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

Background:
Processes and systems are always subjected to faults or malfunctions due to age or unexpected events, which would degrade the operation performance and even lead to operation failure. Therefore, it is motivated to develop fault-tolerant control strategy so that the system can operate with tolerated performance degradation.

Methods:
In this paper, a reinforcement learning-based fault-tolerant control method is proposed without need of the system model and the information of faults.

Results and Conclusions:
Under the real-time tolerant control, the dynamic system can achieve performance tolerance against unexpected actuator or sensor faults. The effectiveness of the algorithm is demonstrated and validated by the rolling system in a test bed of the flux cored wire.

https://journals.sagepub.com/doi/pdf/10.1177/0020294018789202

Reinforcement learning–based fault-tolerant control with application to flux cored wire system

Pmid observer design for unknown input generalized dynamical systems

In this paper, a deep learning fault detection approach is proposed based on the convolutional neural network in order to cope with one class of faults in wind turbine systems. Fault detection is very vital in nowadays industries due to the fact that instantly detection can prevent waste of cost and time. Deep learning as one of the powerful approaches in machine learning is a promising method to identify and classify the intrigued problems, which are hard to solve by classical methods. In this case, less than 5% performance reduction in generator torque along with sensor noise, which is challenging to identify by an operator or classical diagnosis methods is studied. The proposed algorithm, which is evolved from convolutional neural network idea, is evaluated in simulation based on a 4.8 MW wind turbine benchmark and the accuracy of the results confirms the persuasive performance of the suggested approach.

Time-series Deep Learning Fault Detection with the Application of Wind Turbine Benchmark

Diversity, uncertainty and suddenness of unexpected faults bring a challenge for fault tolerant control due to the lack of valid data especially for a fault during an early stage. In this study, a reinforcement learning approach with a critic action architecture is proposed to overcome this challenge by designing an online learning fault-tolerant controller so that the faulty system can approximate the performance index of the fault-free system. Different from the traditional Hebb enhancement rules in the reinforcement learning, the training process is speeded up by introducing a supervisory learning on the basis of the training dataset which is built with the states and the virtual optimal control acquired by particle swarm optimization. The effectiveness of the algorithm is demonstrated by a test bed of a three-tank system.

/pdf/fault-tolerant-control-using-reinforcement-learning-and-17ugkn2bk3.pdf

Fault Tolerant Control Using Reinforcement Learning and Particle Swarm Optimization

The clock in embedded systems usually is driven by a crystal oscillator and implemented via a counter register, such a crystal clock is non-identical and drifting due to the manufacturing tolerance and variation of working conditions. Thus, a common time among distributed wireless sensor nodes, also referred to as Time Synchronization, is required for many time-sensitive wireless applications, such as collaborative condition monitoring, coordinated control and localization. Inspired by fireflies’ behaviour, the Pulse-Coupled Oscillators (PCO) has been proposed for synchronization in complex networks. Since the concurrent transmission of PCO’s Pulses is impossible in Wireless Sensor Networks (WSNs), the desynchronization mechanism is adopted to ensure the implementation of PCO in WSNs. Moreover, due to the uncertainties in radio channels and the complexities of communication protocols and packet-exchange behaviours in wireless networks, it is challenging to have a closed-form solution to the performance of PCO synchronization in WSNs. The realistic software simulation, in particular, the discrete event simulator has been a powerful tool to exam the performance of communication protocols in various scenarios, since an order sequence of well-defined event in time is to represent the behaviour of a complex system. This paper presents the development of a pulse-coupled oscillators time synchronization simulator on the OMNeT++ platform for simulating and studying its behaviour and performance in sensor networks. A clock module with configurable phase and frequency noises, and adjustable and higher resolution is developed to mimic various crystal oscillators in embedded systems, for example, the real-time clock. The developed simulator also supports the full functions devices defined by ZigBee protocol, which allows realistic simulation of multi-hop IEEE 802.15.4 wireless networks. Finally, the intensive simulations of classical PCO with the refractory period in IEEE 802.15.4-based WSNs have been carried out to demonstrate the features and benefits of the developed simulator. It is shown that for the non-identical and time-varying PCO clocks in the WSNs, the achieved synchronization will lose gradually, and the time that maintained synchronization depends on the length of refractory period.

Zhiwei Gao

Papers

Reinforcement learning–based fault-tolerant control with application to flux cored wire system

Pmid observer design for unknown input generalized dynamical systems

Time-series Deep Learning Fault Detection with the Application of Wind Turbine Benchmark

Fault Tolerant Control Using Reinforcement Learning and Particle Swarm Optimization

Simulation and evaluation of pulse-coupled oscillators in wireless sensor networks