Gear Multi-Faults Diagnosis of a Rotating Machinery Based on Independent Component Analysis and Fuzzy K-Nearest Neighbor

Gearboxes are extensively used in various areas including aircraft, mining, manufacturing, and agriculture, etc. The breakdowns of the gearbox are mostly caused by the gear failures. It is therefore crucial for engineers and researchers to monitor the gear conditions in time in order to prevent the malfunctions of the plants. In this paper, a condition monitoring and faults identification technique for rotating machineries based on independent component analysis (ICA) and fuzzy k-nearest neighbor (FKNN) is described. In the diagnosis process, the ICA was initially employed to separate characteristic vibration signal and interference vibration signal from the parallel time series obtained from multi-channel accelerometers mounted on different positions of the gearbox. The wavelet transform (WT) and autoregressive (AR) model method then were performed as the feature extraction technique to attain the original feature vector of the characteristic signal. Meanwhile, the ICA was used again to reduce the dimensionality of the original feature vector. Hence, the useless information in the feature vector could be removed. Finally, the FKNN algorithm was implemented in the pattern recognition process to identify the conditions of the gears of interest. The experimental results suggest that the sensitive fault features can be extracted efficiently after the ICA processing, and the proposed diagnostic system is effective for the gear multi-faults diagnosis, including the gear crack failure, pitting failure, gear tooth broken, compound fault of wear and spalling, etc. In addition, the proposed method can achieve higher performance than that without ICA processing with respect to the classification rate.