Application of machine learning in fault diagnostics of mechanical systems
Najafi, Massieh, Auslander, David M. , Bartlett, Peter L., & Haves, Philip (2008) Application of machine learning in fault diagnostics of mechanical systems. In Proceedings of the World Congress on Engineering and Computer Science 2008: International Conference on Modeling, Simulation and Control 2008, International Association of Engineers, San Fransisco, pp. 957-962.
A diagnostic method based on Bayesian Networks (probabilistic graphical models) is presented. Unlike conventional diagnostic approaches, in this method instead of focusing on system residuals at one or a few operating points, diagnosis is done by analyzing system behavior patterns over a window of operation. It is shown how this approach can loosen the dependency of diagnostic methods on precise system modeling while maintaining the desired characteristics of fault detection and diagnosis (FDD) tools (fault isolation, robustness, adaptability, and scalability) at a satisfactory level. As an example, the method is applied to fault diagnosis in HVAC systems, an area with considerable modeling and sensor network constraints.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Conference Paper|
|Additional Information:||Winner of Best Student Paper Award of the Conference|
|Keywords:||Fault detection, Bayesian networks, Machine learning, System diagnostics, HVAC systems|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
|Copyright Owner:||Copyright 2008 [please consult the author]|
|Deposited On:||05 Sep 2011 22:22|
|Last Modified:||05 Sep 2011 22:23|
Repository Staff Only: item control page