Sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio Algorithm
Anomaly identification for internal combustion engine (ICE) sensors has become an important research area in recent years. In this work, a proposed indirect fuzzy Lyapunov-based computed ratio observer integrated with a support vector machine (SVM) was designed for sensor fault classification. The p...
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| Format: | Article |
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MDPI AG
2022-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/8/2974 |
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| author | Shahnaz TayebiHaghighi Insoo Koo |
| author_facet | Shahnaz TayebiHaghighi Insoo Koo |
| author_sort | Shahnaz TayebiHaghighi |
| collection | DOAJ |
| description | Anomaly identification for internal combustion engine (ICE) sensors has become an important research area in recent years. In this work, a proposed indirect fuzzy Lyapunov-based computed ratio observer integrated with a support vector machine (SVM) was designed for sensor fault classification. The proposed fuzzy Lyapunov-based computed ratio observer integrated with SVM has three main layers. In the preprocessing (first) layer, the resampled root mean square (RMS) signals are extracted from the original signals to the designed indirect observer. The second (observation) layer is the principal part with the proposed indirect fuzzy sensor-fault-classification technique. This layer has two sub-layers: signal modeling and estimation. The Gaussian autoregressive-Laguerre approach integrated with the fuzzy approach is designed for resampled RMS fuel-to-air-ratio normal signal modeling, while the subsequent sub-layer is used for resampled RMS fuel-to-air-ratio signal estimation using the proposed fuzzy Lyapunov-based computed ratio observer. The third layer, for residual signal generation and classification, is used to identify ICE sensor anomalies, where residual signals are generated by the difference between the original and estimated resampled RMS fuel-to-air-ratio signals. Moreover, SVM is suggested for residual signal classification. To test the effectiveness of the proposed method, the results are compared with two approaches: a Lyapunov-based computed ratio observer and a computed ratio observer. The results show that the accuracy of sensor anomaly classification by the proposed fuzzy Lyapunov-based computed ratio observer is 98.17%. Furthermore, the proposed scheme improves the accuracy of sensor fault classification by 8.37%, 2.17%, 6.17%, 4.57%, and 5.37% compared to other existing methods such as the computed ratio observer, the Lyapunov-based computed ratio observer, fuzzy feedback linearization observation, self-tuning fuzzy robust multi-integral observer, and Kalman filter technique, respectively. |
| first_indexed | 2024-03-09T04:14:09Z |
| format | Article |
| id | doaj.art-8b0e081fe32544e7b31d25b810df4ebd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:14:09Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8b0e081fe32544e7b31d25b810df4ebd2023-12-03T13:57:06ZengMDPI AGSensors1424-82202022-04-01228297410.3390/s22082974Sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio AlgorithmShahnaz TayebiHaghighi0Insoo Koo1Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 680-749, KoreaDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 680-749, KoreaAnomaly identification for internal combustion engine (ICE) sensors has become an important research area in recent years. In this work, a proposed indirect fuzzy Lyapunov-based computed ratio observer integrated with a support vector machine (SVM) was designed for sensor fault classification. The proposed fuzzy Lyapunov-based computed ratio observer integrated with SVM has three main layers. In the preprocessing (first) layer, the resampled root mean square (RMS) signals are extracted from the original signals to the designed indirect observer. The second (observation) layer is the principal part with the proposed indirect fuzzy sensor-fault-classification technique. This layer has two sub-layers: signal modeling and estimation. The Gaussian autoregressive-Laguerre approach integrated with the fuzzy approach is designed for resampled RMS fuel-to-air-ratio normal signal modeling, while the subsequent sub-layer is used for resampled RMS fuel-to-air-ratio signal estimation using the proposed fuzzy Lyapunov-based computed ratio observer. The third layer, for residual signal generation and classification, is used to identify ICE sensor anomalies, where residual signals are generated by the difference between the original and estimated resampled RMS fuel-to-air-ratio signals. Moreover, SVM is suggested for residual signal classification. To test the effectiveness of the proposed method, the results are compared with two approaches: a Lyapunov-based computed ratio observer and a computed ratio observer. The results show that the accuracy of sensor anomaly classification by the proposed fuzzy Lyapunov-based computed ratio observer is 98.17%. Furthermore, the proposed scheme improves the accuracy of sensor fault classification by 8.37%, 2.17%, 6.17%, 4.57%, and 5.37% compared to other existing methods such as the computed ratio observer, the Lyapunov-based computed ratio observer, fuzzy feedback linearization observation, self-tuning fuzzy robust multi-integral observer, and Kalman filter technique, respectively.https://www.mdpi.com/1424-8220/22/8/2974internal combustion enginesensor anomaly detectionGaussian autoregressive methodfuzzy approachcomputed ratio observerLyapunov robust method |
| spellingShingle | Shahnaz TayebiHaghighi Insoo Koo Sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio Algorithm Sensors internal combustion engine sensor anomaly detection Gaussian autoregressive method fuzzy approach computed ratio observer Lyapunov robust method |
| title | Sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio Algorithm |
| title_full | Sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio Algorithm |
| title_fullStr | Sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio Algorithm |
| title_full_unstemmed | Sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio Algorithm |
| title_short | Sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio Algorithm |
| title_sort | sensor fault diagnosis using a machine fuzzy lyapunov based computed ratio algorithm |
| topic | internal combustion engine sensor anomaly detection Gaussian autoregressive method fuzzy approach computed ratio observer Lyapunov robust method |
| url | https://www.mdpi.com/1424-8220/22/8/2974 |
| work_keys_str_mv | AT shahnaztayebihaghighi sensorfaultdiagnosisusingamachinefuzzylyapunovbasedcomputedratioalgorithm AT insookoo sensorfaultdiagnosisusingamachinefuzzylyapunovbasedcomputedratioalgorithm |