Condition Assessment for Rolling Bearings Based on SOM

Aiming at the problems that single feature fault diagnosis accuracy was not too high, a rolling bearing condition assessment method was proposed based on SOM herein. Firstly, the multi-dimensional features were extracted from the original vibration signals and preprocessed by PCA, a fusion model was established by training SOM network and weight vectors of competitive neuron were obtained. Secondly, the fusion index, which was the minimum Euclidean distance between every sample values to the competitive neuron weighting vector, was achieved. Finally, the conditions of rolling bearings were classified by comparing the minimum Euclidean distances among the detected samples and the normal samples. The proposed method herein was applied to condition assessment of the rolling bearings, and the test results show that the fusion index is more sensitive and robust than that of original single feature during the stages of early faults; meanwhile, the fusion index may reflect the states of rolling bearings more accurately. © 2017, China Mechanical Engineering Magazine Office. All right reserved.