Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN
2. Construction of HMCVT Shift Hydraulic System Test Platform
3. Obtaining Test of Fault Sample of HMCVT Shift Hydraulic System
3.1. Typical Failure Type
- Normal mode (Fault Type 1): all of the parameters of the shift hydraulic system are in the normal working range, and there is no abnormality in the shifting process.
- The clutch piston is stuck (Fault Type 2): The clutch will always be in a different degree of slipping state. The lighter can change the segment but cannot load (the slip will stall as soon as it is loaded), and the heavy will directly burn the clutch.
- The seal ring at the rotary joint is damaged (Fault Type 3): Local internal leakage occurs in the oil passage, but this leakage occurs inside the oil passage. When the oil passage is filled for the first time, the oil pressure is difficult to establish. As the oil channel is filled with hydraulic oil, its influence on the establishment of the shift section hydraulic pressure is no longer significant, and this fault has an impact on the shift section quality of the transmission.
- The outlet oil passage of the governor valve is blocked (Fault Type 4): The oil filling flow of the clutch is reduced, and the sliding time between the main and driven friction plates is extended. This not only deteriorates the quality of the shift, but also increases the risk of power interruption and clutch burnout. This fault mode has a gradual characteristic and is not easy to detect.
- Leakage of the branch of the oil pipeline (Fault Type 5): the fault generally occurs at the place of the pipe joint of the branch oil pipeline, and a partial external leakage occurs in the oil pipeline.
3.2. Fault Simulation and Data Obtained
4. BP Method for FAULT Diagnosis of HMCVT Shift Hydraulic System
4.1. Fault Diagnosis of Shift Hydraulic System Based on BP Neural Network
4.2. Fault Diagnosis of Shift Hydraulic System Based on PSO-BP Neural Network
4.3. Fault Diagnosis of Shift Hydraulic System Based on BAS-BP Neural Network Model
5. CNN Method for Fault Diagnosis of HMCVT Shift Hydraulic System
5.1. Convolutional Neural Network Overview
5.1.1. Convolutional Layer
5.1.2. Pooling Layer
5.1.3. Fully Connected Layer
5.1.4. Convolutional Neural Network Structure
5.2. Attribute Reduction Based on Rough Set
5.3. Fault Diagnosis Results Based on CNN and Neighborhood Rough Set
- Various types of faults have greater separability after nonlinear transformation by the BP network, the overall similarity is low and the model fault classification effect is good.
- The optimized BP neural network model is better than the unoptimized BP neural network model for fault recognition. Its average diagnostic accuracy rate reached 92.5%.
- The BAS-BP neural network model has the strongest ability to identify the fault of the oil channel blockage fault T4, which is conducive to further analysis to determine and eliminate the fault.
- The experiment shows that the pressure data has the greatest influence on the recognition of the fault mode of the shift hydraulic system. It is feasible to use the pressure data of the hydraulic system as the only input parameter to identify the fault mode of the HMCVT shift hydraulic system.
