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

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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.