Research on Rotating Machinery Fault Diagnosis Method Based on Energy Spectrum Matrix and Adaptive Convolutional Neural Network

Abstract

Traditional intelligent fault diagnosis methods focus on distinguishing different fault modes, but ignore the deterioration of fault severity. This paper proposes a new two-stage hierarchical convolutional neural network for fault diagnosis of rotating machinery bearings. The failure mode and failure severity are modeled as a hierarchical structure. First, the original vibration signal is transformed into an energy spectrum matrix containing fault-related information through wavelet packet decomposition. Secondly, in the model training method, an adaptive learning rate dynamic adjustment strategy is further proposed, which adaptively extracts robust features from the spectrum matrix for fault mode and severity diagnosis. To verify the effectiveness of the method, the bearing fault data was collected using a rotating machine test bench. On this basis, the diagnostic accuracy, convergence performance and robustness of the model under different signal-to-noise ratios and variable load environments are evaluated, and the feature learning ability of the method is verified by visual analysis. Experimental results show that this method has achieved satisfactory results in both fault pattern recognition and fault severity evaluation, and is superior to other machine learning and deep learning methods.

Application of Deep Learning in Fault Diagnosis of Rotating Machinery

“…CNN's powerful feature self-extraction capabilities are not used in end-to-end fault diagnosis. In recent years, some scholars have taken vibration signals as research objects, introduced CNN into fault diagnosis of bearings and hydraulic pumps, and achieved good results by converting vibration signals into twodimensional time-frequency diagrams for fault diagnosis [24][25][26][27][28][29]. With vibration signals as a one-dimensional time-series signal, the data points at per moment are correlated.…”

A Hybrid Intelligent Fault Diagnosis Strategy for Chemical Processes Based on Penalty Iterative Optimization

“…In addition, in recent years, new models have been developed by combining different datadriven methods to achieve the desired results, such as ICA-PCA [23], PCA-XGBoost [24] and the PCA-adaptive neuro-fuzzy inference system [25]. With the popularization of information construction in industrial plants and the development of deep learning (DL) methods, the research of FDD has ushered in another climax, such as convolutional neural networks (CNNs) [26,27] and deep belief networks (DBNs) [28].…”