Motor bearing fault diagnosis based on multi-feature fusion and PSO-BP

Zhang, Yi; Qu, Jianfeng; Fang, Xinsheng; Luo, Guojian

doi:10.1109/scems52239.2021.9646168

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…The performance of a backpropagation neural network is often highly sensitive to the initial weight setting. Inappropriate initial weights may lead to the network converging to suboptimal solutions or failing to converge altogether [17]. To address this issue, this paper utilizes an enhanced IPSO algorithm to optimize the weights and biases of neurons in the BP network.…”

Section: The Improved Particle Swarm Optimization Algorithm Used In T...mentioning

confidence: 99%

Power Transformer Fault Diagnosis Based on Feature Selection and Improved IPSO-BP-AdaBoost

Zhou,

Fu,

et al. 2024

Preprint

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This paper proposes a novel fault diagnosis methodology for oil-immersed transformers to improve the diagnostic accuracy influenced by gas components in power transformer oil. Firstly, the Random Forest (RF) algorithm is utilized to evaluate and filter the raw data features. Secondly, a multi-strategy Improved Particle Swarm Optimization (IPSO) is applied to optimize a double-hidden layer Backpropagation Neural Network (BPNN). Four enhancement strategies, including SPM chaos mapping based on opposition-based learning, adaptive weight, spiral flight search, and crisscross strategies, are introduced based on traditional Particle Swarm Optimization (PSO) to enhance the model’s optimization capabilities. Lastly, AdaBoost is integrated to fortify the resilience of the IPSO-BP network. Ablation experiments demonstrate an enhanced convergence rate and model accuracy of IPSO. Case analysis using Dissolved Gas Analysis (DGA) samples compares the proposed IPSO-BP-AdaBoost model with other swarm intelligence optimization algorithms integrated with BPNN. The experimental findings highlight the superior diagnostic accuracy and classification performance of the IPSO-BP-AdaBoost model.

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Section: The Improved Particle Swarm Optimization Algorithm Used In T...mentioning

confidence: 99%

Power Transformer Fault Diagnosis Based on Feature Selection and Improved IPSO-BP-AdaBoost

Zhou,

Fu,

et al. 2024

Preprint

View full text Add to dashboard Cite

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Intelligent Fault Diagnosis of Inter-Turn Short Circuit Faults in PMSMs for Agricultural Machinery Based on Data Fusion and Bayesian Optimization

Wang,

Lai,

Zhang

et al. 2024

Agriculture

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The permanent magnet synchronous motor (PMSM) plays an important role in the power system of agricultural machinery. Inter-turn short circuit (ITSC) faults are among the most common failures in PMSMs, and early diagnosis of these faults is crucial for enhancing the safety and reliability of motor operation. In this article, a multi-source data-fusion algorithm based on convolutional neural networks (CNNs) has been proposed for the early fault diagnosis of ITSCs. The contributions of this paper can be summarized in three main aspects. Firstly, synchronizing data from different signals extracted by different devices presents a significant challenge. To address this, a signal synchronization method based on maximum cross-correlation is proposed to construct a synchronized dataset of current and vibration signals. Secondly, applying a traditional CNN to the data fusion of different signals is challenging. To solve this problem, a multi-stream high-level feature fusion algorithm based on a channel attention mechanism is proposed. Thirdly, to tackle the issue of hyperparameter tuning in deep learning models, a hyperparameter optimization method based on Bayesian optimization is proposed. Experiments are conducted based on the derived early-stage ITSC fault-severity indicator, validating the effectiveness of the proposed fault-diagnosis algorithm.

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Power Transformer Fault Diagnosis Based on Random Forest and Improved Particle Swarm Optimization–Backpropagation–AdaBoost

Zhou,

Fu,

et al. 2024

Electronics

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This paper proposes a novel fault diagnosis methodology for oil-immersed transformers to improve the diagnostic accuracy influenced by gas components in power transformer oil. Firstly, the Random Forest (RF) algorithm is utilized to evaluate and filter the raw data features, solving the problem of determining significant features in the dataset. Secondly, a multi-strategy Improved Particle Swarm Optimization (IPSO) is applied to optimize a double-hidden layer backpropagation neural network (BPNN), which overcomes the challenge of determining hyperparameters in the model. Four enhancement strategies, including SPM chaos mapping based on opposition-based learning, adaptive weight, spiral flight search, and crisscross strategies, are introduced based on traditional Particle Swarm Optimization (PSO) to enhance the model’s optimization capabilities. Lastly, AdaBoost is integrated to fortify the resilience of the IPSO-BP network. Ablation experiments demonstrate an enhanced convergence rate and model accuracy of IPSO. Case analysis using Dissolved Gas Analysis (DGA) samples compares the proposed IPSO–BP–AdaBoost model with other swarm intelligence optimization algorithms integrated with BPNN. The experimental findings highlight the superior diagnostic accuracy and classification performance of the IPSO–BP–AdaBoost model.

show abstract

Motor bearing fault diagnosis based on multi-feature fusion and PSO-BP

Cited by 3 publications

References 8 publications

Power Transformer Fault Diagnosis Based on Feature Selection and Improved IPSO-BP-AdaBoost

Power Transformer Fault Diagnosis Based on Feature Selection and Improved IPSO-BP-AdaBoost

Intelligent Fault Diagnosis of Inter-Turn Short Circuit Faults in PMSMs for Agricultural Machinery Based on Data Fusion and Bayesian Optimization

Power Transformer Fault Diagnosis Based on Random Forest and Improved Particle Swarm Optimization–Backpropagation–AdaBoost

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