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.
Highlights
As the core component of rotating machinery, the health status of a rotor system will directly affect the normal operation of the entire equipment; as a rotor system often operates in a high temperature and high pressure environment, stress concentration will appear in some parts of the rotating shaft and fatigue cracks may gradually occur after long operation times, due to the long-term effect of complex alternating loads at high speed
convolutional artificial neural network (CNN) is further used to complete the identification of crack shaft misalignment for the rotor system
The ability of variational mode decomposition (VMD) to process nonstationary signals is first fully utilized to decompose the noisy signals into submodal functions with multiple frequency bands
Summary
As the core component of rotating machinery, the health status of a rotor system will directly affect the normal operation of the entire equipment; as a rotor system often operates in a high temperature and high pressure environment, stress concentration will appear in some parts of the rotating shaft and fatigue cracks may gradually occur after long operation times, due to the long-term effect of complex alternating loads at high speed. The recently proposed batch normalization layer is often used after the convolutional layer to speed up network convergence and avoid vanishing gradients [25], and it is necessary to process data through an activation function which can nonlinearly express the extracted features. The gradient descent method is adopted to minimize the loss function to adjust the weight parameters in network layer by layer, and the accuracy of the network is improved through frequent iterative training. The convolutional layer highlights two major characteristics of a CNN, namely sparse connection and weight sharing. Each kernel of the convolution layer is repeatedly applied to the whole receptive field to convolve the input data, and each kernel shares the same weights and biases to improve the training speed.
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