Generic Diagnostic Framework for Anomaly Detection—Application in Satellite and Spacecraft Systems
Spacecraft systems collect health-related data continuously, which can give an indication of the systems’ health status. While they rarely occur, the repercussions of such system anomalies, faults, or failures can be severe, safety-critical and costly. Therefore, the data are used to anticipate any...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/10/8/673 |
| _version_ | 1797585874061361152 |
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| author | Marie Bieber Wim J. C. Verhagen Fabrice Cosson Bruno F. Santos |
| author_facet | Marie Bieber Wim J. C. Verhagen Fabrice Cosson Bruno F. Santos |
| author_sort | Marie Bieber |
| collection | DOAJ |
| description | Spacecraft systems collect health-related data continuously, which can give an indication of the systems’ health status. While they rarely occur, the repercussions of such system anomalies, faults, or failures can be severe, safety-critical and costly. Therefore, the data are used to anticipate any kind of anomalous behaviour. Typically this is performed by the use of simple thresholds or statistical techniques. Over the past few years, however, data-driven anomaly detection methods have been further developed and improved. They can help to automate the process of anomaly detection. However, it usually is time intensive and requires expertise to identify and implement suitable anomaly detection methods for specific systems, which is often not feasible for application at scale, for instance, when considering a satellite consisting of numerous systems and many more subsystems. To address this limitation, a generic diagnostic framework is proposed that identifies optimal anomaly detection techniques and data pre-processing and thresholding methods. The framework is applied to two publicly available spacecraft datasets and a real-life satellite dataset provided by the European Space Agency. The results show that the framework is robust and adaptive to different system data, providing a quick way to assess anomaly detection for the underlying system. It was found that including thresholding techniques significantly influences the quality of resulting anomaly detection models. With this, the framework can provide both a way forward in developing data-driven anomaly detection methods for spacecraft systems and guidance relative to the direction of anomaly detection method selection and implementation for specific use cases. |
| first_indexed | 2024-03-11T00:13:20Z |
| format | Article |
| id | doaj.art-46f90b596e4244ff92533b43f719a053 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T00:13:20Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-46f90b596e4244ff92533b43f719a0532023-11-18T23:49:39ZengMDPI AGAerospace2226-43102023-07-0110867310.3390/aerospace10080673Generic Diagnostic Framework for Anomaly Detection—Application in Satellite and Spacecraft SystemsMarie Bieber0Wim J. C. Verhagen1Fabrice Cosson2Bruno F. Santos3Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The NetherlandsAerospace Engineering and Aviation, RMIT University, Carlton, VIC 3053, AustraliaEuropean Space Research & Technology Centre, European Space Agency, 2200 AG Noordwijk, The NetherlandsFaculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The NetherlandsSpacecraft systems collect health-related data continuously, which can give an indication of the systems’ health status. While they rarely occur, the repercussions of such system anomalies, faults, or failures can be severe, safety-critical and costly. Therefore, the data are used to anticipate any kind of anomalous behaviour. Typically this is performed by the use of simple thresholds or statistical techniques. Over the past few years, however, data-driven anomaly detection methods have been further developed and improved. They can help to automate the process of anomaly detection. However, it usually is time intensive and requires expertise to identify and implement suitable anomaly detection methods for specific systems, which is often not feasible for application at scale, for instance, when considering a satellite consisting of numerous systems and many more subsystems. To address this limitation, a generic diagnostic framework is proposed that identifies optimal anomaly detection techniques and data pre-processing and thresholding methods. The framework is applied to two publicly available spacecraft datasets and a real-life satellite dataset provided by the European Space Agency. The results show that the framework is robust and adaptive to different system data, providing a quick way to assess anomaly detection for the underlying system. It was found that including thresholding techniques significantly influences the quality of resulting anomaly detection models. With this, the framework can provide both a way forward in developing data-driven anomaly detection methods for spacecraft systems and guidance relative to the direction of anomaly detection method selection and implementation for specific use cases.https://www.mdpi.com/2226-4310/10/8/673anomaly detectionspacecraft systemsmetricsthreshold methodologiestime-series data |
| spellingShingle | Marie Bieber Wim J. C. Verhagen Fabrice Cosson Bruno F. Santos Generic Diagnostic Framework for Anomaly Detection—Application in Satellite and Spacecraft Systems Aerospace anomaly detection spacecraft systems metrics threshold methodologies time-series data |
| title | Generic Diagnostic Framework for Anomaly Detection—Application in Satellite and Spacecraft Systems |
| title_full | Generic Diagnostic Framework for Anomaly Detection—Application in Satellite and Spacecraft Systems |
| title_fullStr | Generic Diagnostic Framework for Anomaly Detection—Application in Satellite and Spacecraft Systems |
| title_full_unstemmed | Generic Diagnostic Framework for Anomaly Detection—Application in Satellite and Spacecraft Systems |
| title_short | Generic Diagnostic Framework for Anomaly Detection—Application in Satellite and Spacecraft Systems |
| title_sort | generic diagnostic framework for anomaly detection application in satellite and spacecraft systems |
| topic | anomaly detection spacecraft systems metrics threshold methodologies time-series data |
| url | https://www.mdpi.com/2226-4310/10/8/673 |
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