العرض التقديمي الذي قدمه الأستاذ / علي العثيم في ملتقى مشروعي في جامعة الملك سعود في شهر مايو للعام ٢٠١١ . العرض تضمن الإشارة إلى لجنة شباب الأعمال ومسابقة مبادر للمشاريع الناشئة .
5 Principles of the Modern Math Classroom. Presentation at PCTM 2016 Summer Conference.
To create confident problem solvers, we must not kill thinking. Learn to use 5 Principles of a modern math classroom to design an environment that promotes perseverance. Small groups will experience specific strategies and create a plan for redesigning their own school cultures.
Reliability Maintenance Engineering Day 3 session 1 Measuring AvailabilityThree day live course focused on reliability engineering for maintenance programs. Introductory material and discussion ranging from basic tools and techniques for data analysis to considerations when building or improving a program.
celebrity news ,also celebrity babble ,is enthusiastically trailed by millions around the world.Obviously most of the people keep interest on entertainment and this is the main reason for which celebrity news has a huge demand to know more about the entertainer. Its a very opportunity for you to have knowing your favorite celebrity.
Demystifying Cognitive Approaches to Predictive Maintenance Part 1Anita Raj
While much has been written about industrial digital transformation, only a few industrial companies have nailed the transformation given the complexity and magnitude. Watch the slides to get industry perspectives based on the key lessons learned about scaling and operationalizing “connected ecosystem” for Industrial IoT. The goal is to accurately paint the picture of where the gaps exist and how companies can take advantage of cognitive approaches for higher machine lifetime and greater productivity.
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.
العرض التقديمي الذي قدمه الأستاذ / علي العثيم في ملتقى مشروعي في جامعة الملك سعود في شهر مايو للعام ٢٠١١ . العرض تضمن الإشارة إلى لجنة شباب الأعمال ومسابقة مبادر للمشاريع الناشئة .
5 Principles of the Modern Math Classroom. Presentation at PCTM 2016 Summer Conference.
To create confident problem solvers, we must not kill thinking. Learn to use 5 Principles of a modern math classroom to design an environment that promotes perseverance. Small groups will experience specific strategies and create a plan for redesigning their own school cultures.
Reliability Maintenance Engineering Day 3 session 1 Measuring AvailabilityThree day live course focused on reliability engineering for maintenance programs. Introductory material and discussion ranging from basic tools and techniques for data analysis to considerations when building or improving a program.
celebrity news ,also celebrity babble ,is enthusiastically trailed by millions around the world.Obviously most of the people keep interest on entertainment and this is the main reason for which celebrity news has a huge demand to know more about the entertainer. Its a very opportunity for you to have knowing your favorite celebrity.
Demystifying Cognitive Approaches to Predictive Maintenance Part 1Anita Raj
While much has been written about industrial digital transformation, only a few industrial companies have nailed the transformation given the complexity and magnitude. Watch the slides to get industry perspectives based on the key lessons learned about scaling and operationalizing “connected ecosystem” for Industrial IoT. The goal is to accurately paint the picture of where the gaps exist and how companies can take advantage of cognitive approaches for higher machine lifetime and greater productivity.
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.
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.
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/.
A complete guide on machinery oil analysis and oil condition monitoring.
Topics covered:
1. Oil sampling procedures
2. Oil analysis process
3. Oil analysis parameters
4. Oil specs and oil selection methodology
5. Case study: Car Engine
6. Case study: Power Turbine
7. Case study: Electric Transformer
[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.
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.
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/.
A complete guide on machinery oil analysis and oil condition monitoring.
Topics covered:
1. Oil sampling procedures
2. Oil analysis process
3. Oil analysis parameters
4. Oil specs and oil selection methodology
5. Case study: Car Engine
6. Case study: Power Turbine
7. Case study: Electric Transformer
[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.