Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection
This paper proposes a data-based approach for a robust fault detection (FD) of the inertial measurement unit (IMU) sensors of an aircraft. Fuzzy interval models (FIMs) have been introduced for coping with the significant modeling uncertainties caused by poorly modeled aerodynamics. The proposed FIMs...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/8/2488 |
| _version_ | 1798040642643820544 |
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| author | Michele Crispoltoni Mario Luca Fravolini Fabio Balzano Stephane D’Urso Marcello Rosario Napolitano |
| author_facet | Michele Crispoltoni Mario Luca Fravolini Fabio Balzano Stephane D’Urso Marcello Rosario Napolitano |
| author_sort | Michele Crispoltoni |
| collection | DOAJ |
| description | This paper proposes a data-based approach for a robust fault detection (FD) of the inertial measurement unit (IMU) sensors of an aircraft. Fuzzy interval models (FIMs) have been introduced for coping with the significant modeling uncertainties caused by poorly modeled aerodynamics. The proposed FIMs are used to compute robust prediction intervals for the measurements provided by the IMU sensors. Specifically, a nonlinear neural network (NN) model is used as central prediction of the sensor response while the uncertainty around the central estimation is captured by the FIM model. The uncertainty has been also modelled using a conventional linear Interval Model (IM) approach; this allows a quantitative evaluation of the benefits provided by the FIM approach. The identification of the IMs and of the FIMs was formalized as a linear matrix inequality (LMI) optimization problem using as cost function the (mean) amplitude of the prediction interval and as optimization variables the parameters defining the amplitudes of the intervals of the IMs and FIMs. Based on the identified models, FD validation tests have been successfully conducted using actual flight data of a P92 Tecnam aircraft by artificially injecting additive fault signals on the fault free IMU readings. |
| first_indexed | 2024-04-11T22:10:30Z |
| format | Article |
| id | doaj.art-26a9cd60fd8a48b990231a5b94228f31 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:10:30Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-26a9cd60fd8a48b990231a5b94228f312022-12-22T04:00:34ZengMDPI AGSensors1424-82202018-08-01188248810.3390/s18082488s18082488Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault DetectionMichele Crispoltoni0Mario Luca Fravolini1Fabio Balzano2Stephane D’Urso3Marcello Rosario Napolitano4Dipartimento di Ingegneria, Università di Perugia, Via G. Duranti, 67, 06125 Perugia, ItalyDipartimento di Ingegneria, Università di Perugia, Via G. Duranti, 67, 06125 Perugia, ItalyDipartimento di Ingegneria, Università di Perugia, Via G. Duranti, 67, 06125 Perugia, ItalyDepartment of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26506-6106, USADepartment of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26506-6106, USAThis paper proposes a data-based approach for a robust fault detection (FD) of the inertial measurement unit (IMU) sensors of an aircraft. Fuzzy interval models (FIMs) have been introduced for coping with the significant modeling uncertainties caused by poorly modeled aerodynamics. The proposed FIMs are used to compute robust prediction intervals for the measurements provided by the IMU sensors. Specifically, a nonlinear neural network (NN) model is used as central prediction of the sensor response while the uncertainty around the central estimation is captured by the FIM model. The uncertainty has been also modelled using a conventional linear Interval Model (IM) approach; this allows a quantitative evaluation of the benefits provided by the FIM approach. The identification of the IMs and of the FIMs was formalized as a linear matrix inequality (LMI) optimization problem using as cost function the (mean) amplitude of the prediction interval and as optimization variables the parameters defining the amplitudes of the intervals of the IMs and FIMs. Based on the identified models, FD validation tests have been successfully conducted using actual flight data of a P92 Tecnam aircraft by artificially injecting additive fault signals on the fault free IMU readings.http://www.mdpi.com/1424-8220/18/8/2488fault detectionfuzzy interval modelslinear matrix inequalitiesinertial navigation systemdata-driven modelling |
| spellingShingle | Michele Crispoltoni Mario Luca Fravolini Fabio Balzano Stephane D’Urso Marcello Rosario Napolitano Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection Sensors fault detection fuzzy interval models linear matrix inequalities inertial navigation system data-driven modelling |
| title | Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection |
| title_full | Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection |
| title_fullStr | Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection |
| title_full_unstemmed | Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection |
| title_short | Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection |
| title_sort | interval fuzzy model for robust aircraft imu sensors fault detection |
| topic | fault detection fuzzy interval models linear matrix inequalities inertial navigation system data-driven modelling |
| url | http://www.mdpi.com/1424-8220/18/8/2488 |
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