Research on Satellite Power Anomaly Detection Method Based on LSTM

Cheng, Fuqiang; Guo, Xiaohong; Qi, Yajie; Xu, Jingwen; Qiu, Wan; Zhang, Zhibao; Zhang, Weitao; Qi, Ningning

doi:10.1109/icpeca51329.2021.9362601

Cited by 7 publications

(2 citation statements)

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“…The discrete wavelet transform method was taken to extract data features, while the LSTM was employed as an intelligent classifier to perform prediction tasks. Cheng et al proposed an LSTM-based method to detect anomalies for satellite power systems, where the LSTM was conducted to establish a prediction model [17]. To detect vibration signal faults for the rotating machinery, the LSTM method was adopted as a classifier in Ref.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

Wang

Zhang

Wang

2023

Trans. Electr. Mach. Syst.

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The complex working conditions and nonlinear characteristics of the motor drive control system of industrial robots make it difficult to detect faults. In this paper, a deep learning-based observer, which combines the convolutional neural network (CNN) and the long short-term memory network (LSTM), is employed to approximate the nonlinear driving control system. CNN layers are introduced to extract dynamic features of the data, whereas LSTM layers perform time-sequential prediction of the target system. In terms of application, normal samples are fed into the observer to build an offline prediction model for the target system. The trained CNN-LSTM-based observer is then deployed along with the target system to estimate the system outputs. Online fault detection can be realized by analyzing the residuals. Finally, an application of the proposed fault detection method to a brushless DC motor drive system is given to verify the effectiveness of the proposed scheme. Simulation results indicate the impressive fault detection capability of the presented method for driving control systems of industrial robots.

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Section: Introductionmentioning

confidence: 99%

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

Wang

Zhang

Wang

2023

Trans. Electr. Mach. Syst.

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“…Those images contain waveforms or feature information of vibration signals with a fixed-length time window. In contrast, LSTM is likely to be adapted for dynamic monitoring [5], anomaly detection [22], and sequence prediction [13] of univariate time series of satellite components, which is less applied in fault diagnosis with multi-variable or 2D data. Whereas spacecraft have the characteristic of closed-loop propagation of faults, the above-mentioned papers focus primarily on the fault detection and diagnosis of the key parameters of a single component over time, while ignoring the impact of the jumping change in the parameters of other components at the moment fault occurs due to the closed-loop control.…”

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confidence: 99%

Intelligent fault diagnosis method of spacecraft control system based on sequence data-image mapping

Liang¹,

Liu²,

Liu³

et al. 2024

MFC

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<p style='text-indent:20px;'>Satellite networking, as the future development direction of aero-space, requires high-precision autonomous fault diagnosis capability for a single satellite. In this paper, aiming at the characteristics of closed-loop fault propagation and high data dimensionality of spacecraft control system, neural network algorithms are conducted to study the fault diagnosis of spacecraft high-dimensional coupled data. Based on the ground test data of a certain spacecraft, this paper converts the high-dimensional sequence data into grayscale images, and then uses Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to diagnose them respectively. The effectiveness of the methods in this paper is illustrated by comparing and validating them with three non-image-based machine learning algorithms, namely, K-NearestNeighbor, Bayesian classifier, and K-NearestNeighbor based on Principal Component Analysis.</p>

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Long Short Term Memory-based anomaly detection applied to an industrial dosing pump

et al. 2022

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Research on Satellite Power Anomaly Detection Method Based on LSTM

Cited by 7 publications

References 1 publication

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

Intelligent fault diagnosis method of spacecraft control system based on sequence data-image mapping

Long Short Term Memory-based anomaly detection applied to an industrial dosing pump

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