S
Steven X. Ding
Researcher at University of Duisburg-Essen
Publications - 564
Citations - 26174
Steven X. Ding is an academic researcher from University of Duisburg-Essen. The author has contributed to research in topics: Fault detection and isolation & Fault (power engineering). The author has an hindex of 67, co-authored 541 publications receiving 21539 citations. Previous affiliations of Steven X. Ding include University of Picardie Jules Verne & European Union.
Papers
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Book
Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools
TL;DR: This book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers.
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A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches
TL;DR: The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade.
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A Review on Basic Data-Driven Approaches for Industrial Process Monitoring
TL;DR: A basic data-driven design framework with necessary modifications under various industrial operating conditions is sketched, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.
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A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process
TL;DR: A comparison study on the basic data-driven methods for process monitoring and fault diagnosis (PM–FD) based on the original ideas, implementation conditions, off-line design and on-line computation algorithms as well as computation complexity are discussed in detail.
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An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
TL;DR: A two-stage learning method inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data for intelligent diagnosis of machines that reduces the need of human labor and makes intelligent fault diagnosis handle big data more easily.