Operations & Maintenance - Revision history

BPeat at 20:52, 28 April 2024

2024-04-28T20:52:38Z

BPeat at 00:54, 20 March 2024

2024-03-20T00:54:46Z

BPeat at 18:44, 18 September 2023

2023-09-18T18:44:30Z

BPeat at 16:04, 3 September 2023

2023-09-03T16:04:19Z

BPeat at 17:19, 10 July 2023

2023-07-10T17:19:02Z

BPeat at 17:18, 10 July 2023

2023-07-10T17:18:27Z

BPeat at 15:31, 9 July 2023

2023-07-09T15:31:55Z

BPeat at 14:13, 9 July 2023

2023-07-09T14:13:09Z

BPeat at 14:07, 9 July 2023

2023-07-09T14:07:31Z

BPeat at 13:50, 9 July 2023

2023-07-09T13:50:21Z

@@ Line 106: / Line 106: @@
 <youtube>9VHUSpqFtyA</youtube>
 <b>Predictive maintenance & AI in manufacturing leads to new risk opportunities
-</b><br>What are the new risks that come with insuring predictive maintenance systems in manufacturing industry? Will AI end the role of engineers? And how can companies save costs when buying a predictive maintenance system? Watch the second in the Swiss Re Institute Spotlights webinar series produced with Swiss Re Corporate Solutions, where these topics were covered by leading experts. Please see below  Deep learning and artificial intelligence for predictive maintenance applications - Olga Fink, SNSF Professor for Intelligent Maintenance Systems, ETH Zurich  Predictive maintenance from a risk engineering perspective - André Kreul, Senior Risk Engineer Property, Swiss Re Corporate Solutions
+</b><br>What are the new risks that come with insuring predictive maintenance systems in manufacturing industry? Will AI end the role of engineers? And how can companies save costs when buying a predictive maintenance system? Watch the second in the Swiss Re Institute Spotlights webinar series produced with Swiss Re Corporate Solutions, where these topics were covered by leading experts. Please see below  Deep learning and artificial intelligence for predictive maintenance applications - Olga Fink, SNSF Professor for Intelligent Maintenance Systems, ETH Zurich  Predictive maintenance from a risk engineering [[perspective]] - André Kreul, Senior Risk Engineer Property, Swiss Re Corporate Solutions
   Moderated by Daniel Andris - Head Risk Engineering Services Casualty, Swiss Re Corporate Solutions
 |}

@@ Line 19: / Line 19: @@
 * [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Operations]] ... [[Forecasting]] ... [[Excel#Excel - Forecasting|with Excel]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]]
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
-* [[Immersive Reality]] ... [[Metaverse]] ... [[Digital Twin]] ... [[Internet of Things (IoT)]] ... [[Transhumanism]]
+* [[Immersive Reality]] ... [[Metaverse]] ... [[Omniverse]] ... [[Transhumanism]] ... [[Religion]]
 * [[Architectures]] for AI ... [[Generative AI Stack]] ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
 * [http://www.techemergence.com/ai-for-predictive-maintenance-applications-in-industry-examining-5-use-cases AI for Predictive Maintenance Applications in Industry – Examining 5 Use Cases | Pamela Bump - Techemergence]

@@ Line 20: / Line 20: @@
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
 * [[Immersive Reality]] ... [[Metaverse]] ... [[Digital Twin]] ... [[Internet of Things (IoT)]] ... [[Transhumanism]]
-* [[Architectures]] for AI ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
+* [[Architectures]] for AI ... [[Generative AI Stack]] ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
 * [http://www.techemergence.com/ai-for-predictive-maintenance-applications-in-industry-examining-5-use-cases AI for Predictive Maintenance Applications in Industry – Examining 5 Use Cases | Pamela Bump - Techemergence]
 * [http://www.techemergence.com/industrial-ai-applications-time-series-sensor-data-improve-processes/ Industrial AI Applications – How Time Series and Sensor Data Improve Processes | Daniel Faggella - Techemergence]

@@ Line 17: / Line 17: @@
 [http://www.google.com/search?q=operation+predictive+maintenance+artificial+intelligence+deep+learning+AI ...Google search]
-* [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Maintenance]] ... [[Forecasting]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]] ... [[Excel#Excel - Forecasting|Excel]]
+* [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Operations]] ... [[Forecasting]] ... [[Excel#Excel - Forecasting|with Excel]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]]
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
 * [[Immersive Reality]] ... [[Metaverse]] ... [[Digital Twin]] ... [[Internet of Things (IoT)]] ... [[Transhumanism]]

