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The problem of fault detection for a class of continuous-time nonlinear networked control systems subject to delays induced by the networks from the sensor to controller and from controller to actuator is concerned in this paper. An observer-based fault detection filter is designed. A sufficient condition is also derived which makes the closed-loop system asymptotically stable in terms of linear matrix...
For the problem of hefty workload and bring impurity in the process of artificial transformer internal fault detection, a WiFi control based robot is designed to detect the internal fault of the oil-immersed power transformer. The outline structure of the fault detection robot for transformer is illustrated, and WiFi communication module is used to build the control system of the robot. Electromagnetic...
Problems related to fault detection of turnouts are discussed in this paper, which is important to guarantee the security of a driving train. Motivated by an existing approach for fault detection of turnouts and to deal with its demerits, a hybrid feature extraction method is given, which extracts information in both time and frequency domains, then support vector machine (SVM) is further used to...
In order to improve the accuracy and stability of industrial fault detection and diagnosis, this paper introduces the deep learning theory and proposes an improved Deep Belief Networks (DBNs). In the first, this paper introduces the “centering trick” in the pre-training process of network. This method is done by subtracting offset values from visible and hidden variables. Then, in the process of network...
At present, the integrated navigation system used by multirotor Unmanned Aerial Vehicles (UAVs) is heavily dependent on the GPS when flying outdoors. However, signal block, interference and other factors will lead to the GPS failure, in which case the UAVs' navigation error will quickly diverge. In this paper, an aerodynamic model / INS (Inertial Navigation System) / GPS failure-tolerant navigation...
This article investigates the problem of fault detection for a class of uncertain systems by a new method. Based on the system's own uncertainty and external disturbances, a more effective fault detection method is designed by the method of homogeneous polynomial function. This detection method not only suppresses the effects of disturbances and uncertainties on the residual signal but also makes...
In this paper, a novel kernel independent component analysis method which is named improved DKICA is proposed for dynamic industry processes' fault detection and fault diagnosis. The primary idea of this method is how to obtain an augmented measurement matrix in the data kernel space, the independent component analysis is used, so the dynamic and nonlinear features can be extracted in non-linear non-Gaussian...
In the high temperature technology widely used today, high demand for high temperature materials, and fused magnesia is a very good high temperature material. The operation of the fused magnesium furnace is very important, so we use statistic method to monitor the abnormal condition. First, utilized statistic method to handle the collected data of the preprocess and extract the information of the...
With the continuous development of large-scale, continuous and complex industries process, the reliability and safety requirements are getting higher and higher of the industrial equipment and control system. Process monitoring has become one of the most important research directions in process automation. In this paper, the ICA-based continuous industrial process system monitoring methods were studied...
In this paper, we study the fault detection filter (FDF) design problem for linear discrete time-varying (LDTV) systems with model uncertainties and norm bounded unknown inputs. An alternative indefinite quadratic performance criterion is introduced in lieu of the standard H∞ performance, and the design issue is converted to an optimization problem of finding the positive minimum of the new defined...
Principal component analysis (PCA) is widely used for fault detection for chemical processes; however, the efficient principal component (PC) selection remains an challenge. The effect of PC selection on PCA-based fault detection performance is analyzed within the statistical framework of hypothesis testing. A performance-driven fault-relevant PC (FRPC) subspace construction integrated with Bayesian...
In view of the shortcomings of traditional fault diagnosis system in accuracy, robustness and too complicated recognition algorithm, the distributed intelligent fault diagnosis system is presentation based on ZigBee technology and particle filter. Multi variable data's acquisition and centralized treatment is realized through the wireless sensor network, and the accurate estimation of the state of...
With the increase of the scale and complexity of the industrial process, the requirements for process safety and reliability are further improved. In order to detect the equipment failure accurately and timely, a fault detection method based on continuous hidden Markov model (CHMM) is proposed. The principal component analysis (PCA) method is used to extract the characteristic data of the process...
Pneumatic control valves are the most frequently used actuators in industrial processes. Its property definitely affects the performance of processes and therefore process monitoring of the pneumatic control valve is of great importance. Canonical Variate Analysis (CVA) is a multivariate data-driven method which considers time correlations and has been demonstrated to be superior to some methods in...
In this paper, the authors raise a parity space based fault detection (FD) scheme in closed-loop quadruped robot dynamic control. The complete robot model is simplified to a cuboid, where three perpendicular forces and three perpendicular torques are applied to the center of mass (COM) of the robot model. The closed-loop system of the robot is integrated by a proportional-derivative (PD) controller...
Data-driven fault detection technique has been widely applied for process monitoring, which can effectively detect faults happened in industrial processes. It is extremely significant for guaranteeing the normal operation of processes. Independent Component Analysis (ICA), a type of Data-driven fault detection technique, has been successfully applied to Blind Source Separation and process fault detection...
In this paper, the distributed fault detection (FD) problem is addressed for a class of linear stochastic multi-agent systems (MASs). A novel distributed cooperative control scheme is given for reaching bounded formation of the MAS. It is proved that by using relative outputs between neighboring agents, a set of distributed fault detection filters can be designed for each agent to detect the faults...
In this paper, a decentralized fault diagnosis approach of complex processes is proposed based on multi-block kernel probabilistic principal component analysis (MBKPPCA). Under the probabilistic modeling framework, this paper introduced MBKPPCA into process monitoring and gave a qualitative analysis on the problems of determining the parameters in MBKPPCA. Efficient Expectation-Maximization algorithms...
In this paper, a real-time fault tolerant scheme based on the bionic polarization integrated navigation system is proposed. First, we use the polarized light information to assist SINS/GPS/geomagnetic integrated navigation system, realize the redundancy of the heading information and improve the reliability of the system. Then, Kalman filter based real-time fault detection program is used to detect...
Current industrial system develops more and more complex and intelligent, whose safety and reliability relies on fault diagnosis technology. In the age of big-data, data-driven fault diagnosis becomes the state of the art, and the demand for the diagnostic toolbox also increases. In this paper, theories of data-driven fault detection models, both static model and dynamic, are revealed. And then a...
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