A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation
Recent catastrophic events in aviation have shown that current fault diagnosis schemes may not be enough to ensure a reliable and prompt sensor fault diagnosis. This paper describes a comparative analysis of consolidated data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques us...
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| Format: | Article |
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MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/5/1645 |
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| author | Nicholas Cartocci Marcello R. Napolitano Gabriele Costante Mario L. Fravolini |
| author_facet | Nicholas Cartocci Marcello R. Napolitano Gabriele Costante Mario L. Fravolini |
| author_sort | Nicholas Cartocci |
| collection | DOAJ |
| description | Recent catastrophic events in aviation have shown that current fault diagnosis schemes may not be enough to ensure a reliable and prompt sensor fault diagnosis. This paper describes a comparative analysis of consolidated data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques using flight data. Linear regression models, identified from data, are derived to build primary and transformed residuals. These residuals are then implemented to develop fault isolation schemes for 14 sensors of a semi-autonomous aircraft. Specifically, directional Mahalanobis distance-based and fault reconstruction-based techniques are compared in terms of their FI and FE performance. Then, a bank of Bayesian filters is proposed to compute, in flight, the fault belief for each sensor. Both the training and the validation of the schemes are performed using data from multiple flights. Artificial faults are injected into the fault-free sensor measurements to reproduce the occurrence of failures. A detailed evaluation of the techniques in terms of FI and FE performance is presented for failures on the air-data sensors, with special emphasis on the True Air Speed (TAS), Angle of Attack (AoA), and Angle of Sideslip (AoS) sensors. |
| first_indexed | 2024-03-09T00:29:33Z |
| format | Article |
| id | doaj.art-8416bb269d68489db19d68bdf69f85fa |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:29:33Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8416bb269d68489db19d68bdf69f85fa2023-12-11T18:39:41ZengMDPI AGSensors1424-82202021-02-01215164510.3390/s21051645A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault IsolationNicholas Cartocci0Marcello R. Napolitano1Gabriele Costante2Mario L. Fravolini3Department of Engineering, University of Perugia, Via G. Duranti, 67, 06125 Perugia, ItalyDepartment of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26506-6106, USADepartment of Engineering, University of Perugia, Via G. Duranti, 67, 06125 Perugia, ItalyDepartment of Engineering, University of Perugia, Via G. Duranti, 67, 06125 Perugia, ItalyRecent catastrophic events in aviation have shown that current fault diagnosis schemes may not be enough to ensure a reliable and prompt sensor fault diagnosis. This paper describes a comparative analysis of consolidated data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques using flight data. Linear regression models, identified from data, are derived to build primary and transformed residuals. These residuals are then implemented to develop fault isolation schemes for 14 sensors of a semi-autonomous aircraft. Specifically, directional Mahalanobis distance-based and fault reconstruction-based techniques are compared in terms of their FI and FE performance. Then, a bank of Bayesian filters is proposed to compute, in flight, the fault belief for each sensor. Both the training and the validation of the schemes are performed using data from multiple flights. Artificial faults are injected into the fault-free sensor measurements to reproduce the occurrence of failures. A detailed evaluation of the techniques in terms of FI and FE performance is presented for failures on the air-data sensors, with special emphasis on the True Air Speed (TAS), Angle of Attack (AoA), and Angle of Sideslip (AoS) sensors.https://www.mdpi.com/1424-8220/21/5/1645data-driven fault diagnosisrobust residual generationfault isolation and estimationBayesian filteringaircraft safetyflight data |
| spellingShingle | Nicholas Cartocci Marcello R. Napolitano Gabriele Costante Mario L. Fravolini A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation Sensors data-driven fault diagnosis robust residual generation fault isolation and estimation Bayesian filtering aircraft safety flight data |
| title | A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation |
| title_full | A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation |
| title_fullStr | A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation |
| title_full_unstemmed | A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation |
| title_short | A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation |
| title_sort | comprehensive case study of data driven methods for robust aircraft sensor fault isolation |
| topic | data-driven fault diagnosis robust residual generation fault isolation and estimation Bayesian filtering aircraft safety flight data |
| url | https://www.mdpi.com/1424-8220/21/5/1645 |
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