Representation Learning for Fault Prediction
- Type: Conference Presentation
- Conference Type: AIChE Spring Meeting and Global Congress on Process Safety
- Presentation Date: August 19, 2020
- Duration: 20 minutes
- Skill Level: Intermediate
- PDHs: 0.40
Large volumes of industrial data in conjunction with machine learning provide new opportunities for better decision making and fault prediction in the process industry. We will present a systematic approach to process and synthesize data to build reliable models for fault prediction. We will address questions such as How are the data pre-processed? How do we identify the best model? What are the relevant modern machine learning tools? We will illustrate our approach using a year's worth of data from a real industrial Electric Arc Furnace (EAF) which is widely used for steelmaking and refining particulate ores into base metals. Specifically, we will show how the industrial raw data was pre-processed into a structured form that is amenable to representation learning. We will also illustrate the application of a wide-ranging set of machine learning tools to predict loss of plasma arc in EAF.
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|AIChE Member Credits||0.5|
|AIChE Graduate Student Members||Free|
|AIChE Undergraduate Student Members||Free|
|Computing and Systems Technology Division Members||Free|