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

Wireless Sensor Actuator Networks (WSANs) have shown its advantages of flexibility and cable-free deployment in industrial applications. However, the sensors explore directly to the harsh environment and are the most prone to faults and malfunctioning, which poses a big challenge to the WSAN's applicability in applications with high safety requirements. This paper focuses on highly reliable WSANs equipped with double-sensing system and proposes a model-based on-line sensor fault detection scheme by exploiting information redundancy between sensors and actuators. An optimization method is developed to make the proposed scheme robust to the largely fluctuating end-to-end packet transmission delays due to wireless communications. The performance of the proposed sensor fault detection scheme is demonstrated by the simulation of a pick and place robot.

Model-based on-line sensor fault detection in Wireless Sensor Actuator Networks

In this paper, a model identification method based on artificial neural networks (ANN) for wind turbine dynamics is studied. Due to the fact that wind turbine has a nonlinear dynamics with partially measured states, ANN cannot be applied directly. To cope with this problem, first a Luenberger observer is designed to estimate the states (both measured and unmeasured ones) and then, for the nonlinear part, a multi-input multi-output (MIMO) back propagation neural-network based observer is proposed. By having an ANN model as the reference, a fault detection method is studied based on the residual of the system. This algorithm is evaluated in simulation on a 4.8 MW wind turbine benchmark and the results approve satisfactory performance of the proposed approach.

Grey-box Model Identification and Fault Detection of Wind Turbines Using Artificial Neural Networks

As a key enabling technology in mission-critical Wireless Sensor Networks (WSNs), time synchronization provides a common timescale for distributed sensor nodes in many wireless applications, such as coordinated control and underwater navigation and tactical surveillance. Inspired by the behaviour of fireflies, along with mathematical model, Pulse-Coupled Oscillators (PCO), has been proposed to enable synchronization in complex networks, where all the PCO's firing signal Pulses are broadcasted simultaneously when synchronization is achieved. The requirement of zero-drift clock oscillators, fully-connected network and concurrent transmission of Pulses are, in reality, impossible to achieve. To avoid transmission collision and enable the PCO extension in the multi-hop WSNs, the desynchronization mechanism is adopted to enable the Pulse packets to be transmitted to the wireless channel in a uniformly distributed fashion. Due to the contention-free period's feature of low-latency, thereby avoiding the need to wait for a random and potentially long period until the channel is available, the PCO's Pulse packets are transmitted in the contention-free period of IEEE 802.15.4-2015 superframe. Thus, a novel state-space model for desynchronization-based pulse-coupled nonidentical oscillators is proposed to model a realistic drifting clock oscillator. Moreover, the timestamped Pulse packets are transmitted to determine the offset of connected sensor nodes, and an attenuated clock correction scheme is adopted to correct the local drifting clocks by using measured offset and skew. The intensive simulations of the three-hop three-cluster wireless network and the seven-hop linear network have been carried out to evaluate performance of timestamped PCO with desyn-chronization method.

Synchronization of Pulse-Coupled Oscillators for IEEE 802.15.4 Multi-Hop Wireless Sensor Networks

This paper presents a new power line communication (PLC) modem design which can control data flow with a new networking strategy to propagate signals for long distance without using extra cabling or signal repeaters. An investigation is also carried on the utilization of twin core power line cables in vast solar farm for controlling solar trackers and data acquisition purposes. Frequency responses of these power lines are analyzed and tested according to different coupling circuits. A simple design is proposed for PLC modem based on such channel characteristics, data flow control strategy, collision avoidance technique and error detection.

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A new power line communication modem design with applications to vast solar farm management

Wind turbine system is a safety-critical system, which has the demand to improve the operating reliability and reducing the cost caused by the shut-down time and component repairing. As a result, condition monitoring and fault diagnosis have received much attention for wind turbine energy systems. Noticing that environmental disturbances are unavoidable, therefore how to improve the robustness of a fault diagnosis scheme against disturbances/noises has been a key issue in fault diagnosis community. In this investigation, a robust fault estimation approach with the aid of eigenstructure assignment and genetic algorithm (GA) optimization is presented so that the estimation error dynamics has a good robustness against disturbances. A simulation study is carried out for a 5MW wind turbine dynamic model, which has demonstrated the effectiveness of the proposed techniques.

Zhiwei Gao

Papers

Model-based on-line sensor fault detection in Wireless Sensor Actuator Networks

Grey-box Model Identification and Fault Detection of Wind Turbines Using Artificial Neural Networks

Synchronization of Pulse-Coupled Oscillators for IEEE 802.15.4 Multi-Hop Wireless Sensor Networks

A new power line communication modem design with applications to vast solar farm management

Robust fault estimation in wind turbine systems using GA optimisation