A Novel Method for Identifying Crack and Shaft Misalignment Faults in Rotor Systems under Noisy Environments Based on CNN

Abstract

Crack and shaft misalignment are two common types of fault in a rotor system, both of which have very similar dynamic response characteristics, and the vibration signals are vulnerable to noise contamination because of the interaction among different components of rotating machinery in the actual industrial environment, resulting in great difficulties in fault identification of a rotor system based on vibration signals. A method for identification of faults in the form of crack and shaft misalignments is proposed in this paper, which combines variational mode decomposition (VMD) and probabilistic principal component analysis (PPCA) to denoise the collected vibration signals from a test rig and then achieve signal feature extraction and fault classification with convolutional artificial neural network (CNN). The key parameters of the CNN are optimized and determined by genetic algorithm (GA) firstly, and the domain adaptability of the trained network is verified by the signals with different signal-to-noise ratio (SNR) values; then, the noisy vibration signals are decomposed into multiple band-limited intrinsic modal functions by VMD, and further data dimension reduction is performed by PPCA to realize the separation of the useful signals from noise; finally, the crack and shaft misalignment of the rotor system are identified by the optimized CNN. The results show that the proposed method can effectively remove the interference noise and extract the intrinsic features of the vibration signals, and the recognition rates of crack and shaft misalignment faults for the rotor system with different SNR values are more than 99%, which is considered to be very effective and useful.

Stepwise Intelligent Diagnosis Method for Rotor System with Sliding Bearing Based on Statistical Filter and Stacked Auto-Encoder

“…2020, 10, 2477 of 14 only difference is that masked fraction is set to 0.5 (this parameter in SAE is setting to 0) to denoise. The sigmoid function [26] is selected as active function for the BPNN, DAE, and DBN, the optimizing search algorithms for adjusting the parameters of those neural networks is the traditional gradient descent [27]. Meanwhile, both DBN and DAE directly handle the raw signals, whereas the signal is without statistical filter and stepwise diagnosis.…”

Crack classification in rotor-bearing system by means of wavelet transform and deep learning methods: an experimental investigation

“…Zhao et al, introduced a procedure that has capacity in identification of crack and misalignment in a rotor system which combines variational mode decomposition (VMD) and probabilistic principal component analysis (PPCA) to reduction environmental noises the captured vibration signals from an experimental rig and then gain signal feature extraction and fault classification by using CNN [11].…”

Dynamic Characteristics Analysis of a Coupled Multi-crack Rotor System

“…So it is of great significance to detect cracks in a timely manner and accurately, especially in the early stage of crack propagation in order to maintain healthy and stable operations of rotors and avoid failure of rotating machines. At present, the crack detection methods can be categorised as the modelbased, the signal-based [1,2], and the artificial intelligence-based methods [3]. The model-based method can directly extract features of cracks from the vibration responses based on the finite element(FE) model of a cracked rotor and identify detailed crack parameters, even study the relationship between crack parameters and vibration characteristics, so it is widely used in online monitoring of structural health and fault diagnosis of rotors [4].…”