@@ Line 17: / Line 17: @@
 [http://www.google.com/search?q=operation+predictive+maintenance+artificial+intelligence+deep+learning+AI ...Google search]
-* [[Prescriptive Analytics|Prescriptive &]] ... [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Maintenance]] ... [[Forecasting]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]] ... [[Excel#Excel - Forecasting|Excel]]
+* [[Prescriptive Analytics|Prescriptive &]] [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Maintenance]] ... [[Forecasting]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]] ... [[Excel#Excel - Forecasting|Excel]]
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
 * [[Immersive Reality]] ... [[Metaverse]] ... [[Digital Twin]] ... [[Internet of Things (IoT)]] ... [[Transhumanism]]

@@ Line 95: / Line 95: @@
 <youtube>IdzAGEPQvXo</youtube>
 <b>Predictive Maintenance and Monitoring using AI and Machine Learning
-</b><br>Learn and grow with more BrightTALK webinars and Talks on Artificial Intelligence right here: http://bit.ly/2WNPRfd  In this webinar, we will showcase how [[Google]] Cloud Platform and its Big Data processing, IOT sensor connectivity and [[TensorFlow]] based state of the art machine learning can be used to predict failures and more importantly extend the life of the production equipment leading to break through innovation in production automation and significantly improving productivity and manufacturing flexibility.  About the speaker:   Salil Amonkar, Global Head of Manufacturing and Ai-ML Practice, Pluto7  Thought Leadership in Digital Business Transformation, Cloud and Saas Solution, Data driven [[Analytics]], Value Chain/Supply Chain and Ai and Machine Learning  Overall 25+ years expertise in innovative business transformation services for Value Chain/Supply Chain and Manufacturing, CPG, High Technology and related Industries. Implemented solutions leveraging multiple technologies, Cloud, SaaS, Hybrid architectures leveraging advanced and predictive [[analytics]] using data sciences, AI and machine learning for Sales and Marketing, Services, Quote to Cash and Supply Chain.  Have led several business transformational initiatives for Large Enterprise customers such as ABinBev(Anheuser Busch) Cisco, General Electric, [[IBM]], Vodafone, General Motors, Tata Motors and many others.
+</b><br>Learn and grow with more BrightTALK webinars and Talks on Artificial Intelligence right here: http://bit.ly/2WNPRfd  In this webinar, we will showcase how [[Google]] Cloud Platform and its Big Data processing, IOT sensor connectivity and [[TensorFlow]] based state of the art machine learning can be used to predict failures and more importantly extend the life of the production equipment leading to break through innovation in production automation and significantly improving productivity and manufacturing flexibility.  About the speaker:   Salil Amonkar, Global Head of Manufacturing and Ai-ML Practice, Pluto7  Thought Leadership in Digital Business Transformation, Cloud and Saas Solution, Data driven [[Analytics]], Value Chain/Supply Chain and Ai and Machine Learning  Overall 25+ years expertise in innovative business transformation services for Value Chain/Supply Chain and Manufacturing, CPG, High Technology and related Industries. Implemented solutions leveraging multiple technologies, Cloud, SaaS, Hybrid architectures leveraging advanced and [[Predictive Analytics]] using data sciences, AI and machine learning for Sales and Marketing, Services, Quote to Cash and Supply Chain.  Have led several business transformational initiatives for Large Enterprise customers such as ABinBev(Anheuser Busch) Cisco, General Electric, [[IBM]], Vodafone, General Motors, Tata Motors and many others.
 Certified [[Google]] Professional-Data Engineer
 |}
@@ Line 131: / Line 131: @@
 <youtube>lPl6gyeRtRc</youtube>
 <b>Webinar: AI Predictive Maintenance - Empowering Your Remote Operations
-</b><br>Our CEO and Founder, Trevor Bloch, explores how AI Predictive [[Analytics]] can be applied to critical industrial infrastructure, to assist with reduced personal due to COVID-19 and provides an opportunity for significant maintenance savings which are critical to every business in the current economic climate. The webinar also includes an 'under the hood' look at the VROC platform.
+</b><br>Our CEO and Founder, Trevor Bloch, explores how AI [[Predictive Analytics]] can be applied to critical industrial infrastructure, to assist with reduced personal due to COVID-19 and provides an opportunity for significant maintenance savings which are critical to every business in the current economic climate. The webinar also includes an 'under the hood' look at the VROC platform.
 |}
 |}<!-- B -->
@@ Line 199: / Line 199: @@
 <youtube>6YEp8v_2qm8</youtube>
