Towards a health-aware fault tolerant control of complex systems: A vehicle fleet case

Lipiec, Bogdan ; Mrugalski, Marcin ; Witczak, Marcin ; Stetter, Ralf

International Journal of Applied Mathematics and Computer Science, Tome 32 (2022) no. 4, pp. 619-634.

Résumé

The paper deals with the problem of health-aware fault-tolerant control of a vehicle fleet. In particular, the development process starts with providing the description of the process along with a suitable Internet-of-Things platform, which enables appropriate communication within the vehicle fleet. It also indicates the transportation tasks to the designated drivers and makes it possible to measure their realization times. The second stage pertains to the description of the analytical model of the transportation system, which is obtained with the max-plus algebra. Since the vehicle fleet is composed of heavy duty machines, it is crucial to monitor and analyze the degradation of their selected mechanical components. In particular, the components considered are ball bearings, which are employed in almost every mechanical transportation system. Thus, a fuzzy logic Takagi–Sugeno approach capable of assessing their time-to-failure is proposed. This information is utilized in the last stage, which boils down to health-aware and fault-tolerant control of the vehicle fleet. In particular, it aims at balancing the exploitation of the vehicles in such a way as to maximize they average time-to-failure. Moreover, the fault-tolerance is attained by balancing the use of particular vehicles in such a way as to minimize the effect of possible transportation delays within the system. Finally, the effectiveness of the proposed approach is validated using selected simulation scenarios involving vehicle-based transportation tasks.

Détail

Keywords: FTCD, modeling bearings degradation, remaining useful life prediction, health aware fault tolerant control, Takagi-Sugeno model
Mots-clés : FTCD, przewidywanie pozostałego czasu pracy, model Takagi-Sugeno

Bibliographie

[1] [1] AlShorman, O., Alkahatni, F., Masadeh, M., Irfan, M., Glowacz, A., Althobiani, F., Kozik, J. and Glowacz, W. (2021). Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study, Advances in Mechanical Engineering 13(2): 1687814021996915.

[2] [2] AlShorman, O., Irfan, M., Saad, N., Zhen, D., Haider, N., Glowacz, A. and AlShorman, A. (2020). A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor, Shock and Vibration 2020, Article ID: 8843759.

[3] [3] Anis, M.D. (2018). Towards remaining useful life prediction in rotating machine fault prognosis: An exponential degradation model, Condition Monitoring and Diagnosis (CMD), Bentley, Australia, pp. 1–6, DOI: 10.1109/CMD.2018.8535765.

[4] [4] Arablouei, R. and Doğançay, K. (2013). Modified quasi-OBE algorithm with improved numerical properties, Signal Processing 93(4): 797–803.

[5] [5] Butkovic, P. (2010). Max-Linear Systems: Theory and Algorithms, Springer, London.

[6] [6] Chen, Y., Peng, G., Zhu, Z. and Li, S. (2020). A novel deep learning method based on attention mechanism for bearing remaining useful life prediction, Applied Soft Computing 86: 105–919.

[7] [7] Chudnovsky, B.H. (2012). Electrical Power Transmission and Distribution, CRC Press, Boca Raton.

[8] [8] De Schutter, B. and Van Den Boom, T. (2001). Model predictive control for max-plus-linear discrete event systems, Automatica 37(7): 1049–1056.

[9] [9] Do, N.V., Nguyen, H.D. and Selamat, A. (2018). Knowledge-based model of expert systems using Rela-model, International Journal of Software Engineering and Knowledge Engineering 28(08): 1047–1090.

[10] [10] Duan, Z., Wu, T., Guo, S., Shao, T., Malekian, R. and Li, Z. (2018). Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: A review, International Journal of Advanced Manufacturing Technology 96(1): 803–819.

[11] [11] Gao, Z. and Liu, X. (2021). An overview on fault diagnosis, prognosis and resilient control for wind turbine systems, Processes 9(2): 300.

[12] [12] Gebraeel, N., Lawley, M., Li, R. and Ryan, J. (2005). Residual-life distributions from component degradation signals: A Bayesian approach, IIE Transactions 37(6): 543–557.

[13] [13] Hamdi, H., Rodrigues, M., Rabaoui, B. and Benhadj Braiek, N. (2021). A fault estimation and fault-tolerant control based sliding mode observer for LPV descriptor systems with time delay, International Journal of Applied Mathematics and Computer Science 31(2): 247–258, DOI: 10.34768/amcs-2021-0017.

[14] [14] Jain, T. and Yamé, J. (2020). Health-aware fault-tolerant receding horizon control of wind turbines, Control Engineering Practice 95: 104236.

[15] [15] Kraus, T.,Mandour, G.I. and Joachim, D. (2007). Estimating the error bound in QOBE vowel classification, 50th Midwest Symposium on Circuits and Systems, Montreal, Canada, pp. 369–372.

[16] [16] Li, N., Lei, Y., Lin, J. and Ding, S. (2015). An improved exponential model for predicting remaining useful life of rolling element bearings, IEEE Transactions on Industrial Electronics 62(12): 7762–7773.

[17] [17] Li, X., Ding, Q. and Sun, J.-Q. (2018). Remaining useful life estimation in prognostics using deep convolution neural networks, Reliability Engineering System Safety 172: 1–11.

[18] [18] Lipiec, B., Mrugalski, M. and Witczak,M. (2021). Health-aware fault-tolerant control of multiple cooperating autonoumous vehicles, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Luxembourg, Luxembourg, pp. 1–7.

[19] [19] Liu, Z. and Zhang, L. (2020). A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings, Measurement 149: 107002.

