Detection, Isolation, and Identification of Multiplicative Faults in a DC Motor and Amplifier Using Parameter Estimation Techniques
Source abstract
Abstract
The increasing complexity of modern control systems highlights the need for reliable and robust fault detection, isolation, and identification (FDII) methods, particularly in safety-critical and industrial applications. The study focuses on the FDII of multiplicative faults in a DC motor and its electronic amplifier. To simulate such scenarios, a complete laboratory platform was developed for real-time FDII, using relay-based switching and custom LabVIEW software 2009. This platform enables real-time experimentation and represents an important component of the study. Two estimation-based fault detection (FD) algorithms were implemented: the Sliding Window Algorithm (SWA) for discrete-time models and a modified Sliding Integral Algorithm (SIA) for continuous-time models. The modification introduced to the SIA limits the data length used in least squares estimation, thereby reducing the impact of transient effects on parameter accuracy. Both algorithms achieved high model output-to-measured signal agreement, up to 98.6% under nominal conditions and above 95% during almost all fault scenarios. Moreover, the proposed fault isolation and identification methods, including a decision algorithm and an indirect estimation approach, successfully isolated and identified faults in key components such as amplifier resistors (R1, R9, R12), capacitor (C8), and motor parameters, including armature resistance (Ra), inertia (J), and friction coefficient (B). The decision algorithm, based on continuous-time model coefficients, demonstrated reliable fault isolation and identification, while the reduced Jacobian-based approach in the discrete model enhanced fault magnitude estimation, with deviations typically below 10%. Additionally, the platform supports remote experimentation, offering a valuable resource for advancing model-based FDII research and engineering education.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fault Detection and Control Systems · Machine Fault Diagnosis Techniques · Control Systems and Identification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Technical Sciences Čačak, University of Kragujevac, 32000 Čačak, Serbia
Funding
Ministry of Science, Technological Development, and Innovation of the Republic of Serbia
References (52)
Show source-provided references
1. Park, Y.J., Fan, S.K.S., and Hsu, C.Y. (2020). A review on fault detection and process diagnostics in industrial processes. Processes, 8.10.3390/pr8091123 ↗
2. Fault detection using bispectral features and one-class classifiers10.1016/j.jprocont.2019.08.007 ↗
3. Gertler, J.J. (2017). Fault Detection and Diagnosis in Engineering Systems, CRC Press.10.1201/9780203756126 ↗
4. A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis10.1016/j.jobe.2020.101692 ↗
5. Mercorelli, P. (2024). Recent advances in intelligent algorithms for fault detection and diagnosis. Sensors, 24.10.3390/s24082656 ↗
6. Adouni, A., Ben Hamed, M., Flah, A., and Sbita, L. (2013, January 6–8). Sensor and actuator fault detection and isolation based on artificial neural networks and fuzzy logic applicated on induction motor. Proceedings of the 2013 International Conference on Control, Decision and Information Technologies (CoDIT), Hammamet, Tunisia.10.1109/CoDIT.2013.6689665 ↗
7. Actuator fault detection and isolation on a quadrotor unmanned aerial vehicle modeled as a linear parameter-varying system10.1177/0020294018824764 ↗
8. Debele, Y., Shi, H.Y., Wondosen, A., Ku, T.W., and Kang, B.S. (2023). Deep learning-based robust actuator fault detection and isolation scheme for highly redundant multirotor UAVs. Drones, 7.10.3390/drones7070437 ↗
9. Actuator fault detection and isolation in a class of nonlinear interconnected systems10.1080/00207179.2024.2310606 ↗
10. Application of structured and directional residuals for fault detection and isolation on permanent-magnet DC motor with amplifier10.1002/qre.1962 ↗
11. Comparison of structured residuals design techniques for actuator and sensor fault detection and isolation in a permanent magnet DC motor10.1007/s00202-023-02021-z ↗
12. Expert system for FDI of DC motor faults using structured residuals design technique10.2298/SJEE2301093A ↗
13. Boulkroune, B., Gálvez-Carrillo, M., and Kinnaert, M. (2011, January 28–30). Additive and multiplicative fault diagnosis for a doubly-fed induction generator. Proceedings of the IEEE International Conference on Control Applications (CCA), Denver, CO, USA.10.1109/CCA.2011.6044473 ↗
14. Ding, S.X., Frank, P.M., and Ding, E.L. (2002, January 10–13). An approach to the detection of multiplicative faults in uncertain dynamic systems. Proceedings of the IEEE Conference on Decision and Control, Las Vegas, NV, USA.
