Reference number : GC-2017-Q1-02225, amendment number : , agreement type : G, recipient legal name : Lipsett, Michael (University of Alberta), recipient country : CA, recipient province : nan, recipient city : Alberta, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : All machines are prone to some kind of failure. Machinery diagnostics allows maintenance to be done before a catastrophic failure occurs, by observing some aspect of machine behaviour that indicates an impending problem and then using signal processing and feature extraction to determine whether a fault is present. Some techniques can also predict the remaining useful life of the machine in certain conditions. Unfortunately, limited observability and machine-specific approaches to condition monitoring make data-driven models difficult to apply in general applications. New sensor technologies and embedded sensor network standards hold promise to significantly improve the observability of faults in machinery, both for new equipment and for retrofits to existing components and systems. The new instalment of Discovery-Grant based research will test the hypothesis that improving observability of damage mechanisms will enable system reliability improvements. Individual methods and combined methods of fault classification will be assessed for laboratory-based fault diagnosis case studies of impact faults in rotating machinery and process equipment. Physics-based modeling will be used to evaluate how to measure faults sensitively in rolling-element bearings and process equipment subject to impact wear. The laboratory systems will be publicly available so that others can test the performance of their method using benchmark datasets. Fault models of chronic impact events apply to a wide range of rotating equipment &amp; process units, for process plants, wind turbine generators, and vehicles. Parametric fault identification models will be of direct benefit to industry and to other researchers. Comparative methods for ranking fault severity will yield better methods to predict equipment life and improve maintenance effectiveness. Datasets for machine damage cases will allow researchers to compare diagnostic techniques definitively, contributing to best practices. Improved fault diagnostics will contribute to more durable repairable equipment and products with longer service life, which is crucial for a more sustainable technological future., program purpose french : All machines are prone to some kind of failure. Machinery diagnostics allows maintenance to be done before a catastrophic failure occurs, by observing some aspect of machine behaviour that indicates an impending problem and then using signal processing and feature extraction to determine whether a fault is present. Some techniques can also predict the remaining useful life of the machine in certain conditions. Unfortunately, limited observability and machine-specific approaches to condition monitoring make data-driven models difficult to apply in general applications. New sensor technologies and embedded sensor network standards hold promise to significantly improve the observability of faults in machinery, both for new equipment and for retrofits to existing components and systems. The new instalment of Discovery-Grant based research will test the hypothesis that improving observability of damage mechanisms will enable system reliability improvements. Individual methods and combined methods of fault classification will be assessed for laboratory-based fault diagnosis case studies of impact faults in rotating machinery and process equipment. Physics-based modeling will be used to evaluate how to measure faults sensitively in rolling-element bearings and process equipment subject to impact wear. The laboratory systems will be publicly available so that others can test the performance of their method using benchmark datasets. Fault models of chronic impact events apply to a wide range of rotating equipment &amp; process units, for process plants, wind turbine generators, and vehicles. Parametric fault identification models will be of direct benefit to industry and to other researchers. Comparative methods for ranking fault severity will yield better methods to predict equipment life and improve maintenance effectiveness. Datasets for machine damage cases will allow researchers to compare diagnostic techniques definitively, contributing to best practices. Improved fault diagnostics will contribute to more durable repairable equipment and products with longer service life, which is crucial for a more sustainable technological future., agreement title english : Machinery Diagnostics Using Mechanistic and Data-Driven Models, agreement title french : Machinery Diagnostics Using Mechanistic and Data-Driven Models, agreement number : RGPIN, agreement value : 220000.0, agreement start date : 2017-05-10, 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 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), 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