data to Address Imperatives in Clinical DevelopmentSaama
Karim Damji presents at SCDM 2017 Annual Conference in Orlando, Florida in the Unstructured and Structured Big Data Convergence for Bridging Clinical, Regulatory, and Commercialization session.
Abstract:
Are you fully leveraging the data you generate from trials, regulatory submissions and post-approval marketing to maximize business outcomes? With the deluge of structured, unstructured, and syndicated data, the use of varied data for targeted outcomes remains difficult, despite increased industry efforts to address the issue. New technologies are federating the ability to leverage analytic-ready data for innovations in clinical development and drug commercialization. With the application of clinical data-as-a-service and meta-data core, centralized clinical data lakes have the power to improve data quality, evidence generation, and time-to-insights.
Who’s Keeping Score? A Quantitative Approach to Trial FeasibilitySaama
Luke Stewart, Product Manager at Saama, spoke at ExL's Trial Protocol Optimization even on July 18. With most trials failing to meet enrollment timelines, current approaches for feasibility are failing to identify and minimize risk. Sponsors must arm themselves with the right tools to own this analysis throughout the trial lifecycle. We will discuss a quantitative approach that operationalizes feasibility score tracking.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Maintenance – Industry Statistics
46%
Of Refinery Shutdowns
are for mechanical
failures
23%
Of Refinery
Shutdowns are for
maintenance
92%
Of Refinery
Shutdowns are
Unplanned
Source : Refinery power failures: causes, costs and solutions - Patrick J Christensen, William H Graf and Thomas W Yeung, Aug 2013
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The Challenge
One of the main challenges oil refineries
face is
…to maximize asset life span, in the
most economical way,
while not compromising on safety and
reliability
Classic Methods include
• Reactive Maintenance
• Preventive Maintenance
• Condition Based Maintenance
SOURCE: Scanderbeg SauerEnhanced Predictive Maintenance - Pierre Marchand, Oct 31, 2014
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Urgentand emergencyinterruptions to
operations due to equipment
breakdowns
Revenue Loss due to
Downtime
Inefficient Operations and
Supply Chain process
Inefficient Asset utilization
Resource expense for Root
cause analysis
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Excess Spare parts
Inventory
Unnecessary resource
Utilization
Opportunity Loss cost of
unused maintenance
records
High Costand lower efficiency of
Preventive(Unnecessary) maintenance
SOURCE: Parker Hannifin
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Predictive Analytics captures real time equipment data and
evaluates historical data to estimate equipment life cycle
for continuous
Equipment Health Monitoring
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Predictive Analytics System Advantages
An advanced analytics
foundation to optimize
operations planning
Ability to scour past
data, identify patterns &
model streaming data
Opportunity to analyze
real time monitoring data
Mine Recurring issues,
failure indicators &
resolutions
A Robust, scalable
solution which can
integrate with other
enterprise systems
i
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Application Opportunities
• What equipment to pull in for
maintenance & when
• What resources to source & allocate
for maintenance
• Birds eye view of real time equipment
health
• Measure wear and tear of equipment
in its lifetime
• Use Historical data to Identify Leading
failure indicators
• Root cause analysis of incident
Day to day maintenance
• What spare parts to keep
• Product inventory maintenance based
on upcoming maintenance
Inventory Management
Equipment Health Monitoring
Root Cause Analysis
Operations & Supply Chain
• Efficient supply chain management
using predicted maintenance time
• Efficient resource allocation
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Return on Investment
Using Predictive Maintenance as part of asset management program , a
typical 100,000-bpd refinery can have an estimated annual benefits of over
USD$3.5M per year.:
• Avoiding abnormal incidents…$500,000
• Reducing lost profit opportunities…$1,750,000
• Reducing maintenance budget…$800,000
• Improving staff productivity…$300,000
• Reducing liability insurance premiums…$200,000
Source : “Quantifying the ROI of an asset performance management program”. Hydrocarbon Processing. T Ayral and M Moran, Meridium, Inc. May 2007.
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Predictive Algorithms for Maintenance
A predictive function based on Current
& Historical data used to derive the
measures
Predictive
Maintenance Design
Binary Logistic
Regression
Multinomial
Logistic Algorithm
Supervised
Learning Models
Explanatory variables
Usage duration
Temperature
Pressure
Flow Rate
Historical sensory
data
Forecasting Models
Health Score of
Equipment
Triggers Alarm
for maintenance
requirement
Usecase : Real wear measure of Equipment
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Usecase : Major failure Indicators
Find patterns in tracking variables correlating to failure
Historical
Maintenance Data
Decision trees
Regression based
models
Identify Root
Cause
Predict future
malfunctions
Usecase : Uptime Time before failure
Modelling historical data to calculate from streaming data
Lifespan Analysis
Model
Pearson
Correlation
Identify operating
Variables
associated with
Lifespan
Estimate
Equipment’s
remaining lifespan
Explanatory
Variables
Analytical
Model
Deduction or
Identification
Outcome
Historical &
Real - time
Maintenance Data
Predictive Algorithms for Maintenance
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Equipment Status Dashboard
Component FRC21
Unit ID 0010021
Location Holder 08
0
20
40
60
80
100
Equipment Age Real Wear Age
Percentage Age of Equipment
0
20
40
60
80
100
High Temp Pressure Vibration
Failure Indicators Component
Equipment
No. FRK03
Equipment
No FRK05
Equipment
No FRK06
Component 1
Component 2
Component 3
Component 4
Component 5
0
20
40
60
80
Equipment Wear Progress
Hours Under Use 6708
Unit ID 0010021
Installation Date 26-07-2015
No of Components 58
Hours till Failure 1677
Forecasted expiry 15-10-2015
Choose
Component
Component
Usage Statistics
Calculate Real
wear of equipment
Lifetime wear &
Warning Indicators
Single dashboard to report the overall health status for an entire manufacturing unit
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Conclusion
Enhance Predictive Maintenance by assimilating data
1. Real – time Sensor Data
2. Maintenance Data
3. Historical Data of Equipment
Identify characteristics affecting breakdown before it
happens. Enhance failure predictions
Reduce unplanned shutdowns
Predict when Maintenance is required
Ensure Effective and efficient spending on proactive
maintenance
Optimize operating conditions to maximize equipment
lifetime &
Optimize Supply Chain Processes
Supply & Output
Inventory
Refinery Operations
Predictive Analytics
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References
• Enhanced Predictive Maintenance - Pierre Marchand, Oct 31, 2014
• Refinery power failures: causes, costs and solutions - Patrick J Christensen, William H Graf and
Thomas W Yeung, Aug 2013
• Proactively detect failure patterns to improve asset productivity and product quality - Predictive
Maintenance and Quality, IBM
• Quantifying the ROI of an asset performance management program”. Hydrocarbon Processing. T
Ayral and M Moran, Meridium, Inc. May 2007.