Self-Organizing Maps and Learning Vector Quantization forFeature Sequences
Neural Processing Letters
Expert Systems with Applications: An International Journal
A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system
Expert Systems with Applications: An International Journal
Application of mother wavelet functions for automatic gear and bearing fault diagnosis
Expert Systems with Applications: An International Journal
Fault diagnosis of ball bearings using machine learning methods
Expert Systems with Applications: An International Journal
A rule-based intelligent method for fault diagnosis of rotating machinery
Knowledge-Based Systems
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This paper is focused on fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components using wavelet-based feature extraction. The statistical features required for the training and testing of artificial intelligence techniques are calculated by the implementation of a wavelet based methodology developed using Minimum Shannon Entropy Criterion. Seven different base wavelets are considered for the study and Complex Morlet wavelet is selected based on minimum Shannon Entropy Criterion to extract statistical features from wavelet coefficients of raw vibration signals. In the methodology, firstly a wavelet theory based feature extraction methodology is developed that demonstrates the information of fault from the raw signals and then the potential of various artificial intelligence techniques to predict the type of defect in bearings is investigated. Three artificial intelligence techniques are used for faults classifications, out of which two are supervised machine learning techniques i.e. support vector machine, learning vector quantization and other one is an unsupervised machine learning technique i.e. self-organizing maps. The fault classification results show that the support vector machine identified the fault categories of rolling element bearing more accurately and has a better diagnosis performance as compared to the learning vector quantization and self-organizing maps.