[20] [20] Majdzik, P., Akielaszek-Witczak, A., Seybold, L., Stetter, R. and Mrugalska, B. (2016). A fault-tolerant approach to the control of a battery assembly system, Control Engineering Practice 55: 139–148.

[21] [21] Majdzik, P., Witczak, M., Lipiec, B. and Banaszak, Z. (2021). Integrated fault-tolerant control of assembly and automated guided vehicle-based transportation layers, International Journal of Computer Integrated Manufacturing 35(4–5): 1–18.

[22] [22] Mrugalski, M. and Korbicz, J. (2007). Least mean square vs. outer bounding ellipsoid algorithm in confidence estimation of the GMDH neural networks, in B. Beliczyński et al. (Eds), Adaptive and Natural Computing Algorithms, Part 2, Lecture Notes in Computer Science, Vol. 4432, Springer, Berlin, p. 19.

[23] [23] Nath, A.G., Udmale, S.S. and Singh, S.K. (2021). Role of artificial intelligence in rotor fault diagnosis: A comprehensive review, Artificial Intelligence Review 54(4): 2609–2668.

[24] [24] Nectoux, P.R.G., Medjaher, K., Ramasso, E., Morello, B., Zerhouni, N. and Varnier., C. (2012). PRONOSTIA: An experimental platform for bearings accelerated life test, IEEE International Conference on Prognostics and Health Management, Denver, USA, pp. 1–8.

[25] [25] Pazera, M., Buciakowski, M., Witczak, M. and Mrugalski, M. (2020). A quadratic boundedness approach to a neural network-based simultaneous estimation of actuator and sensor faults, Neural Computing Applications 32(2, SI): 379–389.

[26] [26] Salazar, J.C., Sanjuan, A., Nejjari, F. and Sarrate, R. (2020). Health-aware and fault-tolerant control of an octorotor UAV system based on actuator reliability, International Journal of Applied Mathematics and Computer Science 30(1): 47–59, DOI: 10.34768/amcs-2020-0004.

[27] [27] Seybold, L., Witczak, M., Majdzik, P. and Stetter, R. (2015). Towards robust predictive fault-tolerant control for a battery assembly system, International Journal of Applied Mathematics and Computer Science 25(4): 849–862, DOI: 10.1515/amcs-2015-0061.

[28] [28] Singleton, K.R., Strangas, E.G., Cui, H. and Aviyente, S. (2015). Extended Kalman filtering for remaining-useful-life estimation of bearings, IEEE Transactions on Industrial Electronics 62(3): 1781–1790.

[29] [29] Sun, B., Li, Y., Wang, Z., Ren, Y., Feng, Q., Yang, D., Lu, M. and Chen, X. (2019). Remaining useful life prediction of aviation circular electrical connectors using vibration-induced physical model and particle filtering method, Microelectronics Reliability 92: 114–122.

[30] [30] Sutrisno, E., Oh, H. and Vasan, A.S.S. (2012). Estimation of remaining useful life of ball bearings using data driven methodologies, IEEE Conference on Prognostics and Health Management (PHM), Denver, USA, pp. 1–7.

[31] [31] Tanaka, K. and Sugeno, M. (1992). Stability analysis and design of fuzzy control systems, Fuzzy Sets and Systems 45(2): 135–156.

[32] [32] Van Den Boom, T. and De Schutter, B. (2006). Modelling and control of discrete event systems using switching max-plus-linear systems, Control Engineering Practice 14(10): 1199–1211.

[33] [33] Wang, C., Lu, N., Wang, S., Cheng, Y. and Jiang, B. (2018). Dynamic long short-term memory neural-network-based indirect remaining-useful-life prognosis for satellite lithium-ion battery, Applied Sciences 8(11): 2078.

[34] [34] Wei, Y., Li, Y., Xu, M. and Huang, W. (2019). A review of early fault diagnosis approaches and their applications in rotating machinery, Entropy 21(4): 409, DOI: 10.3390/e21040409.

[35] [35] Witczak,M. (2014). Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems, Springer, Heidelberg.

[36] [36] Witczak, M., Lipiec, B., Mrugalski, M., Seybold, L. and Banaszak, Z. (2020a). Fuzzy modelling and robust fault-tolerant scheduling of cooperating forklifts, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK, pp. 1–10.

[37] [37] Witczak, M., Majdzik, P., Stetter, R. and Lipiec, B. (2020b). A fault-tolerant control strategy for multiple automated guided vehicles, Journal of Manufacturing Systems 55: 56–68.

[38] [38] Witczak, M., Mrugalski, M., Pazera, M. and Kukurowski, N. (2020c). Fault diagnosis of an automated guided vehicle with torque and motion forces estimation: A case study, ISA Transactions 104: 370–381.

[39] [39] Xie, X., Ma, D., Yue, D. and Xia, J. (2021). Gain-scheduling fault estimation for discrete-time Takagi–Sugeno fuzzy systems: A depth partitioning approach, IEEE Transactions on Circuits and Systems I: Regular Papers 69(4): 1693–1703.

[40] [40] Yan, R. and Gao, R.X. (2009). Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis, Tribology International 42(2): 293–302.

[41] [41] Zadeh, L.A. (1992). Knowledge representation in fuzzy logic, in R.R. Yager and L.A. Zadeh (Eds), An Introduction to Fuzzy Logic Applications in Intelligent Systems, Springer, Boston, pp. 1–25.

[42] [42] Zhang, L., Mu, Z. and Sun, C. (2018). Remaining useful life prediction for lithium-ion batteries based on exponential model and particle filter, IEEE Access 6: 17729–17740.

[43] [43] Zhou, Y., Huang, Y., Pang, J. and Wang, K. (2019). Remaining useful life prediction for supercapacitor based on long short-term memory neural network, Journal of Power Sources 440: 227149.