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

Operations, maintenance and reliability of wind turbines have received much attention over the years due to the rapid expansion of wind farms. This paper explores early fault diagnosis technique for a 5MW wind turbine system subjected to multiple faults, where genetic optimization algorithm is employed to make the residual sensitive to the faults, but robust against disturbances. The proposed technique has a potential to reduce the downtime mostly caused by the breakdown of components and exploit the productivity consistency by providing timely fault alarms. Simulation results show the effectiveness of the robust fault detection methods used under Matlab/Simulink/Gatool environment. Keywords—Disturbance robustness, fault monitoring and detection, genetic algorithm and observer technique.

Robust Fault Diagnosis for Wind Turbine Systems Subjected to Multi-Faults

Robust fault estimation for vehicle lateral dynamic systems 1

For the long range communicated Cucker
 $-$ 
Smale model, asymptotic flocking exists for any initial data. It is noted that, for the short range communicated Cucker-Smale model, asymptotic flocking only holds for very restricted initial data. In this case, the non-existence of the asymptotic flocking has been frequently observed in numerical simulations, however, the theoretical results are far from perfect. In this note, we first point out that the non-existence of the asymptotic flocking is equivalent to the unboundedness of the second order space moment, i.e. $\sup_t\sum|x_i(t)-x_j(t)|^2=\infty$ 
. Furthermore, by taking the second derivative and then integrating, we establish a new and key equality about this moment. At last, we use this equality and some technical lemmas to deduce a general sufficient condition to the non-existence of asymptotic flocking.

Non-existence of the asymptotic flocking in the Cucker-Smale model with short range communication weights

In this paper, an innovative unknown input observer (UIO) is developed to estimate the faults of the system subjected to faults and process disturbances. By representing the concerned faults as auxiliary states, an augmented system is constructed. By designing an unknown input observer for the augmented system, the simultaneous estimations of the system states and concerned faults can be obtained readily. In this study, the unknown input disturbance is not assumed to be decoupled completely. The integration of the UIO technique and the linear matrix inequality (LMI) optimization technique is proposed to decouple and attenuate the input disturbances. The existence condition of such an observer and the design method of the observer gains by using the LMI are addressed as well. Finally, the proposed algorithm is applied to the simulation study of a three shaft gas turbine system to show the excellent fault estimation performance.

Novel unknown input observer for fault estimation of gas turbine dynamic systems

In this paper, an aero gas turbine engine with three shafts are investigated. By employing data-driven method, a reduced-order model is obtained, which has the close output performance as the 14th-order full-order model. Based on the reduced-order model, a fault detection filter is designed to detect actuator faults and sensor faults for the system subjected to input and output noises. Genetic optimization algorithm is used to design the filter gains such that the residual signal is sensitive to the faults, but robust to process and sensor noises. Simulated results demonstrate the efficiency of the present algorithm.

Zhiwei Gao

Papers

Robust Fault Diagnosis for Wind Turbine Systems Subjected to Multi-Faults

Robust fault estimation for vehicle lateral dynamic systems 1

Non-existence of the asymptotic flocking in the Cucker-Smale model with short range communication weights

Novel unknown input observer for fault estimation of gas turbine dynamic systems

Data-driven model reduction and fault diagnosis for an aero gas turbine engine