15. Multiple multiplicative fault diagnosis for dynamic processes via parameter similarity measures10.1021/ie061118c ↗
16. Multiplicative fault detection and isolation in dynamic systems using data-driven K-gap metric based KNN algorithm
17. Multiplicative fault estimation-based adaptive sliding mode fault-tolerant control design for nonlinear systems10.1155/2018/1462594 ↗
18. Fault detection and diagnosis of permanent-magnet DC motor based on parameter estimation and neural network10.1109/41.873210 ↗
19. Lu, L., and Wang, W. (2021). Fault diagnosis of permanent magnet DC motors based on multi-segment feature extraction. Sensors, 21.10.3390/s21227505 ↗
20. Dobra, P., Trușcă, M., Sita, I.V., Munteanu, R.A., and Munteanu, M. (2011, January 5–8). Model based approach in fault detection and diagnosis for DC motor. Proceedings of the 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Bologna, Italy.10.1109/DEMPED.2011.6063628 ↗
21. Liu, M., Yang, X., and Cui, S. (2008, January 3–5). Online fault detection and diagnosis of permanent-magnetic DC motor. Proceedings of the 2008 IEEE Vehicle Power and Propulsion Conference (VPPC), Harbin, China.
22. Condition monitoring and fault diagnosis of electrical motors—A review10.1109/TEC.2005.847955 ↗
23. A simple real-time fault signature monitoring tool for motor-drive-embedded fault diagnosis systems10.1109/TIE.2010.2051936 ↗
24. Park, J.-K., Seo, I.-M., and Hur, J. (2013, January 15–19). Fault type detection using frequency pattern of stator current in IPM-type BLDC motor under stator inter-turn, dynamic eccentricity, and coupled faults. Proceedings of the 2013 IEEE Energy Conversion Congress and Exposition (ECCE), Denver, CO, USA.10.1109/ECCE.2013.6647025 ↗
25. Saadaoui, W., and Jelassi, K. (2011, January 11–13). Induction motor bearing damage detection using stator current analysis. Proceedings of the 2011 International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), Málaga, Spain.10.1109/PowerEng.2011.6036429 ↗
26. Application of fully decoupled parity equation in fault detection and identification of DC motors10.1109/TIE.2006.878304 ↗
27. Lee, J.H.L.J., and Lyou, J.L. (2001, January 8–11). Fault Diagnosis and Fault Tolerant Control of DC Motor Driving System. Proceedings of the 2001 IEEE International Symposium on Industrial Electronics (ISIE 2001), Pusan, Republic of Korea.
