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 properties of the disturbance estimation in a high gain observer are analyzed, and a time delay calculation method is proposed to improve the performance of parameter identification. This paper shows that the estimation delay depends on the observer gain, but has nothing to do with the parameter variation. Then, a novel algorithm is proposed to calculate the delay according to the phase response of disturbance estimation transfer function. Hence, in terms of system identification, this delay is compensated by lagging the state estimate by the same amount of delay. This technique, on one side, improves the performance of parameter identification accuracy, and, on the other side, gives a new insight into the high-gain observer design.

Estimation delay compensation in high-gain observer-based parameter identification

The great success of deep neural network (DNN) in image field stimulates its application in fault detection and diagnose. However due to the limitation of system security, it is impossible to obtain complete fault data as the training database for neural network, so that it is challenging to identify a fault that never occurred before. In this paper, an ensemble approach is proposed to adapt to a new fault by adding output branches of the neural network. Firstly, the time series are transferred to numerous imaging matrixes. The intrinsic characteristics of the matrixes are then extracted using deep neural network which are used to judge whether it is a new fault according to the distance criterion. For a new fault, the DNN will retrain by transferring learning in order to reduce the computation and training time. The effectiveness of the algorithm is demonstrated by a numerical simulation example based on a wind turbine benchmark model.

An Ensemble Approach for Fault Diagnosis via Continuous Learning

Fault estimation for nonlinear descriptor systems with lipschitz constraints via lmi approach

THE paradigm of Industry 4.0 aims at reinventing procedures and business strategies in all industrial/factory fields. On the other hand, such a renovation generates massive amounts of data, lying from traditional tracking information of goods and services but also source codes, technology information, and intellectual property. This represents a serious threat for corporations that must invest, more than in the past, in protocols and procedures to keep their data secure from being stolen or widespread over networks. Therefore, this special section is devoted to selected papers focused on open challenges in the context of security and safety of Biometrics in Industry 4.0. Strong authentication mechanisms represent the key to ensure such a high level of security of data and biometrics may play a crucial role in this scenario. Biometric traits, both physical and behavioral, can be used to build up strong authentication platforms and infrastructure of trust enabling big corporations to protect their data against criminal attacks. On the other hand, the use of authentication mechanisms based on biometric solutions can often collide with national and international regulations which tend to protect sensitive information. While the use of biometrics for security can be developed in full awareness of the users, in case of safety the collection of biometric data concerns a more subtle concept of privacy. In a private context, the security aims at guaranteeing controlled access/use to spaces and devices for a limited number of users. In public, is the safety to be guaranteed, involving a wider number of users whose biometrics data need to be acquired and protected. The ethical and practical implication of the use of biometrics data in safety needs to be further explored. The choice of the biometric traits (either hard or soft) as well as the mechanism to put in place to protect those data and the user’s privacy ask for meticulous studies of the minimum number of traits to be involved in the monitored environment to achieve systems that are privacy preserving as well as robust and reliable. Augmented reality applications have been intensively promoted by Industry 4.0 but their usage in operative environments is not free from risks for employees and trainees. The analysis of behavioral biometric traits, and soft biometrics in general, can provide useful estimations on postures and gestures during augmented reality training sessions. They can be used to assess the suitability of the augmented reality application and indicate excessive physical workload that may attempt personnel safety.

Guest Editorial: Biometrics in Industry 4.0: Open Challenges and Future Perspectives

In this paper, a reinforcement learning approach is proposed to detect unexpected faults, where the noise-to-signal ratio of the data series is minimized for achieving robustness. The model parameter is taken as a special action of the reinforcement learning, and the policy valuation and policy improvement are utilized to find the parameters, which can make the estimated model consistent to the real-time system process. Compared with the model forecasted parameters and real-time process parameters, fault diagnosis is implemented. The fault degrees are also discussed by analyzing the distance differences between the healthy parameters and faulty parameters. The effectiveness of algorithm is demonstrated by a numerical simulation example.

Zhiwei Gao

Papers

Estimation delay compensation in high-gain observer-based parameter identification

An Ensemble Approach for Fault Diagnosis via Continuous Learning

Fault estimation for nonlinear descriptor systems with lipschitz constraints via lmi approach

Guest Editorial: Biometrics in Industry 4.0: Open Challenges and Future Perspectives

A reinforcement learning based fault diagnosis for autoregressive-moving-average model