Data Analytics and Learning Based Industrial Diagnostics and Monitoring Systems

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Collaborative research has been carried out with Honeywell Connected Plant (HCP, formerly known as Honeywell Process Solutions), Edmonton, Canada, starting summer 2016, with the support of Engage Grant, and continued in 2017 with the Engage Plus Grant. An autonomous data-driven fault diagnosis (FD) and monitoring scheme has been developed. Built upon the core data-driven model is a hierarchical framework for autonomous detection, diagnosis and prediction. In complex industrial process, data collected demonstrates unique characteristics, including high-dimensionality, nonlinearity, time-variance, and auto-correlation. As a result, one of the challenges from the previous collaboration concerns accuracy of the data model, also referred to as "digital-twin" (of the actual process/plant). In addition, prognosis based on KPI prediction is of great interests. This motives us to investigate the most recent artificial intelligence flavoured machine learning techniques, which are capable of auto-tuning and are more versatile. In this project, we propose to explore various machine learning and AI techniques, and deploy them to process health monitoring. This will lead to a significant improvement of the first prototype of the autonomous fault diagnosis and monitoring system developed for the industry partner.