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Artificial Intelligence for Predictive Maintenance Case Study - Blair Fraser, Lakeside
ARC Advisory Group is the leading technology research and advisory firm for industry, infrastructure and cities. Our coverage of technology and trends extends from business systems to product and asset lifecycle management, Industrial IoT, Industry 4.0, supply chain management, operations management, energy optimization and automation systems. Our analysts and consultants have the industry knowledge and the first-hand experience to help find the best answers to the complex business issues facing organizations today. Contact us: https://www.arcweb.com/about/contact-us
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SAP Predictive Maintenance and Service
learn how SAP Predictive Maintenance and Service can help reduce maintenance cost, increase asset availability, improve customer satisfaction and generate new service revenue.
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Predictive Maintenance Using Recurrent Neural Network (RNN)s
AnacondaCon 2018. Justin Brandenburg. The idea behind predictive maintenance is that the failure patterns of various types of equipment are predictable. If an organization can accurately predict when a piece of hardware will fail, and replace that component before it fails, it can achieve much higher levels of operational efficiency. With many devices now including sensor data and other components that send diagnosis reports, predictive maintenance using big data is increasingly accurate and effective. In this case, how can we enhance our data monitoring to predict the next event? This talk will present an actual use case in the IoT industry 4.0 space. Justin will present an entire workflow of data ingestion, bulk ETL, data exploration, model training, testing, and deployment in a real time streaming architecture that can scale. He will demonstrate how he used Anaconda Python 3.5 and Pyspark 2.1.0 to wrangle data and train a recurrent neural network to predict whether the next event in a real time stream indicated that maintenance was required.
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Predictive Maintenance & Monitoring using Machine Learning: Demo & Case study (Cloud Next '18)
Learn how to build advanced predictive maintenance solution. Learn what is predictive monitoring and new scenarios you can unlock for competitive advantage. MLAI223
Event schedule → http://g.co/next18 Watch more Machine Learning & AI sessions here → http://bit.ly/2zGKfcg Next ‘18 All Sessions playlist → http://bit.ly/Allsessions
Subscribe to the Google Cloud channel! → http://bit.ly/NextSub
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Machine Learning for Maintenance
We’re putting ideas like machine learning for maintenance to work across our operations to minimize unplanned maintenance, reduce overall maintenance costs and extend equipment life. Through our partnership with Google Cloud and Pythian, we are unlocking new insights from millions of data points we collect to predict issues that were previously unpredictable.
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AI: The Future Of Intelligent Maintenance
Huawei is leading the development of intelligent maintenance with Robust Network Service. With over 20 projects going on around the world, telecom operators are benefitting from a range of #AI powered maintenance solutions. This video highlights some of the capabilities and benefits of AI prediction maintenance to the telecom industry.
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Downer Australia: Transforming train maintenance with AI
Downer, a company with over 100 years’ rail experience, is using AI to make sense of operational data and put it into the hands of the workers that need it. Find out how this Australian heavy industry company is working together with Microsoft to support the smooth delivery of Waratah commuter services across Sydney.
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Predictive Maintenance and Monitoring using AI and Machine Learning
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
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Predictive maintenance & AI in manufacturing leads to new risk opportunities
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
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Predictive Maintenance
Manufacturers of aircraft, engines, propellers and appliances have traditionally called for performing preventive maintenance on a fixed timetable. A prime example is engine and propeller TBOs. More recently, this time-based approach has given way to condition-based preventive maintenance based on regular repetitive inspections. Now we're beginning to see this inspection-driven approach giving way to predictive maintenance based on analysis of data from sensors installed on the aircraft and engine. In this webinar, Mike Busch A&P/IA discusses this latest trend and how its starting to trickle down to owner-flown piston GA. Savvy Aviation offers Professional Maintenance Services to owners of General Aviation aircraft, such as: SavvyMx (Professional Maintenance Management), SavvyQA (Expert Consulting), SavvyPrebuy, SavvyAnalysis (Engine Data Analysis) and Breakdown Assistance. Savvy also publishes a monthly newsletter with lots of interesting information for the general aviation enthusiast; subscribe to it at http://www.savvyaviation.com/home/ge.... For more information, visit us at http://savvyaviation.com. This webinar was hosted by the Experimental Aircraft Association (EAA).
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AWS re:Invent 2019: Combining IoT and machine learning for predictive maintenance (IOT309-R1)
Predictive maintenance captures the state of your devices to identify potential breakdowns before they impact operations, often resulting in an increase in equipment life span. In this session, learn how to progress your IoT journey and move from reactive to proactive with Amazon AWS IoT services and AWS machine-learning services. We also teach you how you can train ML models in the cloud and infer at the edge to predict problems faster.
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Webinar: AI Predictive Maintenance - Empowering Your Remote Operations
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.
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Practical Machine Learning for Predictive Maintenance
Simon Xu
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