Zhiwei Gao

A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. 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. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis

This review paper is to give a full picture of fault detection and diagnosis (FDD) in complex systems from the perspective of data processing. As a matter of fact, an FDD system is a data-processing system on the basis of information redundancy, in which the data and humans understanding of the data are two fundamental elements. Humans understanding may be an explicit input-output model representing the relationship among the systems variables. It may also be represented as knowledge implicitly (e.g., the connection weights of a neural network). Therefore, FDD is done through some kind of modeling, signal processing, and intelligence computation. In this paper, a variety of FDD techniques are reviewed within the unified data-processing framework to give a full picture of FDD and achieve a new level of understanding. According to the types of data and how the data are processed, the FDD methods are classified into three categories: model-based online data-driven methods, signal-based methods, and knowledge-based history data-driven methods. An outlook to the possible evolution of FDD in industrial automation, including the hybrid FDD and the emerging networked FDD, are also presented to reveal the future development direction in this field.

A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches

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

Unknown Input Observer-Based Robust Fault Estimation for Systems Corrupted by Partially Decoupled Disturbances

Robust fault estimation plays an important role in real-time monitoring, diagnosis, and fault-tolerance control. Accordingly, this paper aims to develop an effective fault estimation technique to simultaneously estimate the system states and the concerned faults, while minimizing the influences from process/sensor disturbances. Specifically, an augmented system is constructed by forming an augmented state vector composed of the system states and the concerned faults. Next, an unknown input observer (UIO) is designed for the augmented system by decoupling the partial disturbances and attenuating the disturbances that cannot be decoupled, leading to a simultaneous estimate of the system states and the concerned faults. In order to be close to the practical engineering situations, the process disturbances in this study are assumed not to be completely decoupled. In the first part of this paper, the existence condition of such an UIO is proposed to facilitate the fault estimation for linear systems subjected to process disturbances. In the second part, robust fault estimation techniques are addressed for Lipschitz nonlinear systems subjected to both process and sensor disturbances. The proposed technique is finally illustrated by the simulation studies of a three-shaft gas turbine engine and a single-link flexible joint robot.

Real-Time Fault Diagnosis and Fault-Tolerant Control

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

Fault Estimation and Fault-Tolerant Control for Discrete-Time Dynamic Systems

In this paper, a novel discrete-time estimator is proposed, which is employed for simultaneous estimation of system states, and actuator/sensor faults in a discrete-time dynamic system. The existence of the discrete-time simultaneous estimator is proven mathematically. The systematic design procedure for the derivative and proportional observer gains is addressed, enabling the estimation error dynamics to be internally proper and stable, and robust against the effects from the process disturbances, measurement noise, and faults. Based on the estimated fault signals and system states, a discrete-time fault-tolerant design approach is addressed, by which the system may recover the system performance when actuator/sensor faults occur. Finally, the proposed integrated discrete-time fault estimation and fault-tolerant control technique is applied to the vehicle lateral dynamics, which demonstrates the effectiveness of the developed techniques.

Data-Driven Time Discrete Models for Dynamic Prediction of the Hot Metal Silicon Content in the Blast Furnace—A Review

A review of black-box models for short-term time-discrete prediction of the silicon content of hot metal produced in blast furnaces is presented. The review is primarily focused on work presented in journal papers, but still includes some early conference papers (published before 1990) which have a clear contribution to the field. Linear and nonlinear models are treated separately, and within each group a rough subdivision according to the model type is made. Within each subsection the models are treated (almost) chronologically, presenting the principle behind the modeling approach, the signals used and the main findings in terms of accuracy and usefulness. Finally, in the final section the approaches are discussed and some potential lines of future research are proposed. In an Appendix , a list of commonly used input and output variables in the models is presented.

Robust finite-time fault estimation for stochastic nonlinear systems with Brownian motions

Motivated by real-time monitoring and fault diagnosis for complex systems, the presented paper aims to develop effective fault estimation techniques for stochastic nonlinear systems subject to partially decoupled unknown input disturbances and Brownian motions. The challenge of the research is how to ensure the robustness of the proposed fault estimation techniques against stochastic Brownian perturbations and additive process disturbances, and provide a rigorous mathematical proof of the finite-time input-to-stabilization of the estimation error dynamics. In this paper, stochastic input-to-state stability and finite-time stochastic input-to-state stability of stochastic nonlinear systems are firstly investigated based on Lyapunov theory, leading to simple and straightforward criteria. By integrating augmented system approach, unknown input observer technique, and finite-time stochastic input-to-state stability theory, a highly-novel fault estimation technique is proposed. The convergence of the estimation error with respect to un-decoupled unknown inputs and Brownian perturbations is proven by using the derived stochastic input-to-state stability and finite-time stochastic input-to-state stability theorems. Based on linear matrix inequality technique, the robust observer gains can be obtained in order to achieve both stability and robustness of the error dynamic. Finally, the effectiveness of the proposed fault estimation techniques is demonstrated by the detailed simulation studies using a robotic system and a numerical example.

Takagi–Sugeno Fuzzy Model Based Fault Estimation and Signal Compensation With Application to Wind Turbines

In response to the high demand of the operation reliability by implementing real-time monitoring and system health management, a robust fault estimation and fault-tolerant control approach is proposed for Takagi–Sugeno fuzzy systems in this study, by integrating the augmented system method, unknown input fuzzy observer design, linear matrix inequality optimization, and signal compensation techniques. Specifically, a fuzzy augmented system method is used to construct an augmented plant with the concerned faults and system states being the augmented states. An unknown input fuzzy observer technique is thus utilized to estimate the augmented states and decouple unknown inputs that can be decoupled. A linear matrix inequality approach is further addressed to ensure the global stability of the estimation error dynamics and attenuate the influences from the unknown inputs that cannot be decoupled. As a result, the robust estimates of the concerned faults and system states can be obtained simultaneously. Based on the fault estimates, a signal compensation scheme is developed to remove the effects of the faults on the system dynamics and outputs, leading to a stable dynamic satisfying the expected performance. Finally, the effectiveness of the proposed Takagi–Sugeno model based fault estimation and signal compensation algorithms is demonstrated by a case study on a 4.8-MW wind turbine benchmark system.

Estimation and Compensation for Lipschitz Nonlinear Discrete-Time Systems Subjected to Unknown Measurement Delays

Unknown measurement delays usually degrade system performance and even damage a system under output feedback control, which motivate us to develop an effective method to attenuate or offset the adverse effect from the measurement delays. In this paper, an augmented observer is proposed for discrete-time Lipschitz nonlinear systems subjected to unknown measurement delays, enabling a simultaneous estimation for system states and perturbed terms caused by output delays. On the basis of the estimates, a sensor compensation technique is addressed to remove the influence from the measurement delays to the system performance. Furthermore, an integrated robust estimation and compensation technique is proposed to decouple constant piecewise disturbances, attenuate other disturbances/noise, and offset the adverse effect caused by the measurement delays. The proposed methods are applied to a two-stage chemical reactor with delayed recycle and to an electromechanical servosystem, which demonstrate the effectiveness of the present techniques.