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, Qiong Yang, Guotong Li, Jiaxing Leng
Computational Intelligence and Neuroscience, Volume 2021, pp 1-15; https://doi.org/10.1155/2021/1303936

Abstract:
Timely detection and treatment of possible incipient faults in satellites will effectively reduce the damage and harm they could cause. Although much work has been done concerning fault detection problems, the related questions about satellite incipient faults are little addressed. In this paper, a new satellite incipient fault detection method was proposed by combining the ideas of deviation in unsupervised fault detection methods and classification in supervised fault detection methods. First, the proposed method uses dynamic linear discriminant analysis (LDA) to find an optimal projection vector that separates the in-orbit data from the normal historical data as much as possible. Second, under the assumption that the parameters obey a multidimensional Gaussian distribution, it applies the normal historical data and the optimal projection vector to build a normal model. Finally, it employs the noncentral F-distribution to test whether a fault has occurred. The proposed method was validated using a numerical simulation case and a real satellite fault case. The results show that the method proposed in this paper is more effective at detecting incipient faults than traditional methods.
Automation and Remote Control, Volume 82, pp 1293-1320; https://doi.org/10.1134/s0005117921080014

Abstract:
We survey the progress in data mining methods for spacecraft health monitoring. The main emphasis is placed on the analysis of telemetry data enabling the identification of spacecraft states that are atypical during normal operation and the prediction of possible failures in the operation of the spacecraft or its components. The main stages required for the creation of general-purpose spacecraft state monitoring systems are considered; methods for detecting anomalies in telemetry data taking into account the specific features of the spacecraft are presented in detail; and publications on this topic known to the authors are analyzed. Examples of the implementation of such systems in flight control centers of various countries are given. The promising areas of development of methods for analyzing the technical state of complex systems relevant for solving problems in space technology are discussed, and the main factors that hinder the development of machine learning methods for analyzing telemetry data are noted.
Junfu Chen, Xiaodong Zhao, Dechang Pi
Aircraft Engineering and Aerospace Technology, Volume 93, pp 1085-1096; https://doi.org/10.1108/aeat-09-2019-0185

Abstract:
Purpose The purpose of this paper is to ensure the stable operation of satellites in orbit and to assist ground personnel in continuously monitoring the satellite telemetry data and finding anomalies in advance, which can improve the reliability of satellite operation and prevent catastrophic losses. Design/methodology/approach This paper proposes a deep auto-encoder (DAE) satellite anomaly advance warning framework for satellite telemetry data. Firstly, this study performs grey correlation analysis, extracts important feature attributes to construct feature vectors and builds the variational auto-encoder with bidirectional long short-term memory generative adversarial network discriminator (VAE/BLGAN). Then, the Mahalanobis distance is used to measure the reconstruction score of input and output. According to the periodic characteristic of satellite operation, a dynamic threshold method based on periodic time window is proposed. Satellite health monitoring and advance warning are achieved using reconstruction scores and dynamic thresholds. Findings Experiment results indicate DAE methods can probe that satellite telemetry data appear abnormal, trigger a warning before the anomaly occurring and thus allow enough time for troubleshooting. This paper further verifies that the proposed VAE/BLGAN model has stronger data learning ability than other two auto-encoder models and is sensitive to satellite monitoring data. Originality/value This paper provides a DAE framework to apply in the field of satellite health monitoring and anomaly advance warning. To the best of the authors’ knowledge, this is the first paper to combine DAE methods with satellite anomaly detection, which can promote the application of artificial intelligence in spacecraft health monitoring.
, Yusuke Fukushima, Chun Fui Liew, Yuki Sakai, Yukihito Yamaguchi
Lecture Notes in Mechanical Engineering pp 129-141; https://doi.org/10.1007/978-981-15-9199-0_13

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, , Zhiyuan Wu, Xiaodong Zhao, Yue Pan, Qiang Zhang
Published: 11 December 2020
Acta Astronautica, Volume 180, pp 232-242; https://doi.org/10.1016/j.actaastro.2020.12.012