28. Detection and Isolation of Speed-, DC-Link Voltage-, and Current-Sensor Faults Based on an Adaptive Observer in Induction-Motor Drives10.1109/TIE.2010.2055775 ↗
29. Integrated Nonlinear Model Predictive Fault-Tolerant Control and Multiple-Model-Based Fault Detection and Diagnosis10.1016/j.cherd.2013.07.028 ↗
30. Jiang, Z., Huang, X., and Cao, W. (2022). RLS-Based Algorithm for Detecting Partial Demagnetization under Both Stationary and Nonstationary Conditions. Energies, 15.10.3390/en15103509 ↗
31. Kuric, M., Lacevic, B., Osmic, N., and Tahirovic, A. (2017, January 24). RLS-Based Fault-Tolerant Tracking Control of Multirotor Aerial Vehicles. Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Munich, Germany.10.1109/AIM.2017.8014173 ↗
32. Leite, D.F., Hell, M.B., Diez, P.H., Ganglio, B.S.L., Nascimento, L.O., and Costa, P. (2007, January 3–5). Real-time model-based fault detection and diagnosis for alternators and induction motors. Proceedings of the IEEE International Electric Machines and Drives Conference (IEMDC), Miami, FL, USA.10.1109/IEMDC.2007.383577 ↗
33. Application of algebraic geometry techniques in permanent magnet DC motor fault detection and identification10.1016/j.ejcon.2015.05.001 ↗
34. Fernández Gómez, A.J., Jaramillo, V.H., and Ottewill, J.R. (2014, January 9–11). Fault Detection in Electric Motors by Means of the Extended Kalman Filter as Disturbance Estimator. Proceedings of the 2014 UKACC International Conference on Control (CONTROL 2014), Loughborough, UK.10.1109/CONTROL.2014.6915179 ↗
35. Innovative Extended Kalman Filter in a Coherent System for Detection and Compensation of Various Current Sensor Faults in the Induction Motor Drive10.1109/TIE.2025.3528487 ↗
36. Unscented Kalman Filter for Fault Detection10.3182/20050703-6-CZ-1902.01821 ↗
37. Karami, F., Poshtan, J., and Poshtan, M. (2010, January 8–10). Model-Based Fault Detection in Induction Motors. Proceedings of the IEEE International Conference on Control Applications (CCA), Yokohama, Japan.10.1109/CCA.2010.5611214 ↗
38. Rao, C.R., and Toutenburg, H. (1999). Linear Models: Least Squares and Alternatives, Springer.
39. Isermann, R. (2011). Fault-Diagnosis Applications: Model-Based Condition Monitoring; Actuators, Drives, Machinery, Plants, Sensors, and Fault-Tolerant Systems, Springer.10.1007/978-3-642-12767-0 ↗
40. Fuzzy Logic-Based Decision-Making for Fault Diagnosis in a DC Motor10.1016/j.engappai.2004.11.002 ↗
41. Induction motor bearing fault classification using deep neural network with particle swarm optimization-extreme gradient boosting10.1049/elp2.12389 ↗
42. Toma, R.N., Prosvirin, A.E., and Kim, J.M. (2020). Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors, 20.10.3390/s20071884 ↗
43. Patton, R.J., Frank, P.M., and Clark, R.N. (2000). Issues of Fault Diagnosis for Dynamic Systems, Springer.10.1007/978-1-4471-3644-6 ↗
44. Isermann, R. (2006). Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, Springer.10.1007/3-540-30368-5 ↗
45. Permanent Magnet DC Motor Friction Measurement and Analysis of Friction’s Impact
46. Computer Simulation of Stick-Slip Friction in Mechanical Dynamic Systems10.1115/1.3140698 ↗
47. Ljung, L. (1999). System Identification: Theory for the User, Prentice Hall. [2nd ed.].
48. A Sliding Window Approach for Signal Processing in Fault Detection
49. Zhao, J., Wang, X., Gao, G., Na, J., Liu, H., and Luan, F. (2018). Online Adaptive Parameter Estimation for Quadrotors. Algorithms, 11.10.3390/a11110167 ↗
50. Yang, B., Wang, J., and Sun, Y. (2020). Fault Detection and Diagnosis Based on Sliding Mode Integral Algorithm for Nonlinear Systems. Appl. Sci., 10.
51. Antić, S., and Đurović, Ž. (2015, January 8–11). Application of Sliding Integral Algorithm for Parametric Fault Detection of a DC Motor Amplifier. Proceedings of the 2nd International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2015), Silver Lake, Serbia.
52. Numerical Integration Approach to On-Line Identification of Continuous-Time Systems10.1016/0005-1098(90)90158-E ↗