- The convolutional neural network under the same test conditions is significantly better than the shallow optimized BP neural network in the application of the fault diagnosis of the shift hydraulic system, and its fault diagnosis accuracy can reach 97.5%.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
- Lei, X.; Cai, Z.; Zhang, M.; Ma, W. Analyses of Transmission Error of Mechanical Shift Gear in HMCVT. China Mech. Eng. 2015, 26, 3253–3259. [Google Scholar]
- Xia, Y.; Sun, D. Characteristic analysis on a new hydro-mechanical continuously variable transmission system. Mech. Mach. Theory 2018, 126, 457–467. [Google Scholar] [CrossRef]
- Geng, G.; Zhou, K.; Xiao, M.; Zhang, H.; Wang, G. Research on starting process of hydro-mechanical continuously variable transmission for tractor. J. Nanjing Agric. Univ. 2016, 39, 332–340. [Google Scholar]
- Macor, A.; Rossetti, A. Optimization of hydro-mechanical power split transmissions. Mech. Mach. Theory 2011, 46, 1901–1919. [Google Scholar] [CrossRef]
- Hu, J.B.; Wei, C.; Du, J.Y. A Study on the Speed Ratio Follow-Up Control System of Hydro-Mechanical Transmission. Trans. Beijing Inst. Technol. 2008, 28, 481–485. [Google Scholar]
- Wang, G.; Song, Y.; Wang, J.; Xiao, M.; Cao, Y.; Chen, W.; Wang, J. Shift quality of tractors fitted with hydrostatic power split CVT during starting. Biosyst. Eng. 2020, 196, 183–201. [Google Scholar] [CrossRef]
- Kim, D.C.; Kim, K.U.; Park, Y.J.; Huh, J.Y. Analysis of shifting performance of power shuttle transmission. J. Terramech. 2007, 44, 111–122. [Google Scholar] [CrossRef]
- Zhang, X.; Tai, J.; Wang, G.; Zhang, H.; Wang, C.; Zhong, C. Hydraulic fault diagnosis of hydro-mechanical continuously variable transmission in shift based on BP method. J. Chin. Agric. Mech. 2016, 37, 133–139. [Google Scholar] [CrossRef]
- Ma, B. Study on Hydraulic System Fault Diagnosis of Hydro-Mechanical Continuously Variable Transmission. Nanjing Agricultural University, Nanjing, China, 2015. [Google Scholar]
- Xue, L.; Jiang, H.; Zhao, Y.; Wang, J.; Wang, G.; Xiao, M. Fault diagnosis of wet clutch control system of tractor hydrostatic power split continuously variable transmission. Comput. Electron. Agric. 2022, 194, 106778. [Google Scholar] [CrossRef]
- Li, Y.; Liang, X.; Xu, M.; Huang, W. Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform. Mech. Syst. Signal Process. 2017, 86, 204–223. [Google Scholar] [CrossRef]
- Zhang, X.; Zhao, J.; Li, H.; Ni, X.; Sun, F. Compound fault diagnosis for gearbox based on NIC-DWT-WOASVM. J. Vib. Shock. 2020, 39, 146–151+164. [Google Scholar]
- Xia, L. Research on Fault Diagnosis and Related Algorithm Based on Hidden Markov Model. Ph.D. thesis, Huazhong University of Science and Technology, Wuhan, China, 2014. [Google Scholar]
- Wang, G.; Zhang, X.; Zhu, S.; Zhang, H.; Shi, L.; Zhong, C. Hydraulic failure diagnosis of tractor hydro-mechanical continuously variable transmission in shifting process. Trans. Chin. Soc. Agric. Eng. 2015, 31, 25–34. [Google Scholar]
- Li, C.; Sanchez, R.-V.; Zurita, G.; Cerrada, M.; Cabrera, D.; Vásquez, R.E. Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing 2015, 168, 119–127. [Google Scholar] [CrossRef]
- Lin, R.; Ming, T.; Wu, J. Leakage Faults Detection and Isolation for Electro-hydraulic Servo System. Mach. Tool Hydraul. 2012, 40, 162–166+169. [Google Scholar]
- Han, Z. Research on Hydraulic System Fault Diagnosis Method of Rack-Rail Car. M.D. thesis, Dalian University of Technology, Dalian, China, 2013. [Google Scholar]
- Zou, L. Research on Shield Machine Fault Diagnosis Method Based on SOM-BP Neural Network. M.D. thesis, Xi’an University of Technology, Xi’an, China, 2018. [Google Scholar]
- Xin, H. Research on Switch Fault Diagnosis System Based on PSO-BP. M.D. thesis, Lanzhou Jiaotong University, Lanzhou, China, 2020. [Google Scholar]
