Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network

Abstract

The quality of rolling bearings is vital for the working state and rotation accuracy of the shaft. Timely and accurately acquiring bearing status and early fault diagnosis can effectively prevent losses, making it highly practical. To improve the accuracy of bearing fault diagnosis, this paper proposes a CNN-LSTM bearing fault diagnosis model optimized by hybrid particle swarm optimization (HPSO). The HPSO algorithm has a strong global optimization ability and can effectively solve nonlinear and multivariate optimization problems. It is used to optimize and match the parameters of the CNN-LSTM model and dynamically find the optimal value of the parameters. This model overcomes the problem that the parameters of the CNN-LSTM model depend on empirical settings and cannot be adjusted dynamically. This model is used for bearing fault diagnosis, and the accuracy rate of fault diagnosis classification reaches 99.2%. Compared with the traditional CNN, LSTM, and CNN-LSTM models, the accuracy rates are increased by 6.6%, 9.2%, and 5%, respectively. At the same time, comparing the models with different optimization parameters shows that the model proposed in this paper has the highest accuracy. The experimental results verified the superiority of the HPSO algorithm to optimize model parameters and the feasibility and accuracy of the HPSO-CNN-LSTM model for bearing fault diagnosis.

Deep Learning-Enhanced Small-Sample Bearing Fault Analysis Using Q-Transform and HOG Image Features in a GRU-XAI Framework

“…Conventional RNNs propagate a single hidden state over time, which presents challenges for the network in learning longterm dependencies. The models tackle this issue by incorporating a memory cell, which acts as a receptacle capable of retaining information over a prolonged period of time [32]. The control mechanism of the memory cell consists of three distinct gates, namely the input gate, the forget gate, and the output gate.…”

Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment

“…In order to convert 1D data with length of non-square number m into 2D data with target size, multiply S input with size (1, m) by a weight matrix with size (m, n), the input data can be converted into 1D data with length of square number n, and the result can be corrected with deviation, as shown in Equation (11).…”

“…Huang et al [10] proposed a rolling bearing fault-detection method based on an improved Gray Wolf algorithm to optimize multi-stable stochastic resonance parameters, and conducted experimental verification using the published experimental data sets of CWRU and MFPT. Tian et al [11] proposed a CNN-LSTM bearing fault diagnosis model based on hybrid particle swarm optimization, and conducted experimental verification using the experimental data set disclosed by CWRU. However, most of the research objects of these research results are rolling bearings, and the model test data are almost all derived from laboratory bench test data.…”

Adversarial training of multi-scale channel attention network for enhanced robustness in bearing fault diagnosis

“…This escalation makes it challenging to extract fault feature patterns based on empirically designed methods. With the recent advancements in artificial intelligence technologies, deep learning techniques based on CNNs [3,4], RNNs [5,6], DBNs [7,8], etc have found widespread application in the field of fault diagnosis. These methods have the capability to automatically learn highorder features related to faults from complex signals, demonstrating accuracy levels far superior to traditional approaches [9,10].…”