Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network

Raouf, Izaz; Kumar, Prashant; Soo Kim, Heung

doi:10.1016/j.eswa.2024.125137

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2024.125137

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Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network

Izaz Raouf,

Prashant Kumar,

Heung Soo Kim

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Cited by 7 publications

References 41 publications

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Enhanced fault diagnosis and remaining useful life prediction of rolling bearings using a hybrid multilayer perceptron and LSTM network model

Bharatheedasan,

Maity,

Kumaraswamidhas

et al. 2025

Alexandria Engineering Journal

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Enhanced fault diagnosis and remaining useful life prediction of rolling bearings using a hybrid multilayer perceptron and LSTM network model

Bharatheedasan,

Maity,

Kumaraswamidhas

et al. 2025

Alexandria Engineering Journal

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End-to-End Intelligent Fault Diagnosis of Transmission Bearings in Electric Vehicles Based on CNN

Chen,

Li,

et al. 2024

Lubricants

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Environmental noise and transmission components can cause significant interference in vibration signals, rendering the extraction of bearing fault features challenging in service scenarios. Traditional fault diagnosis methods rely heavily on professional domain knowledge, prior models, and signal preprocessing methods. The accuracy of fault diagnosis depends on the quality of the fault-sensitive features extracted by vibration signal preprocessing methods. An improved convolutional neural network (CNN) end-to-end intelligent fault diagnosis model based on raw vibration data (RVDCNN) is proposed. The time-domain vibration signal of the transmission bearing is converted into a continuous two-dimensional numerical matrix, and a two-dimensional CNN model is constructed through network structure optimization. The original time-domain vibration signal numerical matrix of the bearing is trained and tested to extract and learn abstract fault features of different fault types, and then the fault classification of the bearing is achieved. To verify the generalizability of the RVDCNN intelligent fault diagnosis model, it is applied to the recognition of rolling bearings in the two-speed mechanical automatic transmission of electric vehicles, achieving recognition accuracy of 99.11% for seven types of bearings.

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Fault Detection in Harmonic Drive Using Multi-Sensor Data Fusion and Gravitational Search Algorithm

Hsieh,

2024

Machines

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This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, which can compromise system stability and production efficiency. To enhance diagnostic accuracy, the research employs wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) to extract multi-scale features from vibration signals. These features are subsequently fused, and GSA is used to optimize the high-dimensional fused features, eliminating redundant data and mitigating overfitting. The optimized features are then input into a support vector machine (SVM) for fault classification, with K-fold cross-validation used to assess the model’s generalization capabilities. Experimental results demonstrate that the proposed diagnosis method, which integrates multi-sensor data fusion with GSA optimization, significantly improves fault diagnosis accuracy compared to methods using single-sensor signals or unoptimized features. This improvement is particularly notable in multi-class fault scenarios. Additionally, GSA’s global search capability effectively addresses overfitting issues caused by high-dimensional data, resulting in a diagnostic model with greater reliability and accuracy across various fault conditions.

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Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network

Cited by 7 publications

References 41 publications

Enhanced fault diagnosis and remaining useful life prediction of rolling bearings using a hybrid multilayer perceptron and LSTM network model

Enhanced fault diagnosis and remaining useful life prediction of rolling bearings using a hybrid multilayer perceptron and LSTM network model

End-to-End Intelligent Fault Diagnosis of Transmission Bearings in Electric Vehicles Based on CNN

Fault Detection in Harmonic Drive Using Multi-Sensor Data Fusion and Gravitational Search Algorithm

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