A hierarchical approach to data driven fault detection and diagnosis (FDD)

数据驱动故障检测和诊断 (FDD) 的分层方法

• 批准号: 506484-2017
• 负责人: Zhao, Qing
• 金额: $ 0.91万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Engage Plus Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640660
• 关键词: hierarchical; approach; data; driven; fault

Process/Equipment performance management is considered to be a challenging task because processes we aredealing with are mostly dynamic, nonlinear in nature and interconnected. Incipient changes in relationshipbetween underlying variables are not easy to detect and identify through simple process data trending. For asystem/process, fault detection and diagnosis (FDD) should be done at different levels (e.g. sensor level,equipment level, unit level, etc.), since at each level the information and resources available for conductingFDD are different, and algorithms and design objectives of FDD are usually different too. Clearly for a trulyautonomous and intelligent health monitoring system, a metastrategy needs to be developed to integratedifferent design schemes based on specific design objectives at different asset hierarchy. Furthermore,application of data driven FDD at both equipment and unit-level is deemed novel. In this Engage Plus Grantproject, we aim to work closely with Honeywell Process Solutions to investigate and develop an innovativedata-driven fault detection and diagnosis framework by applying a hierarchical deep learning approach.

过程/设备性能管理被认为是一项具有挑战性的任务，因为我们所处理的过程大多是动态的，非线性的，相互关联的。底层变量之间关系的初始变化不容易通过简单的过程数据趋势来检测和识别。对于系统/过程，故障检测和诊断（FDD）应该在不同的层次（例如传感器级、设备级、单元级等）进行，因为在每个层次进行故障检测和诊断的信息和资源是不同的，故障检测和诊断的算法和设计目标通常也是不同的。显然，对于一个真正自主和智能的健康监测系统，需要开发一个元策略来基于不同资产层次的特定设计目标集成不同的设计方案。此外，数据驱动的FDD在设备和单元级别的应用被认为是新颖的。在这个Engage Plus赠款项目中，我们的目标是与霍尼韦尔过程解决方案紧密合作，通过应用分层深度学习方法，研究和开发一个创新的数据驱动故障检测和诊断框架。