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

Abstract

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.

Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis

“…However, they are a kind of linear algorithm, and have disadvantages in processing non-linearity cases. To further develop ICA, Bach [20] presented the kernel ICA (KICA), which is based on nonlinear function space and can deal with nonlinear separations. The details of KICA can refer to Bach [20].…”

“…Yang et al [17] proved that by means of ICA and the further feature extraction strategy based on residual mutual information (RMI), higher than second order features embedded in multi-channel vibration measurements can be captured effectively. Literature [18][19][20] also had shown the usefulness of the ICA for the gear fault detection. Nevertheless, in their work the noise sources were supposed to be mixed linearly with the gear fault vibration and the linear ICA was employed to process the experimental vibration data.…”

Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices

“…The development of the local model is based on the Fuzzy k-Nearest Neighbors (F-kNN) approach. Compared to other techniques, F-kNN is simple, easily interpretable and can achieve an acceptable accuracy rate [17], [18], [19]. The fuzzy version of k-NN averages the value of the points closest to the query point, on the assumption that points close to each other have similar values [20].…”

Hybrid Incremental Modeling Based on Least Squares and Fuzzy $K$-NN for Monitoring Tool Wear in Turning Processes

“…Compared to other techniques, F -feNN is simple, easily interpretable and can achieve an acceptable accuracy rate [14], [19], [20]. The fuzzy version of fe-NN averages the value of the points closest to the query point, on the assumption that points close to each other have similar values [21].…”