Multilayer Architecture for Fault Diagnosis of Embedded Systems

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Published Dec 16, 2021
Daniel Maas
Renan Sebem
André Bittencourt Leal

Abstract

This work presents a multilayer architecture for fault diagnosis in embedded systems based on formal modeling of Discrete Event Systems (DES). Most works on diagnosis of DES focus in faults of actuators, which are the devices subject to intensive wear in industry. However, embedded systems are commonly subject to cost reduction, which may increase the probability of faults in the electronic hardware. Further, software faults are hard to track and fix, and the common solution is to replace the whole electronic board. We propose a modeling approach which includes the isolation of the source of the fault in the model, regarding three layers of embedded systems: software, hardware, and sensors & actuators. The proposed method is applied to a home appliance refrigerator and after exhaustive practical tests with forced fault occurrences, all faults were diagnosed, precisely identifying the layer and the faulty component. The solution was then incorporated into the product manufactured in industrial scale.

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Keywords

Fault Diagnosis, Embedded Systems, Discrete Event Systems, Automata

References
Ammour, R., Leclercq, E., Sanlaville, E., & Lefebvre, D. (2017). State estimation of discrete event systems for RUL prediction issue. International Journal of Production Research, 55(23), 7040 - 7057.
Bennouna, O., & Roux, J. (2013). Real time diagnosis fault detection for the reliability improvement of the embedded systems. Journal of Signal Processing Systems, 73(2), 153 - 160.
Carvalho, L. K., Basilio, J. C., & Moreira, M. V. (2010). Robust diagnosability of discrete event systems subject to intermittent sensor failures. IFAC Proceedings Volumes, 43(12), 84 - 89.
Carvalho, L. K., Moreira, M. V., & Basilio, J. C. (2021). Comparative analysis of related notions of robust diagnosability of discrete-event systems. Annual Reviews in Control, 51, 23-36.
Cassandras, C. G., & Lafortune, S. (2008). Introduction to discrete event systems (2nd ed.). Kluwer Academic Publishers.
Clarke, E., Kroening, D., & Lerda, F. (2004). A tool for checking ansi-c programs. In K. Jensen & A. Podelski (Eds.), Tools and algorithms for the construction and analysis of systems (pp. 168–176). Berlin, Heidelberg: Springer Berlin Heidelberg.
Fritz, R., & Zhang, P. (2018). Overview of fault-tolerant control methods for discrete event systems. IFACPapersOnLine, 51(24), 88-95.
Gandhi, P., Turk, D. N., & Dahiya, D. R. (2020). Health monitoring of induction motors through embedded systems-simulation of broker rotor bar fault and abnormal gear teeth fault. Microprocessors and Microsystems, 76.
Ge, N., Nakajima, S., & Pantel, M. (2015). Online diagnosis of accidental faults for real-time embedded systems using a hidden markov model. Simulation, 91(10), 851 - 868.
Goebel, K., & Rajamani, R. (2021). Policy, regulations and standards in prognostics and health management. International Journal of Prognostics and Health Management.
Guo, Y., Wang, J., Chen, H., Li, G., Huang, R., Yuan, Y., Sun, S. (2019). An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems. Applied Thermal Engineering, 149, 1223 - 1235.
Kramer, S., & Tumer, I. Y. (2009). Towards statecharts based failure propagation analysis for designing embedded PHM systems. In Annual conference of the PHM society (Vol. 1).
Lu, S., He, Q., Yuan, T., & Kong, F. (2017). Online fault diagnosis of motor bearing via stochastic-resonance-based adaptive filter in an embedded system. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(7), 1111 - 1122.
Lu, S., He, Q., & Zhao, J. (2018). Bearing fault diagnosis of a permanent magnet synchronous motor via a fast and online order analysis method in an embedded system. Mechanical Systems and Signal Processing, 113, 36 - 49.
Lu, S., Qian, G., He, Q., Liu, F., Liu, Y., & Wang, Q. (2020). In situ motor fault diagnosis using enhanced convolutional neural network in an embedded system. IEEE Sensors Journal, 20(15), 8287 - 8296.
Moreira, B. G., & Leal, A. B. (2020). A proposal for an active diagnoser for safe fault-tolerant control of discrete event systems. IFAC-PapersOnLine, 53(4), 282-287.
Naha, A., Thammayyabbabu, K. R., Samanta, A. K., Routray, A., & Deb, A. K. (2017). Mobile application to detect induction motor faults. IEEE Embedded Systems Letters, 9(4), 117-120.
Nasri, O., Ben Lakhal, N. M., Adouane, L., & Ben Hadj Slama, J. (2019). Automotive decentralized diagnosis based on can real-time analysis. Journal of Systems Architecture, 98, 249 - 258.
Ning, S., Han, Z., Wu, X., & Wang, Z. (2018). Gear crack fault diagnosis based on embedded sensors. Zhendong yu Chongji/Journal of Vibration and Shock, 37(11), 42 - 47.
Pons, R., Subias, A., & Trave-Massuyes, L. (2015). Iterative hybrid causal model based diagnosis: Application to automotive embedded functions. Engineering Applications of Artificial Intelligence, 37, 319 - 335.
Ranade, A., Provan, G., El-Din Mady, A., & O’Sullivan, D. (2020). A computationally efficient method for fault diagnosis of fan-coil unit terminals in building heating ventilation and air conditioning systems. Journal of Building Engineering, 27.
Rudie, K., Bretzke, H., Dragert, C., Edlund, K., Grigorov, L., McAloney, C., Wood, M. (2020). Integrated discrete-event systems software. GitHub. Retrieved from https://github.com/krudie/IDES
Ruiz-Arenas, S., Rusak, Z., Horvath, I., & Meji-Gutierrez, R. (2019). Systematic exploration of signal-based indicators for failure diagnosis in the context of cyberphysical systems. Frontiers of Information Technology and Electronic Engineering, 20(2), 152 - 175.
Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control, 40(9), 1555-1575.
Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. C. (1996). Failure diagnosis using discrete-event models. IEEE Transactions on Control Systems Technology, 4(2), 105-124.
Takai, S. (2021). A general framework for diagnosis of discrete event systems subject to sensor failures. Automatica, 129, 109669.
Vignolles, A., Chanthery, E., & Ribot, P. (2020). An overview on diagnosability and prognosability for system monitoring. In Proceedings of the european conference of the PHM society 2020 (Vol. 5).
Watanabe, A. T., Leal, A. B., Cury, J. E., & de Queiroz, M. H. (2017). Safe controllability using online prognosis. IFAC-PapersOnLine, 50(1), 12359-12365.
Watanabe, A. T., Sebem, R., Leal, A. B., & da S. Hounsell, M. (2021). Fault prognosis of discrete event systems: An overview. Annual Reviews in Control, 51, 100-110.
Yan, J., Wang, J., Tang, C., Liu, X., Yang, M., Hao, W., Zeng, H. (2018). Performance investigation of vcsel-based voltage probe and its applications to hpem effects diagnosis of embedded systems. IEEE Transactions on Electromagnetic Compatibility, 60(6), 1923 - 1931.
Yang, S., Bian, C., Li, X., Tan, L., & Tang, D. (2018). Optimized fault diagnosis based on fmea-style cbr and bn for embedded software system. International Journal of Advanced Manufacturing Technology, 94(9-12), 3441 - 3453.
Zaytoon, J., & Lafortune, S. (2013). Overview of fault diagnosis methods for discrete event systems. Annual Reviews in Control, 37(2), 308 - 320.
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