Feature Extraction and Diagnosis of Power-Shift System Faults in Unmanned Hydro-Mechanical Transmission Tractors
To enhance the reliability of unmanned hydro-mechanical transmission tractors, a fault diagnosis method for their power-shift system was developed. First, fault types were identified, and sample data was collected via a test bench. Next, a feature extraction method for data dimensionality reduction...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/13/7/586 |
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| Summary: | To enhance the reliability of unmanned hydro-mechanical transmission tractors, a fault diagnosis method for their power-shift system was developed. First, fault types were identified, and sample data was collected via a test bench. Next, a feature extraction method for data dimensionality reduction and a deep learning network called W_SCBAM were introduced for fault diagnosis. Both W_SCBAM and conventional algorithms were trained 20 times, and their performance was compared. Further testing of W_SCBAM was conducted in various application scenarios. The results indicate that the feature extraction method reduces the sample length from 46 to 3. The fault diagnosis accuracy of W_SCBAM for the radial-inlet clutch system has an expectation of 98.5% and a variance of 1.6%, respectively, outperforming other algorithms. W_SCBAM also excels in diagnosing faults in the axial-inlet clutch system, achieving 97.6% accuracy even with environmental noise. Unlike traditional methods, this study integrates the update of a dimensionality reduction matrix into network parameter training, achieving high-precision classification with minimal input data and lightweight network structure, ensuring reliable data transmission and real-time fault diagnosis of unmanned tractors. |
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| ISSN: | 2075-1702 |