다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

A most critical and important issue surrounding the design of automatic control systems with the successively increasing complexity is guaranteeing a high system performance over a wide operating range and meeting the requirements on system reliability and dependability. As one of the key technologies for the problem solutions, advanced fault detection and identification (FDI) technology is receiving considerable attention. The objective of 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.

Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools

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.

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

Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

In this paper, the design of observers is further investigated via the parameterization technique. Two new observer parameterizations are addressed related to nonminimal reduced-order state observers. The known results in the references available can be obtained as particular results from the present parameterizations. For plants with input disturbances, parameterizations of estimation error dynamics are derived, providing useful tools for the design of robust optimal observers. An illustrative example is given for describing the parameterization procedure.

On parameterization design for linear observers

Using coprime fractional techniques and H∞-norms, the performance sensitivity of linear systems with five types of simultaneous structural uncertainty is analyzed. Sufficient conditions for the robust stability are derived. Also, the perturbed upper bounds for the sensitivity function matrix and the closed-loop transfer function matrix are presented.

Performance sensitivity analysis for linear systems with five types of structural uncertainty

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A new fault detection scheme for continuous-time nonlinear dynamic system is studied. The nonlinear system is modelled by Takagi-Sugeno fuzzy model. A fuzzy observer-based approach is presented to detect the fault occurred in the dynamic system. The generated residual signal is robust with respect to undesirable effects of unknown inputs and modelling errors but sensitive to faults. By applying H∞ optimization techniques, a sufficient condition to solvability of the formulated problem is established in terms of Linear Matrix Inequalities. The algorithm for residual evaluation function and threshold computation are also given. Finally, an example is given to illustrate the effectiveness of the proposed design techniques.

Robust Fuzzy Fault Detection for Continuous-Time Nonlinear Dynamic Systems

Induction motors have been extensively employed in industrial automation systems owing to their inexpensiveness and ruggedness. Current sensors of induction machines would have faults or malfunctions due to the age, which may lead to wrong commands of the controller, causing system performance degradation and even dangerous situations. Therefore, it is motivated to detect the current sensor faults at the early stage so that necessary actions can be taken to avoid further damage of the induction machines and serious situations. In this study, an augmented observer is designed to simultaneously estimate system states, and current sensor faults. In order to attenuate the effects from the modelling error and environment disturbances/noises, a genetic algorithm is employed to design observer gain by minimizing the estimation error against modelling errors and environmental disturbances/noises. The real-data of the induction motor collected by experiment is utilized to validate the proposed methods, which has demonstrated the efficiency of the proposed sensor fault diagnosis approaches. The proposed methods have great potential to improve the reliability of the real-time operation of the induction motor drive systems.

Robust sensor fault estimation for induction motors via augmented observer and GA optimisation technique

DC–DC power converters play an important role in renewable energy systems, electrical vehicles, and battery chargers and so forth. DC–DC Buck converters are prone to faults due to age and unexpected accidents. As a result, there is a high demand to improve the operation reliability and safety of power converters by using condition monitoring and fault diagnosis techniques. In this paper, data-driven and machine learning-based fault detection and fault classification strategies are addressed for DC–DC Buck converters under disparate faulty scenarios of the parameters. A variety of algorithms such as principal component analysis, multi-linear principal component analysis, uncorrelated multi-linear principal component analysis, and Fast Fourier Transformation pre-processing based multi-linear principal component analysis and uncorrelated multi-linear principal component analysis techniques are applied for fault classification and diagnosis of the parameter faults in the DC–DC Buck converters. The effectiveness is demonstrated and discussed with details.

Zhiwei Gao

Papers

On parameterization design for linear observers

Performance sensitivity analysis for linear systems with five types of structural uncertainty

Robust Fuzzy Fault Detection for Continuous-Time Nonlinear Dynamic Systems

Robust sensor fault estimation for induction motors via augmented observer and GA optimisation technique

Data-Driven Parameter Fault Classification for A DC–DC Buck Converter