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, Michael Farnsworth, Richard McWilliam, John Erkoyuncu
Published: 10 September 2020
Annual Reviews in Control, Volume 50, pp 13-28; https://doi.org/10.1016/j.arcontrol.2020.08.003

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, Roopak Sinha, Stephen G. MacDonell
Journal of Systems and Software, Volume 168; https://doi.org/10.1016/j.jss.2020.110638

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Published: 30 October 2019
by MDPI
Abstract:
Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.
Jian-Ming Bai, Guang-She Zhao,
Published: 10 September 2019
Neural Computing and Applications, Volume 32, pp 14347-14358; https://doi.org/10.1007/s00521-019-04478-1

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Xu Kang, Dechang Pi
Aircraft Engineering and Aerospace Technology, Volume 90, pp 435-451; https://doi.org/10.1108/aeat-08-2016-0130

Abstract:
Purpose The purpose of this paper is to detect the occurrence of anomaly and fault in a spacecraft, investigate various tendencies of telemetry parameters and evaluate the operation state of the spacecraft to monitor the health of the spacecraft. Design/methodology/approach This paper proposes a data-driven method (empirical mode decomposition-sample entropy-principal component analysis [EMD-SE-PCA]) for monitoring the health of the spacecraft, where EMD is used to decompose telemetry data and obtain the trend items, SE is utilised to calculate the sample entropies of trend items and extract the characteristic data and squared prediction error and statistic contribution rate are analysed using PCA to monitor the health of the spacecraft. Findings Experimental results indicate that the EMD-SE-PCA method could detect characteristic parameters that appear abnormally before the anomaly or fault occurring, could provide an abnormal early warning time before anomaly or fault appearing and summarise the contribution of each parameter more accurately than other fault detection methods. Practical implications The proposed EMD-SE-PCA method has high level of accuracy and efficiency. It can be used in monitoring the health of a spacecraft, detecting the anomaly and fault, avoiding them timely and efficiently. Also, the EMD-SE-PCA method could be further applied for monitoring the health of other equipment (e.g. attitude control and orbit control system) in spacecraft and satellites. Originality/value The paper provides a data-driven method EMD-SE-PCA to be applied in the field of practical health monitoring, which could discover the occurrence of anomaly or fault timely and efficiently and is very useful for spacecraft health diagnosis.
Lei Song, Lili Guo, Huiping Wang, Shilong Yang, Shan Jin, Jiangyong Duan, Lele Xu
2017 International Conference on Dependable Systems and Their Applications (DSA) pp 103-109; https://doi.org/10.1109/dsa.2017.25

Abstract:
Anomaly detection technique is important for finding abnormal parameters and potential faults of equipment. Under the influences of operating instructions, environment conditions and equipment performance, the parameters of space payload fluctuate obviously and traditional threshold value method is unable to ensure the accuracy of anomaly detection. The paper proposed a novel anomaly detection method based on unsupervised learning and time series analysis, thus a new perspective of data correlation and data evolution method are achieved. The proposed method includes offline training process and online detecting process. The offline training process utilizes amounts of normal sampling to establish the normal state model based on multivariate state estimation technical (MSET). To guarantee the accuracy of normal state model, self-organizing feature map (SOM) is introduced to optimize modeling accuracy of memory matrix establishment for MSET, thus the normal model established in offline training process could cover overall working conditions. In online detecting process, the established normal model based on MSET could predict the objective parameter and the deviation of actual data and predict data could reflect the abnormal state of equipment. Finally, the effectiveness of the method is verified by experiments. The experiments result shows that the proposed method could detect the abnormal state of space payload accurately and rapidly.
, Jiusheng Chen, Quan Gan
Journal of Electrical and Computer Engineering, Volume 2017, pp 1-8; https://doi.org/10.1155/2017/4890921

