Development of an Operational Digital Twin of a Locomotive Braking System Solenoid Valve for Fault Classification
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Published
Sep 4, 2023
Gabriel Davidyan
Jacob Bortman
Ron.S Kenett
Abstract
In recent years, a growing role in digital technologies has been filled by model-based digital twinning. A digital twin produces a mapping of a physical structure, operating in the digital domain. Combined with sensor technology and analytics, a digital twin can provide enhanced monitoring, diagnostic, and optimization capabilities. This research harnesses the significant capabilities of digital twining for the unmitigated challenge of fault type classification of a locomotive braking system solenoid valve. We develop a digital twin of the solenoid valve and suggest a method for fault type classification based on the digital twin. The diagnostic ability of the approach is demonstrated on a large experimental dataset.
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Keywords
Digital Twin, Locootive, Fault Classification, Solenoid Valve, Maintenance
References
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B€ O Orner, M., Straky, H., Weispfenning, T., & Isermann, R. (n.d.). Model based fault detection of vehicle suspension and hydraulic brake systems.
Chen, P., Toyota, T., & He, Z. (2001). Automated Function Generation of Symptom Parameters and Application to Fault Diagnosis of Machinery Under Variable Operating Conditions. In SYSTEMS AND HUMANS (Vol. 31, Issue 6).
El Mejdoubi, A., Oukaour, A., Chaoui, H., Gualous, H., Sabor, J., & Slamani, Y. (2016). State-of-Charge and State-of-Health Lithium-Ion Batteries’ Diagnosis According to Surface Temperature Variation. IEEE Transactions on Industrial Electronics, 63(4), 2391– 2402. https://doi.org/10.1109/TIE.2015.2509916
Escobar, R. F., Astorga-Zaragoza, C. M., Tllez-Anguiano, A. C., Jurez-Romero, D., Hernndez, J. A., & Guerrero-Ramrez, G. V. (2011). Sensor fault detection and isolation via high-gain observers: Application to a double-pipe heat exchanger. ISA Transactions, 50(3), 480–486. https://doi.org/10.1016/j.isatra.2011.03.002
Fan, X., He, Y., Cheng, P., & Fang, M. (2019). Fuzzy-Type Fast Terminal Sliding-Mode Controller for Pressure Control of Pilot Solenoid Valve in Automatic Transmission. IEEE Access, 7, 122342–122353. https://doi.org/10.1109/ACCESS.2019.2937847
Guo, H., Wang, K., Cui, H., Xu, A., & Jiang, J. (2017). A Novel Method of Fault Detection for Solenoid Valves Based on Vibration Signal Measurement. Proceedings 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, IThings-GreenCom-CPSCom-Smart Data 2016, 870–873. https://doi.org/10.1109/iThingsGreenCom-CPSCom-SmartData.2016.179
Guo, W., Cheng, J., Tan, Y., & Liu, Q. (2018). Solenoid Valve Fault Diagnosis Based on Genetic Optimization MKSVM. IOP Conference Series: Earth and Environmental Science, 170(4). https://doi.org/10.1088/1755-1315/170/4/042134
Kawashima, K., Ishii, Y., Funaki, T., & Kagawa, T. (2004). Determination of flow rate characteristics of pneumatic solenoid valves using an isothermal chamber. Journal of Fluids Engineering, Transactions of the ASME, 126(2), 273–279. https://doi.org/10.1115/1.1667888
Kwon, D., Hodkiewicz, M. R., Fan, J., Shibutani, T., & Pecht, M. G. (2016). IoT-Based Prognostics and Systems Health Management for Industrial Applications. IEEE Access, 4, 3659–3670. https://doi.org/10.1109/ACCESS.2016.2587754
Luomala, J., & Hakala, I. (2015). Effects of temperature and humidity on radio signal strength in outdoor wireless sensor networks. Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, 1247–1255. https://doi.org/10.15439/2015F241
Oh, H., Choi, S., Kim, K., Youn, B. D., & Pecht, M. (2015). An empirical model to describe performance degradation for warranty abuse detection in portable electronics. Reliability Engineering and System Safety, 142, 92–99. https://doi.org/10.1016/j.ress.2015.04.019
Oh, J. Y., Park, Y. J., Lee, G. H., & Song, C. S. (2012). Modeling and validation of a hydraulic systems for an AMT. International Journal of Precision Engineering and Manufacturing, 13(5), 701–707. https://doi.org/10.1007/s12541-012-0091-6
Park, J., Ha, J. M., Oh, H., Youn, B. D., Choi, J. H., & Kim, N. H. (2016). Model-Based Fault Diagnosis of a Planetary Gear: A Novel Approach Using Transmission Error. IEEE Transactions on Reliability, 65(4), 1830–1841. https://doi.org/10.1109/TR.2016.2590997
Seo, B., Jo, S.-H., Oh, H., & Youn, B. D. (n.d.). Solenoid Valve Diagnosis for Railway Braking Systems with Embedded Sensor Signals and Physical Interpretation.
Trappey, A. J. C., Trappey, C. V., Ma, L., & Chang, J. C. M. (2015). Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions. Computers and Industrial Engineering, 84, 3–11. https://doi.org/10.1016/j.cie.2014.12.033
Tsai, H. H., & Tseng, C. Y. (2010). Detecting solenoid valve deterioration in in-use electronic diesel fuel injection control systems. Sensors, 10(8), 7157–7169. https://doi.org/10.3390/s100807157
Wang, K., Guo, H., Xu, A., Jameson, N. J., Pecht, M., & Yan, B. (2018). Creating Self-Aware Low-Voltage Electromagnetic Coils for Incipient Insulation Degradation Monitoring for Smart Manufacturing. IEEE Access, 6, 69860–69868. https://doi.org/10.1109/ACCESS.2018.2880266
Yoon, J. Il, Truong, D. Q., & Ahn, K. K. (2013). A generation step for an electric excavator with a control strategy and verifications of energy consumption. International Journal of Precision Engineering and Manufacturing, 14(5), 755–766. https://doi.org/10.1007/s12541-0130099-6
Section
Regular Session Papers
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