Multi-level health degree analysis of vehicle transmission system based on PSO-BP neural network data fusion

• Autorzy: Wu Jianpeng , Cui Jiahao , Shu Yuechao , Wang Yuxin , Chen Ruihan , Wang Liyong

Identyfikatory

DOI

10.17531/ein.2023.1.4

• Języki publikacji: EN

Abstrakty

EN

In order to realize the evaluation of the vehicle transmission system health degree, a prediction model by multi-level data fusion method is established in this paper. The prediction model applies PSO(Particle Swarm Optimization)-BP(Back Propagation) neural network algorithm, calculates the whole machine health degree and each module respective weights from the test data. On this basis, it analyzes the error between the model calculated health degree and theoretical health degree. Then the research verifies the validity and prediction model accuracy. The health degree which is obtained by the single module feature parameters fusion, and the vehicle transmission system health degree is investigated, which is less effective compared to the three-level fusions. After that, by analyzing the vehicle transmission system multi-parameter feature weights, it is found that the mechanical module accounted for the largest damage rate, and the three modules influenced the vehicle transmission system health degree in the order of mechanical module, hydraulic module, and electric control module. The study has played a guiding role in the health management of complex equipment.

Słowa kluczowe

EN

vehicle transmission system; data fusion; PSO-BP algorithm; health degree

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2023
• Tom: Vol. 25, no. 1
• Strony: art. no. 4
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Wu Jianpeng

