Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE

Wang, Zhen; Wang, Shuaiyu; Cheng, Yiyang

doi:10.3390/app14010289

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…If the dung beetle encounters an obstacle and cannot move normally, it needs to simulate a dance behavior to change its direction of movement. A tangent function is used to obtain a new direction of movement, and the position update is shown in Formula (14).…”

Section: Dung Beetle Optimization Algorithmmentioning

confidence: 99%

“…M. Wang et al 13 optimized VMD parameters based on sparrow search algorithm and combined them with support vector machine (SVM) to diagnose bearing fault signals, achieving good results. Z. Wang et al 14 proposed an improved DBO algorithm that enhances the global search ability of the algorithm and applied it to optimize the VMD algorithm. Experimental results have shown that the optimized VMD algorithm can effectively perform modal decomposition on fault signals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Dung Beetle Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Sun,

Wang,

You

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

MIVNDN: Ultra-Short-Term Wind Power Prediction Method with MSDBO-ICEEMDAN-VMD-Nons-DCTransformer Net

Zhuang,

Gao,

Zhang

et al. 2024

Electronics

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Wind speed, wind direction, humidity, temperature, altitude, and other factors affect wind power generation, and the uncertainty and instability of the above factors bring challenges to the regulation and control of wind power generation, which requires flexible management and scheduling strategies. Therefore, it is crucial to improve the accuracy of ultra-short-term wind power prediction. To solve this problem, this paper proposes an ultra-short-term wind power prediction method with MIVNDN. Firstly, the Spearman’s and Kendall’s correlation coefficients are integrated to select the appropriate features. Secondly, the multi-strategy dung beetle optimization algorithm (MSDBO) is used to optimize the parameter combinations in the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, and the optimized decomposition method is used to decompose the historical wind power sequence to obtain a series of intrinsic modal function (IMF) components with different frequency ranges. Then, the high-frequency band IMF components and low-frequency band IMF components are reconstructed using the t-mean test and sample entropy, and the reconstructed high-frequency IMF component is decomposed quadratically using the variational modal decomposition (VMD) to obtain a new set of IMF components. Finally, the Nons-Transformer model is improved by adding dilated causal convolution to its encoder, and the new set of IMF components, as well as the unreconstructed mid-frequency band IMF components and the reconstructed low-frequency IMF, component are used as inputs to the model to obtain the prediction results and perform error analysis. The experimental results show that our proposed model outperforms other single and combined models.

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Optimization of Variational Mode Decomposition-Convolutional Neural Network-Bidirectional Long Short Term Memory Rolling Bearing Fault Diagnosis Model Based on Improved Dung Beetle Optimizer Algorithm

Sun,

Wang,

You

et al. 2024

Lubricants

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(1) Background: Rolling bearings are important components in mechanical equipment, but they are also components 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. (2) Methods: Therefore, this article proposes a rolling bearing fault diagnosis model based on an improved dung beetle optimizer (DBO) algorithm-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 hyperparameters of the model were optimized using the CSADBO algorithm. (3) Results: 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%. (4) Conclusions: 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 an improved DBO algorithm optimized VMD-CNN-BiLSTM can effectively be used for rolling bearing fault diagnosis, with high diagnostic accuracy, and can provide a theoretical reference for other related fault diagnosis problems.

show abstract

Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE

Cited by 3 publications

References 18 publications

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

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

MIVNDN: Ultra-Short-Term Wind Power Prediction Method with MSDBO-ICEEMDAN-VMD-Nons-DCTransformer Net

Optimization of Variational Mode Decomposition-Convolutional Neural Network-Bidirectional Long Short Term Memory Rolling Bearing Fault Diagnosis Model Based on Improved Dung Beetle Optimizer Algorithm

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