Abstract
This paper proposes a novelty detection-based method for machine condition monitoring (MCM) using vibration signals and a new feature extraction method based on higher-order statistics of the power spectral density. This novel MCM method is based on Kohonen's self-organising map and adopts a multidimensional dissimilarity measure for dual class classification. The approach is designed to be highly modular and scale well for a multi-sensor condition monitoring environment. Experiments using real-world vibration data sets with upto eight sensors have shown high accuracy in classification and robustness across different condition monitoring applications.
Original language | English |
---|---|
Pages (from-to) | 593-610 |
Number of pages | 18 |
Journal | Mechanical Systems and Signal Processing |
Volume | 20 |
Issue number | 3 |
DOIs | |
Publication status | Published - Apr 2006 |
Keywords
- Artificial neural network
- Condition monitoring
- Features extraction
- Higher-order statistics
- Novelty detection
- Self-organising map
- Vibration signal processing
ASJC Scopus subject areas
- Signal Processing
- Mechanical Engineering