Abstract:
Thousands of flights datasets should be analyzed per day for a moderate sized fleet; therefore, flight datasets are very large. In this paper, an improved kernel principal component analysis (KPCA) method is proposed to search for signatures of anomalies in flight datasets through the squared prediction error statistics, in which the number of principal components and the confidence for the confidence limit are automatically determined by OpenMP-based K-fold cross-validation algorithm and the parameter in the radial basis function (RBF) is optimized by GPU-based kernel learning method. Performed on Nvidia GeForce GTX 660, the computation of the proposed GPU-based RBF parameter is 112.9 times (average 82.6 times) faster than that of sequential CPU task execution. The OpenMP-based K-fold cross-validation process for training KPCA anomaly detection model becomes 2.4 times (average 1.5 times) faster than that of sequential CPU task execution. Experiments show that the proposed approach can effectively detect the anomalies with the accuracy of 93.57% and false positive alarm rate of 1.11%.
Takehisa Yairi, , Tetsuo Oda, Yuta Nakajima, Naoki Nishimura, Noboru Takata
IEEE Transactions on Aerospace and Electronic Systems, Volume 53, pp 1384-1401; https://doi.org/10.1109/taes.2017.2671247

Abstract:
In the operation of artificial satellites, it is very important to monitor the health status of the systems and detect any symptoms of anomalies in the housekeeping data as soon as possible. Recently, the data-driven approach to the system monitoring problem, in which statistical machine learning techniques are applied to the large amount of measurement data collected in the past, has attracted considerable attention. In this paper, we propose a new data-driven health monitoring and anomaly detection method for artificial satellites based on probabilistic dimensionality reduction and clustering, taking into consideration the miscellaneous characteristics of the spacecraft housekeeping data. We applied our method to the telemetry data of the small demonstration satellite 4 (SDS-4) of the Japan Aerospace Exploration Agency (JAXA) and evaluated its effectiveness. The results show that the proposed system provides satellite operators with valuable information for understanding the health status of the system and inferring the causes of anomalies.
Jiusheng Chen, Xiaoyu Zhang, Yuan Gao
Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, Volume 230, pp 651-660; https://doi.org/10.1177/0959651816643670

Abstract:
For commercial aircraft, real-time fault detection is essential for condition monitoring of rotating engine components, which can improve aviation safety and reduce maintenance cost for airline companies. In this article, based on the adaptive kernel principal component analysis method, a real-time fault detection algorithm is proposed for turbine engine disk condition monitoring. A sample reduction strategy based on the k-nearest neighbors method is presented to speed up the kernel principal component analysis approach while still guaranteeing correct results. To efficiently detect fault, the fault detection model is updated timely to suit the working process of turbine engine disk. Sample clusters are obtained through the k-mean method, and the parameter of the kernel function is adaptively adjusted by minimizing the within-cluster distance and maximizing the between-cluster distance in the feature space. Experiments have demonstrated the superiority of the proposed approach in fault detection for turbine engine disk.
, , Emil M. Petriu, Stefan Preitl
Journal of Aerospace Information Systems, Volume 11, pp 551-564; https://doi.org/10.2514/1.i010154

Abstract:
This paper proposes a novel iterative data-driven algorithm for the data-driven tuning of controllers for nonlinear systems. The iterative data-driven algorithm uses an experiment-based solving of the optimization problems for nonlinear processes, with linear controllers accounting for actuator constraints in terms of a quadratic penalty function approach. A neural network-based identification provides the gradient information used in the search algorithm for controller tuning and ensures a reduced sensitivity with respect to the controller parameters. A case study dealing with the data-driven controller tuning for the angular position control of a nonlinear aerodynamic system is included to validate the new iterative data-driven algorithm.
Silvano P. Colombano, Liljana Spirkovska, Vijayakumar Baskaran, Gordon Aaseng, Robert McCann, John Ossenfort, Irene Smith, David Iverson, Mark Schwabacher
AIAA SPACE 2013 Conference and Exposition; https://doi.org/10.2514/6.2013-5319

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