Combined approach to fault detection in complex technical systems based on bond-graph model

A new fault detection approach for complex technical systems has been developed and investigated, enabling the identification and classification of single and multiple simultaneous faults. The challenge of reliable and timely identification of both single and multiple simultaneous faults under conditions of limited access to labeled data has been addressed. A threat to the safe operation of autonomous equipment is a common challenge in the field operating conditions where traditional model-based or data-driven approaches, used individually, prove to be ineffective. This work presents a hybrid approach to fault detection. The proposed solution combines an analytical bond-graph model and a Convolutional Neural Network (CNN). The bond-graph generates residuals — the difference between values calculated based on the system physical laws and sensor measurements. The residuals are then analyzed by the CNN which is trained to detect and classify faults based on their characteristic features. Linear Fractional Transformation is employed to account for parameter uncertainties (e.g., resistance or capacitance). This approach allows combining a priori knowledge of the system physics with the capabilities of deep learning. The effectiveness of the approach was evaluated on a simulator of a hydraulic steering control system for autonomous equipment. Gaussian noise was added to the simulation to simulate real-world conditions. The experiments included incipient, abrupt, single, and multiple faults. Tests with varying amounts of training data, using sample sizes less than 128, demonstrated the higher effectiveness of the proposed hybrid approach compared to classical machine learning methods (such as Random Forest or K-Nearest Neighbors). A solution is proposed for fault detection in hydraulic control systems of autonomous equipment. The developed approach is particularly effective with limited data, making it suitable for field conditions. It allows for timely detection and classification of faults (e.g., valve leaks or solenoid valve failures), which reduces the risk of failures and ensures the safety of autonomous equipment. The results can be adapted and implemented for electrical, mechanical, and other complex technical systems.

1. Bouamama B.O., Biswas G., Loureiro R., Merzouki R. Graphical methods for diagnosis of dynamic systems: Review. Annual reviews in control, 2014, vol. 38, no. 2, pp. 199–219. https://doi.org/10.1016/j.arcontrol.2014.09.004

2. Isermann R. Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and FaultTolerant Systems. Springer Science & Business Media, 2011, 354 p.

3. Chen J., Patton R.J. Robust Model-Based Fault Diagnosis for Dynamic Systems. Springer Science & Business Media, 2012, vol. 3, 356 p.

4. Ding S.X. Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools. Springer Science & Business Media, 2012. 504 p.

5. Akhenak A., Duviella E., Bako L., Lecoeuche S. Online fault diagnosis using recursive subspace identification: application to a dam-gallery open channel system. Control Engineering Practice, 2013, vol. 21, no. 6, pp. 797–806. https://doi.org/10.1016/j.conengprac.2013.02.013

6. Lei Y., Yang B., Jiang X., Jia F., Li N., Nandi A.K. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mechanical Systems and Signal Processing, 2020, vol. 138, pp. 106587. https://doi.org/10.1016/j.ymssp.2019.106587

7. Xu G., Yu Z., Lu N., Lv G. High-gain observer-based sliding mode control for hydraulic excavators. Journal of Harbin Engineering University, 2021, vol. 42, no. 6, pp. 885–892.

8. Shen W., Yuan X., Liu M. Event-triggered control for hydraulic position tracking system with extended state observer. Journal of Mechanical Engineering. 2022, vol. 58, no. 8, pp. 274–284. (in Chinese). https://doi.org/10.3901/JME.2022.08.274

9. Nasiri S., Khosravani M.R., Weinberg K. Fracture mechanics and mechanical fault detection by artificial intelligence methods: a review. Engineering Failure Analysis, 2017, vol. 81, pp. 270–293. https://doi.org/10.1016/j.engfailanal.2017.07.011

10. Hu Z., Zhao G., Li F., Zhou D. Fault diagnosis for nonlinear dynamical system based on adaptive unknown input observer. Control and Decisions, 2016, vol. 31, no. 5, pp. 901–906.

11. Liu Y., Sun C., Chen Y., Lin G. Design and application on sliding mode controller for roller microporous system based on disturbance observer. Forging and Stamping Technology, 2022, vol. 47, no. 3, pp. 116–120. (in Chinese)

12. Bohagen B., Gravdahl J.T. Active surge control of compression system using drive torque. Automatica, 2008, vol. 44, no. 4, pp. 1135–1140. https://doi.org/10.1016/j.automatica.2007.11.002

13. Dmitriev V.A., Marusina M.Ya. Features of constructing a bond graph of walking robots. Journal of Instrument Engineering, 2024, vol. 67, no. 2, pp. 195–199. (in Russian). https://doi.org/10.17586/0021-3454-2024-67-2-195-199

14. Rahman A., Hasan N., Zaki M. Modelling and validation of electric vehicle drive line architectureusing Bond Graph. Test Engineering and Management, 2020, vol. 82, pp. 15154–15167.

15. Gonzalez-Avalos G., Gallegos N.B., Ayala-Jaimes G., Garcia A.P. Modeling and simulation in Multibond Graphs applied to three-phase electrical systems. Applied Sciences, 2023, vol. 13, no. 10, pp. 5880. https://doi.org/10.3390/app13105880

16. Li F., Wu Z., Li J., Lai Z., Zhao B., Min C. A multi-step CNN-based estimation of aircraft landing gear angles. Sensors, 2021, vol. 21, no. 24, pp. 8440. https://doi.org/10.3390/s21248440

17. Chen J., Xu Q., Guo Y., Chen R. Aircraft landing gear retraction/extension system fault diagnosis with 1-D dilated convolutional neural network. Sensors, 2022, vol. 22, no. 4, pp. 1367. https://doi.org/10.3390/s22041367

18. Saucedo-Dorantes J.J., Delgado-Prieto M., Osornio-Rios R.A., Romero-Troncoso R.J. Industrial data-driven monitoring based on incremental learning applied to the detection of novel faults. IEEE Transactions on Industrial Informatics, 2020, vol. 16, no. 9, pp. 5985–5995. https://doi.org/10.1109/TII.2020.2973731

19. Mo H., Li Y. Fault diagnosis based on interval analytic redundancy relation. Journal of Nanjing University of Aeronautics and Astronautics, 2021, vol. 53, no. 6, pp. 972–980.