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Vibration-based condition monitoring of compressors by principal component analysis and discriminant analysis [Elektronski vir]
Potočnik, Primož, 1969- ; Govekar, Edvard
Vibration-based condition monitoring and fault detection approach for compressors built in refrigeration appliances is proposed. The method combines feature extraction and principal component ...analysis (PCA), and compares unsupervised k-means clustering and discriminant analysis (DA). The method is demonstrated on a case study based on a large dataset of 10.000 industrially acquired vibration measurements of compressors during the production of refrigeration appliances. The initial step of the proposed method is feature extraction, based on statistics, statistical moments, and spectral analysis. A selected single feature was applied for statistical detection of initial compressor faults, based on which the initial compressor classes were defined as 'normal', 'noisy', and 'inactive'. In the next step, extracted features were transformed by PCA and only the first two principal components, contributing over 90% of variability, were retained for subsequent analysis. Three initial classes were applied to initialize DA. The results of linear DA revealed many additional 'noisy' and 'inactive' samples that were not evident from a single extracted feature. Furthermore, an additional cluster defining new class 'unstable' was detected, indicating a new type of defect characterized by high vibration transients. Results of DA reveal decision boundaries between all classes, and confirm the efficiency of the proposed method. Finally, the results are compared also with an unsupervised k-means clustering which shows that unsupervised clustering doesn't provide appropriate decision boundaries. The proposed DA-based approach detects compressors with defects and has the potential to detect novel classes of unusual or faulty operation. The method can be effectively applied for industrial condition monitoring of compressors.
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