A Simple SOM Neural Network Based Fault Detection Model for Fault Diagnosis of Rolling Bearings

Rolling bearings are common parts in the transmission systems and have been widely used in various kinds of applications. The normal operation of the rolling bearings hence plays an important role on the efficiency of the system performance. However, due to hostile working environment the rolling bearings are prone to failures. The transmission systems may break down when there occurs faults in the rolling bearings. As a result, it is essential to detect the faults of rolling bearings. However, when use artificial intelligence method to diagnose the rolling bearings faults the signal processing is extensively complex while very few works have been done on the simplification of the artificial neural network (ANN) models for the rolling bearings fault detection. To deal with this problem, a simple self-organized map (SUM) neural network method together with a principal component analysis (PCA) based feature reduction procedure is proposed to diagnosis rolling bearings faults in this work. The vibration data of the normal and faulty rolling bearings was acquired from an experimental test bed. The PCA was firstly used to extract distinct fault features. Then the SUM was employed to train and learn the fault features to identify the fault patterns. The fault detection results show that the proposed method is feasible and effective for the fault diagnosis of rolling bearings. The fault detection rate is beyond 89.0%.