 <b>Predictive Maintenance Use Cases From The Energy Sector - Umid Akhmedov
-</b><br>In this session Umid will discuss some use cases from an energy company that is taking advantage of predictive [[analytics]] to make the world greener. Key takeaways:  Align with the business strategy, Build a solid data backbone, formalize the process. #HyperightDataTalks is a video podcast of interviews with some of the most innovative minds, enterprise practitioners, technology and service providers, start-ups and academics, working with Data Science, Data Management, Big Data, [[Analytics]], AI, IOT and much more. For more interviews, audio podcast and videos from some of the best presentations from our Data Summits, please visit http://www.hyperight.com Presentation recorded during Maintenance Analytics Summit 2018 - http://maintenanceanalyticssummit.com/
+</b><br>In this session Umid will discuss some use cases from an energy company that is taking advantage of [[Predictive Analytics]] to make the world greener. Key takeaways:  Align with the business strategy, Build a solid data backbone, formalize the process. #HyperightDataTalks is a video podcast of interviews with some of the most innovative minds, enterprise practitioners, technology and service providers, start-ups and academics, working with Data Science, Data Management, Big Data, [[Analytics]], AI, IOT and much more. For more interviews, audio podcast and videos from some of the best presentations from our Data Summits, please visit http://www.hyperight.com Presentation recorded during Maintenance Analytics Summit 2018 - http://maintenanceanalyticssummit.com/
 |}
 |}<!-- B -->
@@ Line 225: / Line 225: @@
 <youtube>EINuK6H3cx8</youtube>
 <b>Preventing Failures with Predictive Maintenance Webinar
-</b><br>What would it mean to your organization if you could predict system failures or quality issues before they happen? Preventing failures can help your organization reduce unscheduled downtime, waste, and rework – and avoid costly disruptions in operations. With predictive [[analytics]], it’s possible to proactively manage maintenance and improve operational efficiency by discovering the chance of a failure before it takes place. In this past webinar, we discussed how you can utilize [[Microsoft]] Azure Machine Learning, R, and the Cortana Intelligence Suite to predict and prevent system failure, and explored the benefits of calculating KPIs such as Remaining Useful Life, Time to Failure, and Failure within a certain tim
+</b><br>What would it mean to your organization if you could predict system failures or quality issues before they happen? Preventing failures can help your organization reduce unscheduled downtime, waste, and rework – and avoid costly disruptions in operations. With [[Predictive Analytics]], it’s possible to proactively manage maintenance and improve operational efficiency by discovering the chance of a failure before it takes place. In this past webinar, we discussed how you can utilize [[Microsoft]] Azure Machine Learning, R, and the Cortana Intelligence Suite to predict and prevent system failure, and explored the benefits of calculating KPIs such as Remaining Useful Life, Time to Failure, and Failure within a certain tim
 |}
 |<!-- M -->
@@ Line 277: / Line 277: @@
 ||
 <youtube>_bt8gT63al0</youtube>
-<b>Delivering Value Through Predictive [[Analytics]] in Chemical Process Industries
+<b>Delivering Value Through [[Predictive Analytics]] in Chemical Process Industries
-</b><br>In this webinar with our partner, PPT, learn how machine learning and predictive [[analytics]] was used to save a million dollars in a petrochemical facility through optimizing parameters of cracked gas compressor loop. In this 40-minute session we’ll take you through the following agile [[analytics]] implementation approach of:
+</b><br>In this webinar with our partner, PPT, learn how machine learning and [[Predictive Analytics]] was used to save a million dollars in a petrochemical facility through optimizing parameters of cracked gas compressor loop. In this 40-minute session we’ll take you through the following agile [[analytics]] implementation approach of:
 . Defining the Problem Statement and Hypothesis
 . Extracting-transforming-loading (ETL) of data silos (DCS, SCADA, PLS, LIMS, ERP and others)
@@ Line 306: / Line 306: @@
 <b>The Future of EAM: Top 5 Technology Trends
 </b><br>FuseForward  We take an in-depth look at the next wave of advancements for EAM and provides simple strategies for getting more out of your EAM systems. His talk will cover:
-• Mobile workforce • IoT enablement  • Predictive [[analytics]]  • Real-time [[analytics]] • Augmented reality
+• Mobile workforce • IoT enablement  • [[Predictive Analytics]]  • Real-time [[analytics]] • Augmented reality
 |}
 |<!-- M -->