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1. An Adversarial Learning Approach for Machine Prognostic Health Management

   https://doi.org/10.1109/HPBDIS.2019.8735480  

   Huang, Yu ; Tang, Yufei ; VanZwieten, James ; Liu, Jianxun ; Xiao, Xiaocong ( May 2019 , 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS))

   Achieving accurate remaining useful life (RUL) prediction for prognostic and health management (PHM) depends upon sufficient prior degradation apprehension of critical components within the system. However, such prior knowledge is not always readily available in practice. We alleviate this shortcoming by proposing a novel data-driven framework that is capable of providing accurate RUL prediction without the need for any prior failure threshold knowledge. Correlative and monotonic metrics are utilized to identify critical features throughout the degradation progress. Subsequently, we append one-hot health state indicators to extracted degrading features, which are utilized together as adversarial training data for a Long Short-Term Memory (LSTM) network-based model. Finally, we utilize a fully connected layer to project the LSTM outputs into the parameters of a Gaussian mixture model (GMM) in conjunction with a categorical distribution, from which the long-term degradation progress is sampled. We verify the performance of the proposed framework using aeroengine health data simulated by Modular Aero-Propulsion System Simulation (MAPSS), and the results demonstrate that significant performance improvement can be achieved for long-term degradation progress and RUL prediction tasks. 

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2. Prognostic Health Monitoring of DC Microgrid with Fault Detection and Localization using Machine Learning Techniques

   https://doi.org/10.1109/ECCE53617.2023.10362739  

   Bohara, Bharat ; Krishnamoorthy, Harish S. ( October 2023 , IEEE)

   DC microgrids incorporate several converters for distributed energy resources connected to different passive and active loads. The complex interactions between the converters and components and their potential failures can significantly affect the grids' resilience and health; hence, they must be continually assessed and monitored. This paper presents a machine learning-assisted prognostic health monitoring (PHM) and diagnosis approach, enabling progressive interactions between the converters at multiple nodes to dynamically examine the grid's (or micro-grid's) health in real time. By measuring the resulting impedance at the power converters' terminals at various grid nodes, a neural network-based classifier helps detect the grid's health condition and identify the potential fault-prone zones, along with the type and location of the fault type in the grid topology. For a faulty grid, a Naive Bayes and a support vector machine (SVM)-based classifiers are used to locate and identify the faulty type, respectively. A separate neural network-based regression model predicts the source power delivered and the loads at different terminals in a healthy grid network. The proposed concepts are supported by detailed analysis and simulation results in a simple four-terminal DC microgrid topology and a standard IEEE 5 Bus system. 

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3. Causal discovery in the presence of missing data

   Ruibo Tu, Cheng Zhang ( April 2019 , Proceedings of Machine Learning Research)

   Missing data are ubiquitous in many domain such as healthcare. When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be different from those in the complete data generated by the underlying causal process.Consequently, simply applying existing causal discovery methods to the observed data may lead to wrong conclusions. In this paper, we aim at developing a causal discovery method to recover the underlying causal structure from observed data that are missing under different mechanisms, including missing completely at random (MCAR),missing at random (MAR), and missing not at random (MNAR). With missingness mechanisms represented by missingness graphs (m-graphs),we analyze conditions under which additional correction is needed to derive conditional independence/dependence relations in the complete data. Based on our analysis, we propose Miss-ing Value PC (MVPC), which extends the PC algorithm to incorporate additional corrections.Our proposed MVPC is shown in theory to give asymptotically correct results even on data that are MAR or MNAR. Experimental results on both synthetic data and real healthcare applications illustrate that the proposed algorithm is able to find correct causal relations even in the general case of MNAR. 

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4. Finite horizon constrained control and bounded-error estimation in the presence of missing data

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   Rutledge, Kwesi ; Yong, Sze Zheng ; Ozay, Necmiye ( May 2020 , Nonlinear Analysis: Hybrid Systems)

   null (Ed.)
5. On Two‐Stage Estimation of the Spectral Density with Assigned Risk in Presence of Missing Data

   https://doi.org/10.1111/jtsa.12435  

   Efromovich, Sam ( November 2018 , Journal of Time Series Analysis)