Detection of unknown bearing faults using re-weighted symplectic geometric node network characteristics and structure analysis

Abstract

When appears unknown fault type signal, typical classifiers would misclassify this fault to other types. And when equipment works, it will be accompanied by strong background noise, resulting in low accuracy of fault diagnosis. Therefore, a detection method based on re-weighted symplectic geometric node network (RSGNN) characteristics and structure analysis is proposed for noise reduction and unknown fault detection in this paper. First, the original signal was projected into network form to eliminate the interference components and characterize the inner topological relation. Then, the complex network (CN) theory was introduced to establish enhanced diagnosis features. Finally, the unsupervised Newman Fast (NF) community clustering algorithm was utilized to realize the unknown type signal detection. On the one hand, the proposed method defines Autocorrelation kurtosis (AK) and the ΓR to help better select the useful components and to eliminate the noise interference. On the other hand, the proposed method with the help of CN theory and community division to realize the unknown fault unsupervised detection. The experimental results on five groups bearing datasets show that the proposed method could achieve good unknown fault detection accuracy and maintain good robustness in a high-noise environment, indicating that it has advantages over other methods.