Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis

By Shen Yin, Xiangping Zhu, Okyay Kaynak. IEEE transactions on industrial electronics (1982. Print), 2015

494 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a4d7637cac8b307a43fb0f21a85417cb8b160cbc

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In Control and Systems Engineering

In Mechanical Engineering

In Statistics and Probability

Citations in the corpus

Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems (2016, background): “DUE TO the increasing demands on system performance, production quality as well as economic operation and modern industrial processes are becoming more and more complicated and the degrees of automation of such processes are therefore significantly increasing [1]–[5].”
Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System (2015, background): “, chemical process [26]–[28], mechanical equipment [29]–[32], and networked system [33], [34].”
A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning (2019, methodology): “In the era of big data, a variety of advanced technologies and methods are used in fault diagnosis, such as Bayesian networks [6], deep learning [7]–[9], Support Vector Machine (SVM) [10], Extreme Learning Machine (ELM) [11], and so on [12]–[14], which remarkably enhance the diagnosis accuracy and e”
A Review on Recent Development of Spacecraft Attitude Fault Tolerant Control System (2016, background): “Besides, some extra effort has examined the relationship between feedback and FDD [12], differences between passive and active FTC design strategies [13], data driven-based FDD [14], [15], and FDD/FTC in flight control system [16], [17].”
Sequential Fault Diagnosis Based on LSTM Neural Network (2018, methodology): “[13] S. Yin, X. Zhu, and O. Kaynak, ‘‘Improved PLS focused on keyperformance-indicator-related fault diagnosis,’’ IEEE Trans.”
The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities (2021, unknown): “A variety of technologies used in fault diagnosis like Support Vector Machines [36], Bayesian Networks [37], Deep Learning [38]–[40], andmany others [41]–[44] are capable of enhanced fault diagnosis.”
Convergence of the recursive identification algorithms for multivariate pseudo‐linear regressive systems (2016, methodology): “In order to extract the useful information from the recorded process data, the model-based optimization framework [10, 11] and the data-driven framework [12, 13] have been established.”
Unknown Input Observer-Based Robust Fault Estimation for Systems Corrupted by Partially Decoupled Disturbances (2016, background): “During the last four decades, extensive results were reported on various analytical redundancy based fault diagnosis techniques [1-4] and their applications in aero systems, electro-mechanical systems, energy systems [5-8] and so forth.”