Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019)

Process/Equipment performance management is considered to be a challenging task because processes we arex000D
dealing with are mostly dynamic, nonlinear in nature and interconnected. Incipient changes in relationshipx000D
between underlying variables are not easy to detect and identify through simple process data trending. For ax000D
system/process, fault detection and diagnosis (FDD) should be done at different levels (e.g. sensor level,x000D
equipment level, unit level, etc.), since at each level the information and resources available for conductingx000D
FDD are different, and algorithms and design objectives of FDD are usually different too. Clearly for a trulyx000D
autonomous and intelligent health monitoring system, a metastrategy needs to be developed to integratex000D
different design schemes based on specific design objectives at different asset hierarchy. Furthermore,x000D
application of data driven FDD at both equipment and unit-level is deemed novel. In this Engage Plus Grantx000D
project, we aim to work closely with Honeywell Process Solutions to investigate and develop an innovativex000D
data-driven fault detection and diagnosis framework by applying a hierarchical deep learning approach.