A fault diagnosis method of rolling element bearing based on improved PSO and BP neural network

Song, Xudong; Wang, Hao; Liu, Yifan; Wang, Zi; Cui, Yunxian

doi:10.3233/jifs-213485

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…Ertunc et al (2020) established a multi-stage decision management model for bearing health management based on the ANN model. Song et al (2022) used particle swarm optimization algorithm to optimize the neural network and established a BP neural network (PSO-BP) fault rate prediction model, but there was a problem of slightly slower timeliness. Yang et al (2019) proposed a data-driven prediction model that combines state monitoring data and Elman neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Bearing fault detection method based on machine learning

Liu,

Xu,

2024

Preprint

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Traditional fault diagnosis for bearing usually requires a large amount of labeled data for training and deliberate selection of features and the diagnostic results are often too scattered to provide a thorough fault diagnosis procedure. To address these issues, we use data mining techniques to analyze the raw vibration signals of rolling bearings in various fault states under different operating conditions and construct labeled training and test datasets. By introducing the attention mechanism, we utilize convolutional neural networks and bidirectional long and short-term memory prediction models to diagnose deep fault categories and improve prediction accuracy from the perspective of machine learning. In addition, to intuitively describe the operating state of equipment bearings, we construct a visualization interface based on the prediction model. After example analysis, the model constructed in this paper has certain feasibility and effectiveness.

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Section: Literature Reviewmentioning

confidence: 99%

Bearing fault detection method based on machine learning

Liu,

Xu,

2024

Preprint

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“…It can be clearly seen from Figure 11 that there is no modal aliasing phenomenon between the spectra of each component, which can be well used to describe the fault characteristics. In order to highlight the decomposition effect, a comparison was made with the decomposition effect of the artificially set parameter combination of [K, α]= [4,2000] in reference 36 . The decomposition results of the parameter combination of [4,2000] are shown in Figure 12.…”

Section: ) Vmd Parameter Optimization Testmentioning

confidence: 99%

“…In order to highlight the decomposition effect, a comparison was made with the decomposition effect of the artificially set parameter combination of [K, α]= [4,2000] in reference 36 . The decomposition results of the parameter combination of [4,2000] are shown in Figure 12. By comparison, it can be seen that the optimized parameters in this article have an additional IMF4 component in the decomposition results.…”

Section: ) Vmd Parameter Optimization Testmentioning

confidence: 99%

“…X. Song et al 4 used the empirical mode decomposition (EMD) algorithm to decompose the original bearing fault signal into multiple modal components, and extracted the time-frequency domain features of the signal to construct a feature matrix. D. Meng et al 5 analyzed the natural frequency and characteristic frequency of bearing fault signals, and extracted the features of fault signals based on the EMD algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of VMD-CNN-BiLSTM Rolling Bearing Fault Diagnosis Model Based on Improved DBO Algorithm

Sun,

Wang,

You

et al. 2024

Preprint

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Rolling bearings are important components in mechanical equipment, but they are also a component with a high failure rate. Once a malfunction occurs, it will cause mechanical equipment to malfunction and may even affect personnel safety. Therefore, studying the fault diagnosis methods for rolling bearings is of great significance and is also a current research hotspot and frontier. However, the vibration signals of rolling bearings usually exhibit nonlinear and non-stationary characteristics, and are easily affected by industrial environmental noise, making it difficult to accurately diagnose bearing faults. Therefore, this article proposes a rolling bearing fault diagnosis model based on an improved dung beetle optimizer algorithm(DBO) optimized variational mode decomposition-convolutional neural network-bidirectional long short-term memory(VMD-CNN-BiLSTM). Firstly, an improved DBO algorithm named CSADBO is proposed by integrating multiple strategies such as chaotic mapping and cooperative search; Secondly, the optimal parameter combination of VMD was adaptively determined through the CSADBO algorithm, and the optimized VMD algorithm was used to perform modal decomposition on the bearing vibration signal; Then, CNN-BiLSTM was used as the model for fault classification, and the hyperparameters of the model were optimized using the CSADBO algorithm; Finally, multiple experiments were conducted on the bearing dataset of Case Western Reserve University, and the proposed method achieved an average diagnostic accuracy of 99.6%. Experimental comparisons were made with other models to verify the effectiveness of the proposed model. The experimental results show that the proposed model based on improved DBO algorithm optimized VMD-CNN-BiLSTM can effectively be used for rolling bearing fault diagnosis, with high diagnostic accuracy, and can provide theoretical reference for other related fault diagnosis problems.

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