Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis

The application of the existing complex network in fault diagnosis is usually modelled based on the time domain, resulting in the loss of sign frequency domain features, and making the extracted topology features of network too macroscopic and insensitive to local changes within the network. This chapter proposes a new method of local feature extraction based on frequency complex network (FCN) decomposition, and builds a new complex network structure feature, namely, sub-network average degree, on this basis. The variation law of signals in frequency domain is obtained with the aid of the structural features of the complex network. The local features sensitive to local changes of the network are applied to characterize the whole network, with flexible application and without limitation in mechanism. The average degree of sub-network could be regarded as feature parameters for rolling bearing fault diagnosis and degradation state recognition. Analysis of the experimental data and bearing life cycle data shows that the method proposed in this chapter is effective and that the extracted features have effective separability and high accuracy in fault recognition and the degradation detection of the life-cycle of rolling bearings combined with neural networks. Moreover, the proposed method has reference value for the processing and recognition of other non-stationary signals.