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| | * [[Case Studies]] | | * [[Case Studies]] |
| | + | * [[Digital Twin]] |
| | * [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/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] | | * [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] |
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| | <b>Webinar: Business manufacturing applications for AI | | <b>Webinar: Business manufacturing applications for AI |
| | </b><br>AI isn't just for technology pioneers or academic organizations. The path to using advanced techniques like deep learning is realistic (and realizable) for businesses from the enterprise on down. In manufacturing, in particular, AI can be applied to customer-facing, support and internal systems — and can revolutionize predictive maintenance — right now. Led by AI experts at Peltarion and Futurice, this manufacturing-focused webinar will educate you on how to extract business value from AI and goes through relevant AI use cases for the manufacturing industry to help illustrate this, getting your business enabled quickly to leverage deep learning. To find out more about operational AI and the Peltarion Platform, go to peltarion.com/platform | | </b><br>AI isn't just for technology pioneers or academic organizations. The path to using advanced techniques like deep learning is realistic (and realizable) for businesses from the enterprise on down. In manufacturing, in particular, AI can be applied to customer-facing, support and internal systems — and can revolutionize predictive maintenance — right now. Led by AI experts at Peltarion and Futurice, this manufacturing-focused webinar will educate you on how to extract business value from AI and goes through relevant AI use cases for the manufacturing industry to help illustrate this, getting your business enabled quickly to leverage deep learning. To find out more about operational AI and the Peltarion Platform, go to peltarion.com/platform |
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| − | <b>The Core Principles of Predictive Maintenance (PDX ML Meetup)
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| − | </b><br>This talk was given by Ian Downard at the Portland Machine Learning Meetup on 12/13/2018. Slides at http://bit.ly/2A13J9p. In this presentation, Ian describes the core principles around data collection, feature engineering and machine learning for predictive maintenance.
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| − | <youtube>tl1pHVwlhRs</youtube>
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| − | <b>On Demand Webinar: The Right Solution for Predicting Machine Failure
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| − | </b><br>Predictive maintenance is a technique used in various industries to reduce machine downtime by predicting its failure. It is fair to say that most enterprises consider this a difficult technique to deploy in production. The right implementation uses a combination of the following steps:
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| − | - Real-time data ingestion from IoT devices
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| − | - Extract-transform-load (ETL) of this data and writing it into a data store
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| − | - Developing machine learning (ML) algorithms to extract insights into failure events and training these algorithms using the stored data
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| − | - Deploying the final algorithm(s) in production onto the target environment
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| − | - Monitoring the performance of the system and tuning the implementation as physical conditions change over time
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| − | MapR has partnered with RapidMiner to bring a holistic solution to help manufacturers predict machine failure accurately. The solution offers unique flexibility in design, multiple deployment options for convenient transition to production, and an easy-to-use UI.
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