Reference number : GC-2017-Q1-00555, amendment number : , agreement type : G, recipient legal name : Zhao, Qing (University of Alberta), recipient country : CA, recipient province : nan, recipient city : Alberta, recipient postal code : nan, program name english : Engage Plus Grants for Universities, program name french : Subvention d'engagement partenarial Plus pour les universités, program purpose english : Process/Equipment performance management is considered to be a challenging task because processes we are_x000D_
dealing with are mostly dynamic, nonlinear in nature and interconnected. Incipient changes in relationship_x000D_
between underlying variables are not easy to detect and identify through simple process data trending. For a_x000D_
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 conducting_x000D_
FDD are different, and algorithms and design objectives of FDD are usually different too. Clearly for a truly_x000D_
autonomous and intelligent health monitoring system, a metastrategy needs to be developed to integrate_x000D_
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 Grant_x000D_
project, we aim to work closely with Honeywell Process Solutions to investigate and develop an innovative_x000D_
data-driven fault detection and diagnosis framework by applying a hierarchical deep learning approach., program purpose french : Process/Equipment performance management is considered to be a challenging task because processes we are_x000D_
dealing with are mostly dynamic, nonlinear in nature and interconnected. Incipient changes in relationship_x000D_
between underlying variables are not easy to detect and identify through simple process data trending. For a_x000D_
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 conducting_x000D_
FDD are different, and algorithms and design objectives of FDD are usually different too. Clearly for a truly_x000D_
autonomous and intelligent health monitoring system, a metastrategy needs to be developed to integrate_x000D_
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 Grant_x000D_
project, we aim to work closely with Honeywell Process Solutions to investigate and develop an innovative_x000D_
data-driven fault detection and diagnosis framework by applying a hierarchical deep learning approach., agreement title english : A hierarchical approach to data driven fault detection and diagnosis (FDD), agreement title french : A hierarchical approach to data driven fault detection and diagnosis (FDD), agreement number : EGP2, agreement value : 12500.0, agreement start date : 2017-04-25, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2018-2019), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada