- Title
- Unitary anomaly detection for ubiquitous safety in machine health monitoring
- Creator
- Amar, Muhammad; Gondal, Iqbal; Wilson, Campbell
- Date
- 2012
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/74330
- Identifier
- vital:7238
- Identifier
-
https://doi.org/10.1007/978-3-642-34500-5
- Identifier
- ISBN:9783642344992
- Abstract
- Safety has always been of vital concern in both industrial and home applications. Ensuring safety often requires certain quantifications regarding the inclusive behavior of the system under observation in order to determine deviations from normal behavior. In machine health monitoring, the vibration signal is of great importance for such measurements because it includes abundant information from several machine parts and surroundings that can influence machine behavior. This paper proposes a unitary anomaly detection technique (UAD) that, upon observation of abnormal behavior in the vibration signal, can trigger an alarm with an adjustable threshold in order to meet different safety requirements. The normalized amplitude of spectral contents of the quasi stationary time vibration signal are divided into frequency bins, and the summed amplitudes frequencies over bin are used as features. From a training set consisting of normal vibration signals, Gaussian distribution models are obtained for each feature, which are then used for anomaly detection.
- Publisher
- Springer
- Relation
- 19th International Conference on Neural Information Processing (INCONIP) p. 361-368
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; machine health monitoring; Anomaly detection; Spectral content; Ubiquitous safety
- Reviewed
- Hits: 1418
- Visitors: 1378
- Downloads: 1
| Thumbnail | File | Description | Size | Format |
|---|