- Title
- Autonomous behavior modeling approach for diverse anomaly detection application
- Creator
- Amar, Muhammad; Wilson, Campbell; Gondal, Iqbal
- Date
- 2014
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161617
- Identifier
- vital:12512
- Identifier
-
https://doi.org/10.1109/ICOSST.2014.7029331
- Identifier
- ISBN:9781479920549
- Abstract
- For absolute process safety in diverse machine applications, timely and reliable anomalous behavior detection is very crucial. Different machine applications have different normal behavior patterns and safety standards thus require adjustable and adaptive anomaly detection techniques. In this paper an autonomous behavior modeling approach for anomaly detection has been presented. In this approach time segmented vibration signals from the machines are transformed into spectral contents. After normalization, these frequency domain contents are divided into weighted frequency bins and then Gaussian models are achieved for these frequency bins over the entire training set. Using summation rule on the outputs of Gaussian models a single indicative measure of the machine health: normality score is obtained. The sensitivity of the normality score and anomaly detector towards potential anomalous signals can be controlled by using different number of bins and weights. Suitable parameters values, number of bins and weights profile, for anomaly detector model are selected autonomously using minimum value of the cost function. The increase of normality score of this model above a certain threshold is considered an alarm indicating anomalous behavior. Thus the proposed method enables us to achieve autonomously a suitable anomaly detection model with suitable parameters with controlled sensitivity during the test phase.
- Publisher
- IEEE
- Relation
- ICOSST 2014 - 2014 International Conference on Open Source Systems and Technologies, Lahore, Pakistan, 18-20th Dec 2014 p. 122-127
- Rights
- Copyright IEEE
- Rights
- This metadata is freely available under a CCO license
- Subject
- Anomaly detection; Bearing faults; Machine Health Monitoring (MHM)
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