- Title
- Complex anomaly for enhanced machine independent condition monitoring
- Creator
- Amar, Muhammad; Gondal, Iqbal; Wilson, Campbell
- Date
- 2015
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/100866
- Identifier
- vital:10611
- Identifier
-
https://doi.org/10.1109/ICOSST.2015.7396409
- Abstract
- Safety in machine applications requires tracking machine health during the time of operations. Anomaly detection techniques are used to model normal behavior of the machines and raise an alarm if any anomaly is observed. But traditional anomaly detection techniques do not identify type and severity of aberrance in terms of amplitude, pattern or both. Once the anomalous behavior is observed then fault detection techniques are applied to diagnose faults. For machine independent condition monitoring (MICM) a range of features transforms are needed for autonomous learning of the fault classifiers for different parameters to identify variety of fault types which requires huge amount of time. In this paper a novel complex anomaly plan (CAP) representation has been proposed with amplitude anomalies on real and pattern anomalies on imaginary axis. To plot amplitude and pattern anomalies in the CAP, normal state vibrations frequency features are used to train Gaussian models for each of the frequency. The dynamic location of the anomaly plotted in the CAP gives a measure of the intensity of the anomaly, where real and imaginary axis components help the fault classifier to make an appropriate selection of the transform and thus enhances the efficiency of MICM framework. © 2015 IEEE.; ICOSST 2015 - 2015 International Conference on Open Source Systems and Technologies, Proceedings
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 9th International Conference on Open Source Systems and Technologies, ICOSST 2015; Lahore, Pakistan; 17th-19th December 2015
- Rights
- Copyright © 2015 IEEE.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Anomaly detection; Bearing faults; Machine Health Monitoring (MHM)
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