Data Analytics and Learning Based Industrial Diagnostics and Monitoring Systems

基于数据分析和学习的工业诊断和监控系统

• 批准号: 543899-2019
• 负责人: Zhao, Qing
• 金额: $ 4.81万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=720820
• 关键词: Data; Analytics; Learning; Based; Industrial

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.

从2016年夏季开始，在Engage Grant的支持下，与加拿大埃德蒙顿的Honeywell Connected Factory(HCP，前身为Honeywell Process Solutions)开展了协作研究，并于2017年继续进行Engage Plus Grant。提出了一种自主数据驱动的故障诊断与监测方案。建立在核心数据驱动模型基础上的是用于自主检测、诊断和预测的分层框架。在复杂的工业过程中，采集到的数据表现出独特的特点，包括高维、非线性、时变性和自相关性。因此，前一次合作带来的挑战之一是数据模型的准确性，也称为(实际流程/工厂的)“数字孪生”。此外，基于KPI预测的预后具有重要意义。这促使我们研究最新的人工智能口味的机器学习技术，这些技术能够自动调整，而且更通用。在这个项目中，我们建议探索各种机器学习和人工智能技术，并将它们部署到过程健康监测中。这将使为行业合作伙伴开发的自主故障诊断和监测系统的第一个原型得到显着改进。