Gearbox Fault Diagnosis Based on Multi-Sensor Deep Spatiotemporal Feature Representation

Xie, Fei; Gan, Wang; Shang, Jiandong; Sun, Erxin; San-mao, Xie

doi:10.3390/math11122679

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…To demonstrate the superiority of the proposed MHSTA-PGRU in multi-sensor based mechanical fault diagnosis tasks, several data-driven methods, including support vector machine (SVM), MH1DCNN [33], 2D-CNN, PCNN_GRU [34], and BiGRU are used as comparative test models. MH1DCNN, which stands for multi-head 1D convolutional neural network, is a method that focuses on capturing dependencies and patterns in sequential data using a multi-head convolutional architecture.…”

Section: Experiments Settingsmentioning

confidence: 99%

Multi-head spatio-temporal attention based parallel GRU architecture: a novel multi-sensor fusion method for mechanical fault diagnosis

Li,

Luo,

Xie

et al. 2023

Meas. Sci. Technol.

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The sensor signals with multiple measuring points and data types not only bring sufficient information, but also put forward more stringent requirements for multi-sensor fusion efficiency and fault feature extraction capability. The redundancy and conflicts in the information of multi-sensor signals often hinder the accurate extraction of crucial fault features. To address this problem, our study proposes an intelligent mechanical fault diagnosis method, which is based on a multi-head spatio-temporal attention mechanism and parallel gated recurrent units (GRUs) architecture. This method utilizes multiple attention heads to model the correlation information in spatial and temporal dimensions, and employs a parallel GRU network for targeted feature extraction. Finally, it combines local features from different attention heads to achieve flexible scheduling of various spatio-temporal attention modes. This novel application and fusion approach of multi-head attention enables accurate identification of the spatio-temporal value differences in the collected multi-sensor signals from multiple perspectives. Experimental results on multiple mechanical fault datasets show that the proposed method performs well in multi-sensor signals based mechanical fault diagnosis tasks and can maintain effectiveness under small samples and imbalanced data conditions.

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Section: Experiments Settingsmentioning

confidence: 99%

Multi-head spatio-temporal attention based parallel GRU architecture: a novel multi-sensor fusion method for mechanical fault diagnosis

Li,

Luo,

Xie

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…This approach successfully fuses multi-sensor spatiotemporal data for comprehensive diagnosis. Supplementing these advancements, an intelligent strategy for diagnosing rolling bearing faults has been presented in [25]. It fuses multiple signals with a Morlet transform function-residual network (MTF-ResNet).…”

Section: Introductionmentioning

confidence: 99%

Fusion of Audio and Vibration Signals for Bearing Fault Diagnosis Based on a Quadratic Convolution Neural Network

Yan,

Liao,

Gao

et al. 2023

Sensors

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In this paper, a quadratic convolution neural network (QCNN) using both audio and vibration signals is utilized for bearing fault diagnosis. Specifically, to make use of multi-modal information for bearing fault diagnosis, the audio and vibration signals are first fused together using a 1 × 1 convolution. Then, a quadratic convolution neural network is applied for the fusion feature extraction. Finally, a decision module is designed for fault classification. The proposed method utilizes the complementary information of audio and vibration signals, and is insensitive to noise. The experimental results show that the accuracy of the proposed method can achieve high accuracies for both single and multiple bearing fault diagnosis in the noisy situations. Moreover, the combination of two-modal data helps improve the performance under all conditions.

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Richly connected spatial–temporal graph neural network for rotating machinery fault diagnosis with multi-sensor information fusion

Wang,

Wang

et al. 2025

Mechanical Systems and Signal Processing

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Gearbox Fault Diagnosis Based on Multi-Sensor Deep Spatiotemporal Feature Representation

Cited by 4 publications

References 42 publications

Multi-head spatio-temporal attention based parallel GRU architecture: a novel multi-sensor fusion method for mechanical fault diagnosis

Multi-head spatio-temporal attention based parallel GRU architecture: a novel multi-sensor fusion method for mechanical fault diagnosis

Fusion of Audio and Vibration Signals for Bearing Fault Diagnosis Based on a Quadratic Convolution Neural Network

Richly connected spatial–temporal graph neural network for rotating machinery fault diagnosis with multi-sensor information fusion

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