Fault Detection in Gearbox With Non-Stationary Rotational Speed Using CWT Feature Extraction, PCA Reduction and ANN Classifier Methods

Gearbox fault diagnosis is one of the core research areas in the field of condition monitoring of rotating machines. The aim of this paper is to present an intelligent method for fault diagnosis of a kind of automotive gearbox in run-up condition based on vibration signals. The vibration signals are obtained from an acceleration sensor and sampled at constant time increment by AdvantechTM PCI-1712 card. Automotive gearbox test setup has been designed and constructed in Acoustics Research Laboratory in Amirkabir University of Technology. To process the non-stationary vibration signals, the re-sampling technique at constant angle increment is combined with the continuous wavelet transform (CWT) and the wavelet coefficients of the signals are obtained. The statistical parameters of the wavelet coefficients are extracted, and then the principle component analysis (PCA) is introduced to enhance the pattern recognition and reduce the dimensionality of the original feature space. Gearbox is considered in healthy, chipped tooth and worn teeth gears conditions. Finally, a feedforward multilayer perceptron (MLP) neural network is used for classification. The experimental results show that the adoption of PCA diagnosis method leads to higher accuracy and less training time for fault detection of the gear chip and wear.