An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems. (7th January 2023)
- Record Type:
- Journal Article
- Title:
- An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems. (7th January 2023)
- Main Title:
- An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems
- Authors:
- Sun, Xianbin
Wang, Mohan
Zhan, Bo
Xiong, Yuanyuan
Yu, Wei - Other Names:
- Batista Antonio Academic Editor.
- Abstract:
- Abstract : In the actual environment, there are difficult points such as complicated mechanical system fault types, random fault locations, and inconspicuous minor fault signals, which make it difficult to accurately diagnose faults. This paper proposes a new method for fault diagnosis of an adaptive multisensor bearing-gear system based on GAF/MTF (Gramian angular fields and Markov transition fields) and ResNet (deep residual network). First, we establish a multisensor signal acquisition system to monitor the running signals of the bearing-gearbox composite test bench in real time. Faulty parts include multiple types of composite faults of different sizes, different fault types, and different transmission stages. Second, based on GAF/MTFs, the multichannel timing signal collected by using the acquisition system is converted into multichannel pictures, and pictures are fused and compressed into three-channel pictures. Finally, we input these pictures into ResNet for fault diagnosis. The experimental results show that the GAF/MTF-ResNet model has a recognition accuracy of 72.14% for a total of 520 classification label test sets under different motor speeds, different sampling times, and different types of faults. Among them, the accuracy of the motor speed and sampling time is close to 100%, and the accuracy of gearbox failure and bearing failure is 75.25% and 88.97%, respectively. This shows that the method provides new ideas for the composite fault diagnosis of mechanicalAbstract : In the actual environment, there are difficult points such as complicated mechanical system fault types, random fault locations, and inconspicuous minor fault signals, which make it difficult to accurately diagnose faults. This paper proposes a new method for fault diagnosis of an adaptive multisensor bearing-gear system based on GAF/MTF (Gramian angular fields and Markov transition fields) and ResNet (deep residual network). First, we establish a multisensor signal acquisition system to monitor the running signals of the bearing-gearbox composite test bench in real time. Faulty parts include multiple types of composite faults of different sizes, different fault types, and different transmission stages. Second, based on GAF/MTFs, the multichannel timing signal collected by using the acquisition system is converted into multichannel pictures, and pictures are fused and compressed into three-channel pictures. Finally, we input these pictures into ResNet for fault diagnosis. The experimental results show that the GAF/MTF-ResNet model has a recognition accuracy of 72.14% for a total of 520 classification label test sets under different motor speeds, different sampling times, and different types of faults. Among them, the accuracy of the motor speed and sampling time is close to 100%, and the accuracy of gearbox failure and bearing failure is 75.25% and 88.97%, respectively. This shows that the method provides new ideas for the composite fault diagnosis of mechanical systems under different working conditions and different types of faults and has theoretical guiding significance. … (more)
- Is Part Of:
- Shock and vibration. Volume 2023(2023)
- Journal:
- Shock and vibration
- Issue:
- Volume 2023(2023)
- Issue Display:
- Volume 2023, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 2023
- Issue:
- 2023
- Issue Sort Value:
- 2023-2023-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-01-07
- Subjects:
- Shock (Mechanics) -- Periodicals
Vibration -- Periodicals
534.5 - Journal URLs:
- https://www.hindawi.com/journals/sv/ ↗
- DOI:
- 10.1155/2023/6928871 ↗
- Languages:
- English
- ISSNs:
- 1070-9622
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 25127.xml