Temporal dependence Mahalanobis distance for anomaly detection in multivariate spacecraft telemetry series

Pang, Jingyue; Liu, Datong; Yu, Peng; Peng, Xiyuan

doi:10.1016/j.isatra.2023.06.002

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…The different dimensions of the feature matrix representing the shield tunneling scenarios have different meanings. The Mahalanobis distance takes into account the differences between different dimensions when calculating matrix similarity [35]. Therefore, we use the Mahalanobis distance between feature matrices of different shield tunneling scenarios as a measure of similarity for shield tunneling scenarios.…”

Section: Feature Presentation and Identification Of Shield Tunneling ...mentioning

confidence: 99%

Anomaly Detection and Identification Method for Shield Tunneling Based on Energy Consumption Perspective

Hu,

Zhang,

2024

Applied Sciences

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Various abnormal scenarios might occur during the shield tunneling process, which have an impact on construction efficiency and safety. Existing research on shield tunneling construction anomaly detection typically designs models based on the characteristics of a specific anomaly, so the scenarios of anomalies that can be detected are limited. Therefore, the research objective of this article is to establish an accurate anomaly detection model with generalization and identification capabilities on multiple types of abnormal scenarios. Inspired by energy dissipation theory, this paper innovatively detects various anomalies in the shield tunneling process from the perspective of energy consumption and designs the AD_SI model (Anomaly Detection and Scenario Identification model of shield tunneling) based on machine learning. The AD_SI model first monitors the shield machine’s energy consumption status based on the VAE-LSTM (Variational Autoencoder–Long Short-Term Memory) algorithm with a dynamic threshold, thereby detecting abnormal sections. Secondly, the AD_SI model uses the correlation of construction parameters to represent different known scenarios and further clarifies scenarios of the abnormal sections, thus achieving anomaly identification. The application of the AD_SI model in a shield tunneling construction project demonstrates its capability to accurately detect and identify different anomalies, with a recall value exceeding 0.9 and F1 exceeding 0.8, thereby providing guidance for accurately detecting multiple types anomaly scenarios in practical applications.

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Section: Feature Presentation and Identification Of Shield Tunneling ...mentioning

confidence: 99%

Anomaly Detection and Identification Method for Shield Tunneling Based on Energy Consumption Perspective

Hu,

Zhang,

2024

Applied Sciences

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“…The spacecraft is in normal operation most of the time in orbit, which leads to the lack of abnormal data. In the absence of abnormal data, unknown anomaly detection is an urgent problem to be solved [8].…”

Section: Introductionmentioning

confidence: 99%

Control Moment Gyroscope Anomaly Detection Based on Sparse Autoencoder

Zheng,

Dou,

Cui

et al. 2024

J. Phys.: Conf. Ser.

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The Control Moment Gyroscope (CMG) is an important executing part of spacecraft, and its anomaly detection has a key influence on the reliability of spacecraft. With the rapid growth of the number of spacecraft and telemetry data, data-driven spacecraft anomaly detection methods have received extensive attention, and such methods may become an important means of spacecraft anomaly detection. However, at present, the spacecraft is faced with the problem of unknown anomalies and lack of abnormal data during orbit. To solve this problem, a sparse autoencoder (SAE) based CMG anomaly detection method was proposed, which takes the telemetry signal of CMG as the object and lacks the sample of anomalous telemetry signal in training data. Firstly, data sample selection and processing were carried out to normalize the data of each telemetry value. Secondly, the SAE model was constructed, and the model was trained with the training set data. Then the sample reconstruction error of the test set was calculated to complete the anomaly detection of the CMG. Finally, it was compared with the traditional artificial threshold monitoring method. The results indicated that the CMG anomaly detection method based on SAE can effectively detect the anomaly samples of the CMG telemetry data, and has better anomaly detection performance than the traditional artificial threshold monitoring method.

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Heat dissipation capacity prediction and cooling adjustment using digital twin technologies for a typical space station cabin

Yuan,

Li,

Sun

2024

J Therm Anal Calorim

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Temporal dependence Mahalanobis distance for anomaly detection in multivariate spacecraft telemetry series

Cited by 7 publications

References 25 publications

Anomaly Detection and Identification Method for Shield Tunneling Based on Energy Consumption Perspective

Anomaly Detection and Identification Method for Shield Tunneling Based on Energy Consumption Perspective

Control Moment Gyroscope Anomaly Detection Based on Sparse Autoencoder

Heat dissipation capacity prediction and cooling adjustment using digital twin technologies for a typical space station cabin

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