With the ever increase of complexity and expense industrial systems, there is less tolerance for performance degradation, productivity decrease safety hazards, which greatly stimulates to detect identify any kinds potential abnormalities faults as early possible, implement realtime fault-tolerant operation minimizing degradation avoiding dangerous situations.During last four decades, fruitful results were reported about fault diagnosis control methods their applications in a variety...

Intelligent fault diagnosis is a promising tool to deal with mechanical big data due its ability in rapidly and efficiently processing collected signals providing accurate results. In traditional intelligent methods, however, the features are manually extracted depending on prior knowledge diagnostic expertise. Such processes take advantage of human ingenuity but time-consuming labor-intensive. Inspired by idea unsupervised feature learning that uses artificial intelligence techniques learn...

Data mining and analytics have played an important role in knowledge discovery decision making/supports the process industry over past several decades. As a computational engine to data analytics, machine learning serves as basic tools for information extraction, pattern recognition predictions. From perspective of learning, this paper provides review on existing applications The state-of-the-art are reviewed through eight unsupervised ten supervised algorithms, well application status...

This is the second-part paper of survey on fault diagnosis and fault-tolerant techniques, where methods applications are overviewed, respectively, from knowledge-based hybrid/active viewpoints. With aid first-part paper, review completes a whole overview techniques their applications. Comments advantages constraints various including model-based, signal-based, knowledge-based, also given. An overlook future development presented.

In this paper, two online schemes for an integrated design of fault-tolerant control (FTC) systems with application to Tennessee Eastman (TE) benchmark are proposed. Based on the data-driven proposed architecture whose core is observer/residual generator based realization Youla parameterization all stabilization controllers, FTC achieved by adaptive residual identification fault diagnosis relevant vectors, and iterative optimization method system performance enhancement. The effectiveness...

The remaining useful life (RUL) prediction of rolling element bearings has attracted substantial attention recently due to its importance for the bearing health management. exponential model is one most widely used methods RUL bearings. However, two shortcomings exist in model: 1) first predicting time (FPT) selected subjectively; and 2) random errors stochastic process decrease accuracy. To deal with these shortcomings, an improved proposed this paper. In model, adaptive FPT selection...

In this paper, problems of optimizing observer-based fault detection (FD) systems in the sense increasing robustness to unknown inputs and simultaneously enhancing sensitivity faults are studied. The core study is development an approach that solves four optimization problems. Different algorithms derived for application optimal selection post-filters as well filters, with without structure constraints. achieved results also reveal some interesting relationships among considered. Copyright ©...

Recently, to ensure the reliability and safety of high-speed trains, detection diagnosis faults (FDD) in traction systems have become an active issue transportation area over past two decades. Among these FDD methods, data-driven designs, that can be directly implemented without a logical or mathematical description systems, received special attention because their overwhelming advantages. Based on existing methods for first objective this paper is systematically review categorize most...

This paper investigates the problem of output feedback robust ℋ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control for a class nonlinear spatially distributed systems described by first-order hyperbolic partial differential equations (PDEs) with Markovian jumping actuator faults. The PDE are first expressed Takagi-Sugeno fuzzy models parameter uncertainties, and then, objective is to design reliable static controller guaranteeing...

In recent years, the analysis and synthesis of fuzzy-model-based nonlinear networked control systems (NCSs) have received increasing attention from both scientific industrial communities, a number significant results been proposed. This paper gives review on advances design NCSs with various network-induced limitations such as packet dropouts, time delays, signal quantization. With these constraints, developments filtering issues are surveyed in details, some essential technical difficulties...

In this paper, we first study a generalized canonical correlation analysis (CCA)-based fault detection (FD) method aiming at maximizing the detectability under an acceptable false alarm rate. More specifically, two residual signals are generated for detecting of faults in input and output subspaces, respectively. The minimum covariances achieved by taking between into account. Considering limited application scope CCA due to Gaussian assumption on process noises, FD technique combining with...

This "Special Section on Real-Time Fault Diagnosis and Fault-Tolerant Control" of the IEEE Transactions Industrial Electronics is motivated to provide a forum for academic industrial communities report recent theoretic/application results in real-time monitoring, diagnosis, fault-tolerant design, exchange ideas about emerging research direction this field. Twenty-three papers were eventually selected through strict peer-reviewed procedure, which represent most progress fault control their...

In this paper, a data-driven scheme of key performance indicator (KPI) prediction and diagnosis is developed for complex industrial processes. For static processes, KPI approach proposed in order to improve the performance. comparison with standard partial least squares (PLS) method, alternative significantly simplifies computation procedure. By means realization so-called left coprime factorization (LCF) process, efficient prediction, algorithms are dynamic respectively, without measurable...