Operations & Maintenance

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• Case Studies
• AI for Predictive Maintenance Applications in Industry – Examining 5 Use Cases | Pamela Bump - Techemergence
• Industrial AI Applications – How Time Series and Sensor Data Improve Processes | Daniel Faggella - Techemergence
• Deloitte Consulting takes aim at artificial intelligence (AI) software tools for predictive maintenance | Military/Aerospace Electronics ...to build the Joint Common Foundation (JCF) artificial intelligence development environment for the Joint Artificial Intelligence Center (JAIC).

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

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.

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.

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

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.

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.

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.

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