With the advancement of huge data generation and data handling capability, Machine Learning and Probabilistic modelling enables an immense opportunity to employ predictive analytics platform in high security critical industries namely data centers, electricity grids, utilities, airport etc. where downtime minimization is one of the primary objectives. This paper proposes a novel, complete architecture of an intelligent predictive analytics platform, Fault Engine, for huge device network connected with electrical/information flow. Three unique modules, here proposed, seamlessly integrate with available technology stack of data handling and connect with middleware to produce online intelligent prediction in critical failure scenarios. The Markov Failure module predicts the severity of a failure along with survival probability of a device at any given instances. The Root Cause Analysis model indicates probable devices as potential root cause employing Bayesian probability assignment and topological sort. Finally, a community detection algorithm produces correlated clusters of device in terms of failure probability which will further narrow down the search space of finding route cause. The whole Engine has been tested with different size of network with simulated failure environments and shows its potential to be scalable in real-time implementation.
An Intelligent Fault Diagnosis Method for General Aviation Aircraft Based on Multi-Fidelity Digital Twin and FMEA Knowledge Enhancement
Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；School of Mechanical Engineering, Zhejiang University（浙江大学机械工程学院）；School of Reliability and Systems Engineering, Beihang University（北京航空航天大学可靠性与系统工程学院）；Department of Mechanical Engineering, City University of Hong Kong（香港城市大学机械工程系）
An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications
Peking University（北京大学）；Peking University Information Technology Institute (Tianjin Binhai)（北京大学信息技术研究院（天津滨海））；Beijing University of Posts and Telecommunications（北京邮电大学）
Explainable Incipient Fault Detection Systems for Photovoltaic Panels
Kalasalingam Academy for Research and Education（卡萨斯林甘研究与教育学院）；GE Corporate Research Center（通用电气企业研究中心）；SINTEF；Norwegian University of Science and Technology（挪威科技大学）；SUNY Polytechnic Institute（纽约州立大学理工学院）
Hybrid Autoencoder-Based Framework for Early Fault Detection in Wind Turbines
Department of Computer Science and Engineering, Alliance University（计算机科学与工程系，联盟大学）；College of Engineering and IT, University of Dubai（工程与信息技术学院，迪拜大学）；Shiv Nadar University（Shiv Nadar大学）
