An Integrated Method of Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network Optimized by Sparrow Optimization Algorithm

Abstract

Intending to solve the problems including poor self-adaptive ability and generalization ability of the traditional categorizing method under big data, a parameter-optimized Convolutional Neural Network (CNN) based on Sparrow Search Algorithm (SSA) is proposed in this research. Initially, the raw data regarding a series of bearing vibration signals are processed with Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) to attain groups of time-frequency maps. Then, Locally Linear Embedding (LLE) and linear normalization are introduced to make these maps proper for the input of CNN. Next, the preprocessed data sets are utilized as training and testing samples for CNN, and the accuracy rate of the testing is considered as the fitness of SSA, which is used to search for optimal parameter combinations for CNN by SAA. Meanwhile, the construction of the CNN is determined by experience and other previous researches. Finally, an NN-based defect diagnosis model for bearings will be constructed after the SAA has determined the appropriate parameters. The model’s accuracy rate may reach 99.4 percent after repeated testing using samples, which is significantly superior to the classic fault detection approach and the fault diagnostic method based solely on shallow networks. This experimental result demonstrates that the suggested strategy may significantly increase the model’s self-adaptive feature extraction capacity and accuracy rate, implying a higher performance in defect diagnosis in the presence of huge data.