Leo H. Chiang

TL;DR: The appearance of this book is quite timely as it provides a much needed state-of-the-art exposition on fault detection and diagnosis, a topic of much interest to industrialists.

TL;DR: In this article, the authors developed an information criterion that automatically determines the order of the dimensionality reduction for FDA and DPLS, and show that FDA is more proficient than PCA for diagnosing faults, both theoretically and by applying these techniques to simulated data collected from the Tennessee Eastman chemical plant simulator.

TL;DR: With key variables selected by genetic algorithms and the contribution charts, SVM and PSVM outperformed FDA and demonstrated the advantage of using nonlinear technique when data are overlapped and the effectiveness of the proposed approach is increased in PSVM.

TL;DR: A residual-based CVA statistic proposed in this paper gave the best overall sensitivity and promptness, but the initially proposed threshold for the statistic lacked robustness, so increasing the threshold to achieve a specified missed detection rate was motivated.

TL;DR: This paper presents a meta-analysis of the Tennessee Eastman Process, an attempt to evaluate the methodology and techniques used in this type of analysis, as well as some of the approaches used in other approaches.