- Imtiaz, T.; Khan, B.H.; Khanam, N. Fast and improved PSO (FIPSO)-based deterministic and adaptive MPPT technique under partial shading conditions. IET Renew. Power Gener. 2020, 14, 3164–3171. [Google Scholar] [CrossRef]
- Peng-cheng, Y.; Song-hang, S.; Chao-yin, Z.; Xiao-fei, Z. Research on classification of coal mine water source by improved BP neural network algorithm. Spectrosc. Spectr. Anal. 2021, 41, 2288–2293. [Google Scholar]
- Jarusek, R.; Volna, E.; Kotyrba, M. Photomontage detection using steganography technique based on a neural network. Neural Netw. 2019, 116, 150–165. [Google Scholar] [CrossRef]
- Su, L.; Zha, Z.; Lu, X.; Shi, T.; Liao, G. Using BP network for ultrasonic inspection of flip chip solder joints. Mech. Syst. Signal Process. 2013, 34, 183–190. [Google Scholar] [CrossRef]
- Song, Z.; Geng, D.; Su, C.; Liu, Y. Vibration prediction of a hydro-power house base on IFA-BPNN. J. Vib. Shock. 2017, 36, 64–69. [Google Scholar]
- Gong, W.F.; Chen, H.; Zhang, Z.H.; Zhang, M.L.; Guan, C.; Wang, X. Intelligent fault diagnosis for rolling bearing based on improved convolutional neural network. J. Vib. Eng. 2020, 33, 400–413. [Google Scholar]
- Chandrasekhar, V.; Lin, J.; Morère, O.; Goh, H.; Veillard, A. A practical guide to CNNs and Fisher Vectors for image instance retrieval. Signal Process. 2016, 128, 426–439. [Google Scholar] [CrossRef]
- Jing, Y.; Sun, A.; Zhang, M. Fault diagnosis of three-phase bridge rectifier circuit based on particle swarm optimization. J. Comput. Appl. 2019, 39, 60–64. [Google Scholar]
- Jiang, X.; Li, S. BAS: Beetle Antennae Search Algorithm for Optimization Problems. Int. J. Robot. Control 2018, 1, 1–5. [Google Scholar] [CrossRef]
- Qu, J.; Yu, L.; Yuan, T.; Tian, Y.; Gao, F. A hierarchical intelligent fault diagnosis algorithm based on convolutional neural network. Control Decis. 2019, 34, 2619–2626. [Google Scholar]
- Pan, R.; Wang, X.; Yi, C.; Zhang, Z.; Fan, Y.; Bao, W. Multi-objective optimization method for thresholds learning and neighborhood computing in a neighborhood based decision-theoretic rough set model. Neurocomputing 2017, 266, 619–630. [Google Scholar] [CrossRef]
- Zhang, N. Research of Attribute Reduction Algorithm Based on Neighborhood Rough Set. M.D. thesis, Hunan University, Changsha, China, 2017. [Google Scholar]
- Deng, D.; Li, Y.; Huang, H. Concept Drift and Attribute Reduction From the Viewpoint of F-rough Sets. Acta Autom. Sin. 2018, 44, 1781–1789. [Google Scholar]
- Yao, S.; Xu, F.; Wu, Z.; Chen, J.; Wang, J.; Wang, W. Non-monotonic attribute reduction based on neighborhood rough mutual information entropy. Control Decis. 2019, 34, 353–361. [Google Scholar]
- Hu, Q.; Yu, D.; Xie, Z. Numerical Attribute Reduction Based on Neighborhood Granulation and Rough Approximation. J. Softw. 2008, 19, 640–649. [Google Scholar] [CrossRef]
|Number||Part Name||Model||Main Performance Parameters|
|1||Asynchronous motor||JO2-22-4||Rated speed: 1450 r/min|
|2||Vane pump||YB1-6.3||Displacement: 6.3 mL/r|
|3||Relief valve||2FRM5/10QB||Adjustment range: 0~10 L/min|
|4||Pressure sensor||NS-F||Measuring range: 0~10 MPa|
|Output signal: 0~5 V|
|5||Flow sensors||LWGB-4 Turbine flow sensor||Measuring range: 0~0.4 m3/h|
|Output signal: 4~20 mA|
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Wang, J.; Lu, Z.; Wang, G.; Hussain, G.; Zhao, S.; Zhang, H.; Xiao, M. Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN. Agriculture 2023, 13, 461. https://doi.org/10.3390/agriculture13020461
Wang J, Lu Z, Wang G, Hussain G, Zhao S, Zhang H, Xiao M. Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN. Agriculture. 2023; 13(2):461. https://doi.org/10.3390/agriculture13020461Chicago/Turabian Style
Wang, Jiabo, Zhixiong Lu, Guangming Wang, Ghulam Hussain, Shanhu Zhao, Haijun Zhang, and Maohua Xiao. 2023. "Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN" Agriculture 13, no. 2: 461. https://doi.org/10.3390/agriculture13020461