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

The vibration signal acquired by a single sensor contains limited information and is easily interfered by noise signals, resulting in the inability to fully express the operating characteristics and state of a gearbox. To address this problem, our study proposes a gearbox fault diagnosis method base...

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Bibliographic Details
Published inMathematics (Basel) Vol. 11; no. 12; p. 2679
Main Authors Xie, Fengyun, Wang, Gan, Shang, Jiandong, Sun, Enguang, Xie, Sanmao
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2023
Subjects
Accuracy
Analysis
Artificial intelligence
Artificial neural networks
Classification
convolutional neural network
Data collection
Deep learning
Fault diagnosis
gated recurrent unit
gearbox
Gearboxes
Machine learning
multi-sensor fusion
Neural networks
Pattern recognition
Position sensing
Representations
Sensors
Signal processing
Spatial data
spatiotemporal features
Vibration
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Summary:The vibration signal acquired by a single sensor contains limited information and is easily interfered by noise signals, resulting in the inability to fully express the operating characteristics and state of a gearbox. To address this problem, our study proposes a gearbox fault diagnosis method based on multi-sensor deep spatiotemporal feature representation. This method utilizes two vibration sensors to obtain the vibration information of the gearbox. A fault diagnosis model (PCNN–GRU) combined with a parallel convolutional neural network (PCNN) and gated recurrent unit (GRU) was used to fuse the gearbox vibration information. The parallel convolutional neural network was used to extract the spatial information of the vibration signals collected by different position sensors, and the timing information was mined through the gated recurrent unit. The deep spatiotemporal features that fuse the multi-sensor spatial and temporal information were composed. The collected multi-sensor vibration signals were directly input into the PCNN–GRU model, and an end-to-end intelligent diagnosis of the gearbox faults was realized. Finally, through experimental verification, the accuracy rate of this model can reach up to 99.92%. Compared with other models, this model has a higher diagnostic accuracy and stability.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11122679