Detection, Isolation, and Identification of Multiplicative Faults in a DC Motor and Amplifier Using Parameter Estimation Techniques
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 ele...
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MDPI AG
2025-07-01
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| author | Sanja Antić Marko Rosić Branko Koprivica Alenka Milovanović Milentije Luković |
| author_facet | Sanja Antić Marko Rosić Branko Koprivica Alenka Milovanović Milentije Luković |
| author_sort | Sanja Antić |
| collection | DOAJ |
| description | 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 (<i>R</i><sub>1</sub>, <i>R</i><sub>9</sub>, <i>R</i><sub>12</sub>), capacitor (<i>C</i><sub>8</sub>), and motor parameters, including armature resistance (<i>R</i><sub>a</sub>), inertia (<i>J</i>), and friction coefficient (<i>B</i>). 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. |
| format | Article |
| id | doaj-art-36ddef95c8f049c2acaae9a5f5a820ad |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-36ddef95c8f049c2acaae9a5f5a820ad2025-08-20T03:36:02ZengMDPI AGApplied Sciences2076-34172025-07-011515832210.3390/app15158322Detection, Isolation, and Identification of Multiplicative Faults in a DC Motor and Amplifier Using Parameter Estimation TechniquesSanja Antić0Marko Rosić1Branko Koprivica2Alenka Milovanović3Milentije Luković4Faculty of Technical Sciences Čačak, University of Kragujevac, 32000 Čačak, SerbiaFaculty of Technical Sciences Čačak, University of Kragujevac, 32000 Čačak, SerbiaFaculty of Technical Sciences Čačak, University of Kragujevac, 32000 Čačak, SerbiaFaculty of Technical Sciences Čačak, University of Kragujevac, 32000 Čačak, SerbiaFaculty of Technical Sciences Čačak, University of Kragujevac, 32000 Čačak, SerbiaThe 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 (<i>R</i><sub>1</sub>, <i>R</i><sub>9</sub>, <i>R</i><sub>12</sub>), capacitor (<i>C</i><sub>8</sub>), and motor parameters, including armature resistance (<i>R</i><sub>a</sub>), inertia (<i>J</i>), and friction coefficient (<i>B</i>). 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.https://www.mdpi.com/2076-3417/15/15/8322fault detection, isolation, and identificationmultiplicative faultsDC motorelectronic amplifierSliding Window AlgorithmSliding Integral Algorithm |
| spellingShingle | Sanja Antić Marko Rosić Branko Koprivica Alenka Milovanović Milentije Luković Detection, Isolation, and Identification of Multiplicative Faults in a DC Motor and Amplifier Using Parameter Estimation Techniques Applied Sciences fault detection, isolation, and identification multiplicative faults DC motor electronic amplifier Sliding Window Algorithm Sliding Integral Algorithm |
| title | Detection, Isolation, and Identification of Multiplicative Faults in a DC Motor and Amplifier Using Parameter Estimation Techniques |
| title_full | Detection, Isolation, and Identification of Multiplicative Faults in a DC Motor and Amplifier Using Parameter Estimation Techniques |
| title_fullStr | Detection, Isolation, and Identification of Multiplicative Faults in a DC Motor and Amplifier Using Parameter Estimation Techniques |
| title_full_unstemmed | Detection, Isolation, and Identification of Multiplicative Faults in a DC Motor and Amplifier Using Parameter Estimation Techniques |
| title_short | Detection, Isolation, and Identification of Multiplicative Faults in a DC Motor and Amplifier Using Parameter Estimation Techniques |
| title_sort | detection isolation and identification of multiplicative faults in a dc motor and amplifier using parameter estimation techniques |
| topic | fault detection, isolation, and identification multiplicative faults DC motor electronic amplifier Sliding Window Algorithm Sliding Integral Algorithm |
| url | https://www.mdpi.com/2076-3417/15/15/8322 |
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