## Mathematical Notations  
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Anomaly Detection and Identification in Satellite Telemetry Data Based on Pseudo-Period. _Appl. Sci._**2019**, _10_, 103. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities. _Prog. Aerosp. Sci._**2019**, _105_, 60-73. [CrossRef]
* [PERSON] et al. (2009) [PERSON]; [PERSON]; [PERSON] Anomaly Detection: A Survey. _ACM Comput. Surv._**2009**, _41_, 1-58. [CrossRef]
* [PERSON] and [PERSON] (2015) [PERSON]; [PERSON] Survey on Fault Detection, Isolation, and Recovery Strategies in the Space Domain. _J. Aerosp. Inf. Syst._**2015**, _12_, 235-256. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] Enhancement of incipient fault detection and estimation using the multivariate Kullback-Leibler Divergence. In Proceedings of the 2016 24 th European Signal Processing Conference (EUSIPCO), Budapest, Hungary, 28 August-2 September 2016; pp. 1408-1412. * [PERSON] (2012) [PERSON]; [PERSON] Kullback-Leibler Divergence Measure for Multivariate Skew-Normal Distributions. _Entropy_**2012**, _14_, 1606-1626. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep PCA Based Real-Time Incipient Fault Detection and Diagnosis Methodology for Electrical Drive in High-Speed Trains. _IEEE Trans. Veh. Technol._**2018**, _67_, 4819-4830. [CrossRef]
* [PERSON] and [PERSON] (2007) [PERSON]; [PERSON] [PERSON] HyDE--A General Framework for Stochastic and Hybrid Model-based Diagnosis. _Proc. DX_**2007**, \(7\), 162-169. * [PERSON] et al. (1997) [PERSON]; [PERSON]; [PERSON] QSI's Integrated Diagnostics Toolset. In Proceedings of the 1997 IEEE Autotestcon Proceedings AUTOTESTCON'97, Anaheim, CA, USA, 22-25 September 1997; pp. 408-421. * [PERSON] and [PERSON] (2008) [PERSON]; [PERSON] Costs and Benefits of Model-based Diagnosis. In Proceedings of the 2008 IEEE Aerospace Conference, Big Sky, MT, USA, 1-8 March 2008; pp. 1-14. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification. _IEEE Trans. Ind. Electron._**2019**, _66_, 509-518. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Empowering Predictive Maintenance: A Hybrid Method to Diagnose Abnormal Situations. _Appl. Sci._**2020**, _10_, 6929. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series. _Sensors_**2018**, _18_, 967. [CrossRef]
* [PERSON] et al. (2009) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] General Purpose Data-Driven System Monitoring for Space Operations--AIAA Infotech@Aerospace Conference (AIAA). In Proceedings of the Aiaa Infotech, Seattle, WA, USA, 6-9 April 2009; pp. 26-44. * [PERSON] et al. (2009) [PERSON]; [PERSON]; [PERSON] Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring. _J. Aeros. Comp. Inf. Com._**2009**, \(6\), 464-482. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON] Anomaly detection based on uncertainty fusion for univariate monitoring series. _Measurement_**2017**, _95_, 280-292. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding. In Proceedings of the 24 th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK, 19-23 August 2018; pp. 387-395. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Detecting Anomalies in Space using Multivariate Convolutional LSTM with Mixtures of Probabilistic PCA. In Proceedings of the 25 th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 4-8 August 2019; pp. 2123-2133. * [PERSON] et al.