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

Fengyun Xie (), Gan Wang, Jiandong Shang, Enguang Sun and Sanmao Xie
Additional contact information
Fengyun Xie: School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Gan Wang: School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Jiandong Shang: School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Enguang Sun: School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Sanmao Xie: School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China

Mathematics, 2023, vol. 11, issue 12, 1-19

Abstract: 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.

Keywords: gearbox; multi-sensor fusion; convolutional neural network; gated recurrent unit; spatiotemporal features (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/12/2679/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/12/2679/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:12:p:2679-:d:1169893

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().