• Beijing Information Science and Technology University, China

• https://orcid.org/0000-0002-6360-2047

autor

Cui Jiahao

• Beijing Information Science and Technology University, China

• https://orcid.org/0000-0002-8459-6192

autor

Shu Yuechao

• Beijing Information Science and Technology University, China

autor

Wang Yuxin

• Beijing Information Science and Technology University, China

autor

Chen Ruihan

• Beijing Information Science and Technology University, China

autor

Wang Liyong

• Beijing Information Science and Technology University, China

Bibliografia

• 1. Bachar L, Klein R, Tur M, Bortman J. Fault diagnosis of gear transmissions via optic Fiber Bragg Grating strain sensors. Mechanical Systems and Signal Processing 2022; (169-), https://doi.org/10.1016/j.ymssp.2021.108629.
• 2. Chen H, Kurt M, Lee Y S, et al. Experimental system identification of the dynamics of a vibro-impact beam with a view towards structural health monitoring and damage detection. Mechanical Systems and Signal Processing 2014; 46(1): 91-113, https://doi.org/10.1016/j.ymssp.2013.12.014.
• 3. Guo R, Sui J. Remaining Useful Life Prognostics for the Electrohydraulic Servo Actuator Using Hellinger Distance-Based Particle Filter. IEEE Transactions on Instrumentation and Measurement, 2020, 69(4):1148-1158. https://doi.org/10.1109/TIM.2019.2910919.
• 4. Helwig A, Muller G, Paul S. Health Monitoring of Aviation Hydraulic Fluids Using Opto-Chemical Sensor Technologies. Chemosensors 2021; 8(4),131, https://doi.org/10.3390/chemosensors8040131.
• 5. Huh J, Van H P, Han S, et al. A Data-Driven Approach for the Diagnosis of Mechanical Systems Using Trained Subtracted Signal Spectrograms. Sensors 2019; 19(5):1055, https://doi.org/10.3390/s19051055.
• 6. Jacobo V H, Ortiz A, Cerrud Y, et al. Hybrid expert system for the failure analysis of mechanical elements. Engineering Failure Analysis 2007; 14(8):1435-1443, https://doi.org/10.1016/j.engfailanal.2007.02.002.
• 7. Jia F, Lei Y, Lin J, et al. Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical System and Signal Processing 2016; 72/73: 303-315, https://doi.org/10.1016/j.ymssp.2015.10.025.
• 8. Jiang C, Wei Feng W. Prognostics and Health Management of Mechanical Systems. Advanced Materials Research 2013; 694–697:872–875, https://doi.org/10.4028/www.scientific.net/AMR.694-697.872.
• 9. Kelley J, Hagan M. New Fault Diagnosis Procedure and Demonstration on Hydraulic Servo-Motor for Single Faults. IEEE/ASME Transactions on Mechatronics 2020; 25(3): 1499-1509, https://doi.org/10.1109/TMECH.2020.2977857.
• 10. Kim Y, Na K, Youn B D. A health-adaptive time-scale representation (HTSR) embedded convolutional neural network for gearbox fault diagnostics. Mechanical Systems and Signal Processing 2022; 167:108575-, https://doi.org/10.1016/j.ymssp.2021.108575.
• 11. Krishnan M, Venkatesan S, Nagendran V, et al. Health monitoring and prognosis of electric vehicle motor using intelligent-digital twin. IET Electric Power Applications 2019; 13(9):1328-1335, https://doi.org/10.1049/iet-epa.2018.5732.
• 12. Kumar S, Kumar P, Kumar G. Degradation assessment of bearing based on machine learning classification matrix. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021(2); https://doi.org/10.17531/ein.2021.2.20.
• 13. Lei Y, Jia F, Lin J, Xing S, Ding S X, et al. An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data. IEEE Transactions on Industrial Electronics 2016; 63:3137-3147. https://doi.org/10.1109/TIE.2016.2519325.
• 14. Macaluso A, Jacazio G. Prognostic and Health Management System for Fly-by-wire Electro-hydraulic Servo Actuators for Detection and Tracking of Actuator Faults. Procedia Cirp 2017; 59:116-121. https://doi.org/10.1016/j.procir.2016.09.016.
• 15. Meng LH, Wang PZ, Liu ZG, et al. Safety Assessment for Electrical Motor Drive System Based on SOM Neural Network. Mathematical Problems in Engineering 2016; 2358142, https://doi.org/10.1155/2016/2358142.
• 16. Morais TS, Leao LD, Ap Cavalini A, Steffen V. Rotating machinery health evaluation by modal force identification. Inverse Problems in Science and Engineering 2019; 28(5): 659-715, https://doi.org/10.1080/17415977.2019.1644331.
• 17. Mu H, Yao Z, Yi X , et al. Reliability analysis for an EHCS of automatic transmission based on GO method// 2016 11th International Conference on Reliability, Maintainability and Safety (ICRMS). IEEE, 2016. https://doi.org/10.1109/ICRMS.2016.8050062
• 18. Pang T, Yu T, Song B. A Bayesian network model for fault diagnosis of a lock mechanism based on degradation data. Engineering Failure Analysis 2021; 122:105225, https://doi.org/10.1016/j.engfailanal.2021.105225.
• 19. Prakash J, Kankar PK. Health prediction of hydraulic cooling circuit using deep neural network with ensemble feature ranking technique. Measurement 2019; (151):107225, https://doi.org/10.1016/j.measurement.2019.107225.
• 20. Qi H T, Zhao D A, Liu D, et al. Double Redundancy Electro-Hydrostatic Actuator Fault Diagnosis Method Based on Progressive Fault Diagnosis Method. Actuators 2022; 11(9): 264, https://doi.org/10.3390/act11090264.
• 21. Rodrigues, Leonardo. Remaining Useful Life Prediction for Multiple-Component Systems Based on a System-Level Performance Indicator. IEEE/ASME Transactions on Mechatronics 2017; 23(1): 141-150, https://doi.org/10.1109/TMECH.2017.2713722.
• 22. Seventekidis P , Giagopoulos D. A combined finite element and hierarchical Deep learning approach for structural health monitoring: Test on a pin-joint composite truss structure. Mechanical Systems and Signal Processing 2021; 157(5):107735, https://doi.org/10.1016/j.ymssp.2021.107735.
• 23. Suh S, Jang J, Won S, et al. Supervised Health Stage Prediction Using Convolutional Neural Networks for Bearing Wear. Sensors 2020; 20(20): 5846, https://doi.org/10.3390/s20205846.
• 24. Sun W, Shao S, Zhao R, et al. A sparse auto- encoder-based deep neural network approach for induction motor faults classification. Measurement 2016; 89:171-178, https://doi.org/10.1016/j.measurement.2016.04.007.
• 25. Yan S, Ma B, Zheng C. Health index extracting methodology for degradation modelling and prognosis of mechanical transmissions. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 21(1):137-144, https://doi.org/10.17531/ein.2019.1.15.
• 26. Yong B L, Lee G C, Park J W, et al. Failure Analysis of a Hydraulic Power System in the Wind Turbine. Engineering Failure Analysis 2019; 107:104218, https://doi.org/10.1016/j.engfailanal.2019.104218.
• 27. Zhang Y, Liu L, Peng Y, et al. Health indicator extraction with phase current for power electronics of electro-mechanical actuator. Measurement, 159:107787, https://doi.org/10.1016/j.measurement.2020.107787

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).