Towards a Health–Aware Fault Tolerant Control of Complex Systems: A Vehicle Fleet Case

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): 1687814021996915.10.1177/1687814021996915 Search in Google Scholar

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: 8843759.10.1155/2020/8843759 Search in Google Scholar

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. Ouvrir le DOISearch in Google Scholar

Arablouei, R. and Doğançay, K. (2013). Modified quasi-OBE algorithm with improved numerical properties, Signal Processing 93(4): 797–803.10.1016/j.sigpro.2012.09.024 Search in Google Scholar

Butkovic, P. (2010). Max-Linear Systems: Theory and Algorithms, Springer, London.10.1007/978-1-84996-299-5 Search in Google Scholar

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: 105–919.10.1016/j.asoc.2019.105919 Search in Google Scholar

Chudnovsky, B.H. (2012). Electrical Power Transmission and Distribution, CRC Press, Boca Raton. Search in Google Scholar

De Schutter, B. and Van Den Boom, T. (2001). Model predictive control for max-plus-linear discrete event systems, Auto-matica 37(7): 1049–1056.10.1016/S0005-1098(01)00054-1 Search in Google Scholar

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): 1047–1090.10.1142/S0218194018500304 Search in Google Scholar

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): 803–819.10.1007/s00170-017-1474-8 Search in Google Scholar

Gao, Z. and Liu, X. (2021). An overview on fault diagnosis, prognosis and resilient control for wind turbine systems, Processes 9(2): 300.10.3390/pr9020300 Search in Google Scholar

Gebraeel, N., Lawley, M., Li, R. and Ryan, J. (2005). Residual-life distributions from component degradation signals: A Bayesian approach, IIE Transactions 37(6): 543–557.10.1080/07408170590929018 Search in Google Scholar

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. Ouvrir le DOISearch in Google Scholar

Jain, T. and Yamé, J. (2020). Health-aware fault-tolerant receding horizon control of wind turbines, Control Engineering Practice 95: 104236.10.1016/j.conengprac.2019.104236 Search in Google Scholar

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. Search in Google Scholar

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): 7762–7773.10.1109/TIE.2015.2455055 Search in Google Scholar

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: 1–11.10.1016/j.ress.2017.11.021 Search in Google Scholar

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. Search in Google Scholar

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: 107002.10.1016/j.measurement.2019.107002 Search in Google Scholar

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: 139–148.10.1016/j.conengprac.2016.07.001 Search in Google Scholar

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): 1–18.10.1080/0951192X.2021.1872103 Search in Google Scholar

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. Search in Google Scholar

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): 2609–2668.10.1007/s10462-020-09910-w Search in Google Scholar

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. Search in Google Scholar

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): 379–389.10.1007/s00521-018-3706-8 Search in Google Scholar

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. Ouvrir le DOISearch in Google Scholar

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. Ouvrir le DOISearch in Google Scholar

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): 1781–1790.10.1109/TIE.2014.2336616 Search in Google Scholar

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: 114–122.10.1016/j.microrel.2018.11.015 Search in Google Scholar

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. Search in Google Scholar

Tanaka, K. and Sugeno, M. (1992). Stability analysis and design of fuzzy control systems, Fuzzy Sets and Systems 45(2): 135–156.10.1016/0165-0114(92)90113-I Search in Google Scholar

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): 1199–1211.10.1016/j.conengprac.2006.02.006 Search in Google Scholar

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): 2078.10.3390/app8112078 Search in Google Scholar

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/e21040409.751489833267123 Ouvrir le DOISearch in Google Scholar

Witczak, M. (2014). Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems, Springer, Heidelberg.10.1007/978-3-319-03014-2 Search in Google Scholar

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. 1–10.10.1109/FUZZ48607.2020.9177782 Search in Google Scholar

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: 56–68.10.1016/j.jmsy.2020.02.009 Search in Google Scholar

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: 370–381.10.1016/j.isatra.2020.05.01232439131 Search in Google Scholar

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): 1693–1703.10.1109/TCSI.2021.3135911 Search in Google Scholar

Yan, R. and Gao, R.X. (2009). Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis, Tribology International 42(2): 293–302.10.1016/j.triboint.2008.06.013 Search in Google Scholar

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. 1–25.10.1007/978-1-4615-3640-6_1 Search in Google Scholar

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: 17729–17740.10.1109/ACCESS.2018.2816684 Search in Google Scholar

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: 227149.10.1016/j.jpowsour.2019.227149 Search in Google Scholar

• A Genetic Algorithm Based Optimized Convolutional Neural Network for Face Recognition
• Efficient Face Detection Based Crowd Density Estimation using Convolutional Neural Networks and an Improved Sliding Window Strategy
• Hospitalization Patient Forecasting Based on Multi–Task Deep Learning
• Lightweight Compression with Encryption Based on Asymmetric Numeral Systems
• Infrared Small–Target Detection Under a Complex Background Based on a Local Gradient Contrast Method
• Generation of Synchronizing State Machines from a Transition System: A Region–Based Approach
• A Contemporarymulti–Objective Feature Selection Model for Depression Detection Using a Hybrid pBGSK Optimization Algorithm
• FSPL: A Meta–Learning Approach for a Filter and Embedded Feature Selection Pipeline
• On the Finite Time Stabilization Via Robust Control for Uncertain Disturbed Systems
• Implications of the Arithmetic Ratio of Prime Numbers for RSA Security
• Fractional Time–Invariant Compartmental Linear Systems
• Decentralized Static Output Feedback Controller Design for Linear Interconnected Systems