Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems )ABS) Speed Sensors by Using Neural Networks
This paper proposed neural networks to continuously provide alternative constructed signals for vehicle and wheel speed sensors utilized for the Anti-Lock Braking System (ABS), which serves as the fault tolerant control method. These alternative constructed signals are used for two purposes. The fir...
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| Format: | Article |
| Language: | English |
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Unviversity of Technology- Iraq
2023-02-01
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| Series: | Engineering and Technology Journal |
| Subjects: | |
| Online Access: | https://etj.uotechnology.edu.iq/article_175849_8c5be9d9dbe2514814bb5c4a0f41bd72.pdf |
| _version_ | 1797337685731311616 |
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| author | Ayad Abdulkareem Abdulrahim Humod Oday Ahmed |
| author_facet | Ayad Abdulkareem Abdulrahim Humod Oday Ahmed |
| author_sort | Ayad Abdulkareem |
| collection | DOAJ |
| description | This paper proposed neural networks to continuously provide alternative constructed signals for vehicle and wheel speed sensors utilized for the Anti-Lock Braking System (ABS), which serves as the fault tolerant control method. These alternative constructed signals are used for two purposes. The first is to generate residual signals, and the second is to be adopted instead of isolated faulty signals. The residual signal is generated by extracting the difference between the alternative constructed signals and the corresponding actual signals. These residual signals serve as an indication of fault occurrence and to express that fault severity. Whenever a fault occurrence is detected and diagnosed in one of the sensor’s signals, the faulty signal is isolated and replaced by the corresponding constructed signal to maintain the system's normal behavior under a faulty condition. The range of data covered under the proposed estimating neural networks is huge, continuous in time, and not sampled. In this work, the range of the data lies between [50 to 120 km/h] when the braking is started. That cannot be performed by any available method. These models' training process is based on the Levenberg-Marquardt (LM) algorithm, implemented and tested by MATLAB/Simulink. The results show that these models can accurately map the measured data into the desired output through the best-fit functions. The fast response of the trained models makes them suitable for real-time alternative signals for fault-tolerant purposes for speed sensors during hard or panic braking. |
| first_indexed | 2024-03-08T09:17:09Z |
| format | Article |
| id | doaj.art-6601cbeed6804e269d368d2e0c135732 |
| institution | Directory Open Access Journal |
| issn | 1681-6900 2412-0758 |
| language | English |
| last_indexed | 2024-03-08T09:17:09Z |
| publishDate | 2023-02-01 |
| publisher | Unviversity of Technology- Iraq |
| record_format | Article |
| series | Engineering and Technology Journal |
| spelling | doaj.art-6601cbeed6804e269d368d2e0c1357322024-01-31T14:15:37ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582023-02-0141233334410.30684/etj.2022.135106.1259175849Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems )ABS) Speed Sensors by Using Neural NetworksAyad Abdulkareem0Abdulrahim Humod1Oday Ahmed2Department of Electrical Engineering/ University of Technology-Iraq., Al-Sinaha Street, Baghdad, IraqDepartment of Electrical Engineering, University of Technology, Baghdad - IraqOday A. Ahmed received his MSc degree in Electrical and Electronic Engineering from University of Technology, Baghdad-Iraq, in 2002. He was awarded a PhD degree from University of Leicester in 2012. Since 2002, he has been a LecturerThis paper proposed neural networks to continuously provide alternative constructed signals for vehicle and wheel speed sensors utilized for the Anti-Lock Braking System (ABS), which serves as the fault tolerant control method. These alternative constructed signals are used for two purposes. The first is to generate residual signals, and the second is to be adopted instead of isolated faulty signals. The residual signal is generated by extracting the difference between the alternative constructed signals and the corresponding actual signals. These residual signals serve as an indication of fault occurrence and to express that fault severity. Whenever a fault occurrence is detected and diagnosed in one of the sensor’s signals, the faulty signal is isolated and replaced by the corresponding constructed signal to maintain the system's normal behavior under a faulty condition. The range of data covered under the proposed estimating neural networks is huge, continuous in time, and not sampled. In this work, the range of the data lies between [50 to 120 km/h] when the braking is started. That cannot be performed by any available method. These models' training process is based on the Levenberg-Marquardt (LM) algorithm, implemented and tested by MATLAB/Simulink. The results show that these models can accurately map the measured data into the desired output through the best-fit functions. The fast response of the trained models makes them suitable for real-time alternative signals for fault-tolerant purposes for speed sensors during hard or panic braking.https://etj.uotechnology.edu.iq/article_175849_8c5be9d9dbe2514814bb5c4a0f41bd72.pdfanti-lock braking systemdata constructionfault tolerant controlneural network, residual generationsensor fault |
| spellingShingle | Ayad Abdulkareem Abdulrahim Humod Oday Ahmed Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems )ABS) Speed Sensors by Using Neural Networks Engineering and Technology Journal anti-lock braking system data construction fault tolerant control neural network, residual generation sensor fault |
| title | Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems )ABS) Speed Sensors by Using Neural Networks |
| title_full | Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems )ABS) Speed Sensors by Using Neural Networks |
| title_fullStr | Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems )ABS) Speed Sensors by Using Neural Networks |
| title_full_unstemmed | Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems )ABS) Speed Sensors by Using Neural Networks |
| title_short | Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems )ABS) Speed Sensors by Using Neural Networks |
| title_sort | fault detection and fault tolerant control for anti lock braking systems abs speed sensors by using neural networks |
| topic | anti-lock braking system data construction fault tolerant control neural network, residual generation sensor fault |
| url | https://etj.uotechnology.edu.iq/article_175849_8c5be9d9dbe2514814bb5c4a0f41bd72.pdf |
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