- Title
- Severity invariant machine fault diagnosis
- Creator
- Yaqub, Muhammad; Gondal, Iqbal; Kamruzzaman, Joarder
- Date
- 2011
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/74340
- Identifier
- vital:7260
- Identifier
-
https://doi.org/10.1109/ICIEA.2011.5975544
- Identifier
- ISBN:9781424487554
- Abstract
- Vibration signals used for abnormality detection in machine health monitoring (MHM) suffer from significant variation with fault severity. This variation causes overlap among the features belonging to different types of faults resulting in severe degradation of fault detection accuracy. This paper identifies a new problem due to severity variant features and proposes a novel adaptive training set and feature selection (ATSFS) scheme based upon the orientation of the test data. In order to build ATSFS and validate its performance, training and testing data are obtained from different severity levels. To capture the non-stationary behavior of vibration signal, robust tools such as wavelet transform (WT) for time-frequency analysis are employed. Simulation studies show that ATSFS attains high classification accuracy even if training and testing data belong to different severity levels.
- Publisher
- The Institute of Electrical and Electronics Engineers
- Relation
- 6th IEEE Conference on Industrial Electronics and Applications p. 21-26
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0805 Distributed Computing; Adaptive feature selection; Adaptive training set; Machine health monitoring; Severity invariant
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