0000000000042429
AUTHOR
Shen Yin
Advanced stochastic control systems with engineering applications
1 School of Astronautics, Harbin Institute of Technology, Harbin, Heilongjiang, China 2 School of Electrical and Electronic Engineering, The University of Adelaide, SA 5005, Australia 3 Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway 4 Institute of Automation and Complex Systems, University of Duisburg-Essen, Duisburg, Germany 5 College of Automation, Chongqing University, Chongqing 400044, China
Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/836895 Open Access This paper investigates the proficiency of support vector machine (SVM) using datasets generated by Tennessee Eastman process simulation for fault detection. Due to its excellent performance in generalization, the classification performance of SVM is satisfactory. SVM algorithm combined with kernel function has the nonlinear attribute and can better handle the case where samples and attributes are massive. In addition, with forehand optimizing the parameters using the cross-validation technique, SVM can produce high accuracy i…
Setpoints compensation in industrial processes via multirate output feedback control
This paper investigates the setpoints compensation for a class of complex industrial processes. Plants at the device layer are controlled by the local regulation controllers, and a multirate output feedback control approach for setpoint compensation is proposed such that the subsystems can reach the dynamically changed setpoints and the given economic objective can also be tracked via certain economic performance index (EPI). First, a sampled-data multivariable direct output feedback proportional integral (PI) controller is designed to regulate the performance of the subsystems. Second, the outputs and control inputs of the plants at the device layer are sampled at operation layer sampling …
Filtering for Discrete Fuzzy Stochastic Time-Delay Systems with Sensor Saturation
Published version of an article from the journal: Mathematical Problems in Engineering. Also available from Hindawi: http://dx.doi.org/10.1155/2013/146325 This paper addresses the H-infinity filtering problem for discrete fuzzy stochastic systems with time-varying delay and sensor saturation. Random noise depending on state and external disturbance is also taken into account. A decomposition approach is employed to solve the characteristic of sensor saturation. The scaled small gain (SSG) theorem is extended to the stochastic systems, which is employed to handle with the time-varying delay by transforming the original system into the form of an interconnected system consisting of two subsys…
Residual Generator-Based Controller Design via Process Measurements
Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://dx.doi.org/10.1155/2014/290371 This paper deals with designing the controller of LTI system based on data-driven techniques. We propose a scheme embedding a residual generator into control loop based on realization of the Youla parameterization for advanced controller design. Basic idea of the proposed scheme is constructing the residual generator by using the solution of the Luenberger equations as well as the well-established relationship between diagnosis observer (DO) and the parity vector. Besides, the core of the above idea is straightly using the process …
Metric learning method aided data-driven design of fault detection systems
Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://dx.doi.org/10.1155/2014/974758 Fault detection is fundamental to many industrial applications. With the development of system complexity, the number of sensors is increasing, which makes traditional fault detection methods lose efficiency. Metric learning is an efficient way to build the relationship between feature vectors with the categories of instances. In this paper, we firstly propose a metric learning-based fault detection framework in fault detection. Meanwhile, a novel feature extraction method based on wavelet transform is used to obtain the feature ve…
Data-driven design of robust fault detection system for wind turbines
Abstract In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark. The main challenges of the wind turbine fault detection lie in its nonlinearity, unknown disturbances as well as significant measurement noise. To overcome these difficulties, a data-driven fault detection scheme is proposed with robust residual generators directly constructed from available process data. A performance index and an optimization criterion are proposed to achieve the robustness of the residual signals related to the disturbances. For the residual evaluation, a proper evaluation approach as well as a suitable decision logic is given to make a correct …
Robust Estimation for Discrete Markov System with Time-Varying Delay and Missing Measurements
This paper addresses theℋ∞filtering problem for time-delayed Markov jump systems (MJSs) with intermittent measurements. Within network environment, missing measurements are taken into account, since the communication channel is supposed to be imperfect. A Bernoulli process is utilized to describe the phenomenon of the missing measurements. The original system is transformed into an input-output form consisting of two interconnected subsystems. Based on scaled small gain (SSG) theorem and proposed Lyapunov-Krasovskii functional (LKF), the scaled small gains of the subsystems are analyzed, respectively. New conditions for the existence of theℋ∞filters are established, and the correspondingℋ∞f…
Data-Driven Adaptive Observer for Fault Diagnosis
Published version of an article from the journal: Mathematical Problems in Engineering. Also available from the publisher:http://dx.doi.org/10.1155/2012/832836 This paper presents an approach for data-driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After a fault is successfully detected, the iso…
Design of a TFT-LCD based digital automobile instrument
Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://dx.doi.org/10.1155/2014/549790 The traditional mechanical instrument lacks the ability to satisfy the market with characters of favorable compatibility, easy upgrading, and fashion. Thus the design of a TFT-LCD (thin film transistor-liquid crystal display) based automobile instrument is carried out. With a 7-inch TFT-LCD and the 32-bit microcontroller MB91F599, the instrument could process various information generated by other electronic control units (ECUs) of a vehicle and display valuable driving parameters on the 7-inch TFT-LCD. The function of aided parkin…
A subspace based fault diagnose method and its application on mechatronics systems
The mechatronics systems are widely used in modern society. This paper presents a novel data-driven scheme which can be used for fault diagnose of mechatronics systems. The proposed method is based on the subspace identification of parity vector. By constructing the output observer, critical variables can be acquired by soft sensors. This makes the fault diagnoses free from the limitation of online measurement. A diagnose observer is designed directly from the parity vector. Finally, the proposed scheme is tested by the Simulink benchmark of vehicle suspension and shows its good performance.
Multivariate Methods Based Soft Measurement for Wine Quality Evaluation
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/740754 Open Access Soft measurement is a new, developing, and promising industry technology and has been widely used in the industry nowadays. This technology plays a significant role especially in the case where some key variables are difficult to be measured by traditional measurement methods. In this paper, the quality of the wine is evaluated given the wine physicochemical indexes according to multivariate methods based soft measurement. The multivariate methods used in this paper include ordinary least squares regression (OLSR), principal c…
Allocation of actuators and sensors for coupled-adjacent-building vibration attenuation
An actuator and sensor allocation approach is proposed for the design of coupled-adjacent-building vibration suppression under seismic excitation. This paper first establishes a full-order model of adjacent buildings with the location information of actuators and sensors. Then, the order of the model is reduced via modal cost analysis, by retaining the modes contributing the most. In view of the fact that the output powers of the actuators are limited, this paper brings forward a mixed H∞/GH2 control. By considering that not all the states of the system can be measured by the sensors, a dynamic output feedback controller is designed. The genetic algorithm is employed to obtain the locations…
Active Vibration Control in Mechanical Systems
1 School of Astronautics, Harbin Institute of Technology, P.O. Box 3015, Yikuang Street No. 2, Nangang District, Harbin 150001, China 2Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway 3 Institute of Automation and Complex Systems, University of Duisburg-Essen, 47057 Duisburg, Germany 4Department of Applied Mathematics III, Universitat Politecnica de Catalunya (UPC), 08242 Manresa, Spain