Anomaly Detection of Control Moment Gyroscope Based on Working Condition Classification and Transfer Learning
The process of human exploration of the universe has accelerated, and aerospace technology has developed rapidly. The health management and prognosis guarantee of spacecraft systems has become an important basic technology. However, with thousands of telemetry data channels and massive data scales,...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/7/4259 |
| _version_ | 1797608382809505792 |
|---|---|
| author | Kuan Zhang Shuchen Wang Saijin Wang Qizhi Xu |
| author_facet | Kuan Zhang Shuchen Wang Saijin Wang Qizhi Xu |
| author_sort | Kuan Zhang |
| collection | DOAJ |
| description | The process of human exploration of the universe has accelerated, and aerospace technology has developed rapidly. The health management and prognosis guarantee of spacecraft systems has become an important basic technology. However, with thousands of telemetry data channels and massive data scales, spacecraft systems are increasingly complex. The anomaly detection that relied on simple threshold judgment and expert manual annotation in the past is no longer applicable. In addition, the particularity of the anomaly detection task leads to the lack of fault data for training. Therefore, a data-driven deep transfer learning-based approach is needed for rapid analysis and accurate detection of large-scale data. The control moment gyroscope (CMG) is a significant inertial actuator in the process of large-scale, long-life spacecraft in-orbit operation and mission execution. Its anomaly detection plays a major role in the prevention and elimination of early failures. Based on the research of SincNet and Long Short-Term Memory (LSTM) networks, this paper proposed a Sinc-LSTM neural network based on transfer learning and working condition classification for CMG anomaly detection. First, a two-stage pre-training method is proposed to alleviate the data imbalance, using the Mars Reconnaissance Orbiter (MRO) dataset and a satellite dataset from NASA. Second, the Sinc-LSTM network is designed to enhance the local fitting and long-period memory ability of the model for CMG time series data. Finally, a dynamic threshold judgment anomaly detection method based on working condition classification is designed to accommodate threshold changes for CMG full-cycle anomaly detection. The method is validated on the spacecraft CMG dataset. |
| first_indexed | 2024-03-11T05:42:43Z |
| format | Article |
| id | doaj.art-44ac5a8876c349ad964799cf89dd39a2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:42:43Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-44ac5a8876c349ad964799cf89dd39a22023-11-17T16:17:49ZengMDPI AGApplied Sciences2076-34172023-03-01137425910.3390/app13074259Anomaly Detection of Control Moment Gyroscope Based on Working Condition Classification and Transfer LearningKuan Zhang0Shuchen Wang1Saijin Wang2Qizhi Xu3Beijing Aerospace Control Center, Beijing 100094, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaBeijing Aerospace Control Center, Beijing 100094, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaThe process of human exploration of the universe has accelerated, and aerospace technology has developed rapidly. The health management and prognosis guarantee of spacecraft systems has become an important basic technology. However, with thousands of telemetry data channels and massive data scales, spacecraft systems are increasingly complex. The anomaly detection that relied on simple threshold judgment and expert manual annotation in the past is no longer applicable. In addition, the particularity of the anomaly detection task leads to the lack of fault data for training. Therefore, a data-driven deep transfer learning-based approach is needed for rapid analysis and accurate detection of large-scale data. The control moment gyroscope (CMG) is a significant inertial actuator in the process of large-scale, long-life spacecraft in-orbit operation and mission execution. Its anomaly detection plays a major role in the prevention and elimination of early failures. Based on the research of SincNet and Long Short-Term Memory (LSTM) networks, this paper proposed a Sinc-LSTM neural network based on transfer learning and working condition classification for CMG anomaly detection. First, a two-stage pre-training method is proposed to alleviate the data imbalance, using the Mars Reconnaissance Orbiter (MRO) dataset and a satellite dataset from NASA. Second, the Sinc-LSTM network is designed to enhance the local fitting and long-period memory ability of the model for CMG time series data. Finally, a dynamic threshold judgment anomaly detection method based on working condition classification is designed to accommodate threshold changes for CMG full-cycle anomaly detection. The method is validated on the spacecraft CMG dataset.https://www.mdpi.com/2076-3417/13/7/4259control moment gyroscopeabnormal detectionLSTMtransfer learning |
| spellingShingle | Kuan Zhang Shuchen Wang Saijin Wang Qizhi Xu Anomaly Detection of Control Moment Gyroscope Based on Working Condition Classification and Transfer Learning Applied Sciences control moment gyroscope abnormal detection LSTM transfer learning |
| title | Anomaly Detection of Control Moment Gyroscope Based on Working Condition Classification and Transfer Learning |
| title_full | Anomaly Detection of Control Moment Gyroscope Based on Working Condition Classification and Transfer Learning |
| title_fullStr | Anomaly Detection of Control Moment Gyroscope Based on Working Condition Classification and Transfer Learning |
| title_full_unstemmed | Anomaly Detection of Control Moment Gyroscope Based on Working Condition Classification and Transfer Learning |
| title_short | Anomaly Detection of Control Moment Gyroscope Based on Working Condition Classification and Transfer Learning |
| title_sort | anomaly detection of control moment gyroscope based on working condition classification and transfer learning |
| topic | control moment gyroscope abnormal detection LSTM transfer learning |
| url | https://www.mdpi.com/2076-3417/13/7/4259 |
| work_keys_str_mv | AT kuanzhang anomalydetectionofcontrolmomentgyroscopebasedonworkingconditionclassificationandtransferlearning AT shuchenwang anomalydetectionofcontrolmomentgyroscopebasedonworkingconditionclassificationandtransferlearning AT saijinwang anomalydetectionofcontrolmomentgyroscopebasedonworkingconditionclassificationandtransferlearning AT qizhixu anomalydetectionofcontrolmomentgyroscopebasedonworkingconditionclassificationandtransferlearning |