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1.
This paper addresses an active fault diagnosis problem for a class of discrete-time closed-loop system with stochastic noise. By introducing the theories of system identification, a novel active fault diagnosis method is developed to detect and isolate the faults. An important advantage of the proposed method is that there is no need to cut off the original input signal, which is necessary in most active fault diagnosis methods. Firstly, due to the features of the faults, we transform the problem of fault diagnosis into a problem of model selection by estimating model parameters. Then, the sufficient condition for active fault diagnosability is analysed, and the property that auxiliary input signal can enhance the fault diagnosability is given. Finally, simulation studies are carried out to demonstrate the effectiveness and applicability of the proposed method.  相似文献   

2.
Multiplicative faults generally refer to the change of process parameters or structures which are well-suited to represent the process-related anomalies. Unlike sensor faults and external disturbances that are added into process observations and independent with process states, process-related faults directly influence process states such that it is more challenge to reconstruct and diagnose. To address the process-related fault diagnosis, an online fault reconstruction method based on the multiplicative fault model is proposed with the commonly used multivariate statistical process monitoring framework. The fault reconstruction strategy based on the multiplicative fault representation is given by minimizing reconstruction errors. The diagnosability of the proposed reconstruction method is guaranteed for the change of a single parameter, also known as a unidimensional fault. Moreover, the reconstruction-based contribution is derived for providing heuristic references when diagnosing multidimensional faults. Experiments on a numerical example and a simulated continuous stirred tank heater process benchmark are carried out to investigate the effectiveness of the proposed method. The results show that this method can accurately diagnose the faulty variable or loop and further reconstruct the faulty samples into normal ones.  相似文献   

3.
This paper mainly focuses on the event-based state and fault estimation problem for a class of nonlinear systems with logarithmic quantization and missing measurements. The sensors are assumed to have different missing probabilities and a constant fault is considered here. Different from a constant threshold in existing event-triggered schemes, the threshold in this paper is varying in the state-independent condition. With resort to the state augmentation approach, a new state vector consisting of the original state vector and the fault is formed, thus the corresponding state and fault estimation problem is transmitted into the recursive filtering problem. By the stochastic analysis approach, an upper bound for the filtering error covariance is obtained, which is expressed by Riccati difference equations. Meanwhile, the filter gain matrix minimizing the trace of the filtering error covariance is also derived. The developed recursive algorithm in the current paper reflects the relationship among the upper bound of the filtering error covariance, the varying threshold, the linearization error, the probabilities of missing measurements and quantization parameters. Finally, two examples are utilized to verify the effectiveness of the proposed estimation algorithm.  相似文献   

4.
Nonlinear characteristic widely exists in industrial processes. Many approaches based on kernel methods and machine learning have been developed for nonlinear process monitoring. However, the fault isolation for nonlinear processes has rarely been studied in previous works. In this paper, a process monitoring and fault isolation framework is proposed for nonlinear processes using variational autoencoder (VAE) model. First, based on the probability graph model of VAE, a uniform monitoring index can be calculated by the probability density of observation variables. Then, the fault variables are estimated with normal variables by a missing value estimation method. The optimal fault variable set can be searched by branch and bound (BAB) algorithm. The proposed method can resolve the ”smearing effects” problem existing in traditional fault isolation methods. Finally, a numerical case and a hot strip mill process case are used to verified the proposed method.  相似文献   

5.
《Journal of The Franklin Institute》2022,359(18):10765-10784
In most of existing literature, it is assumed that all of the sensors can work normally. However in some situations, several sensors occur abnormal behavior or stuck at faults such that prior diagnosable decisions may not hold. By this regard, we address the problem of robustly distributed failure diagnosis of discrete-event systems with observation losses in this paper. In order to ensure diagnosability, the notion of robustly diagnosability is proposed in the distributed framework. Motivated by earlier works, new communication models and dilation operators are constructed, based on which the robustly distributed diagnosis problem is converted to a distributed diagnosis problem. One algorithm for the verification of robustly distributed diagnosis is proposed. Followed by it, a necessary and sufficient condition for the robustly diagnosability is presented. Finally, a part of Alipay transaction systems as an application is used to illustrate the construction of some automata and the verification algorithm.  相似文献   

6.
This paper is devoted to the fault detection of linear systems over networks with bounded packet loss. The inputs and the measurements of the monitored system are transmitted to a fault detection node over an unreliable network with bounded packet loss. The packet loss process is assumed to be arbitrary or Markovian in this paper. Due to the bounded packet loss process, the monitored system is modeled as a switched system by re-sampling it at each time instant when the measurements arrive at the fault detection node. A fault detection filter for this switched system is designed in this paper to satisfy some performance constraints. The filter updates only at the time instant when new measurements arrive at the fault detection node and the input data packets' lost are considered as external disturbances. Finally, the numerical example and simulations have demonstrated the usefulness of the proposed method.  相似文献   

7.
This note is concerned with the static output feedback control problem for two-dimensional (2-D) uncertain stochastic nonlinear systems. The systems under consideration are subjected to time delays, multiplicative noises and randomly occurring missing measurements. A random variable sequence following the Bernoulli distribution with time-varying probability is employed to character the missing measurements which are assumed to occur in a random way. A new gain-scheduling method based on the time-varying probability parameter is proposed to accomplish the design task. By constructing a suitable Lyapunov functional, sufficient conditions to guarantee the systems to be mean-square asymptotically stable are established. The addressed 2-D controller design problem can be reduced to a convex optimization problem by some mathematical techniques. In the last section, a numerical example and the comparative analysis are provided to illustrate the efficiency of our proposed design approach.  相似文献   

8.
Accurate and effective state estimation is essential for nonlinear fractional system, since it can provide some vital operation information about the system. However, inevitably missing measurements and additive uncertainty in the gain will affect the performance of estimation result. Thus, in this paper, in order to deal with these problems, a novel robust extended fractional Kalman filter (REFKF) is developed for states estimation of nonlinear fractional system, by which the states can be estimated accurately even with missing measurements. Finally, simulation results are provided to demonstrate that the proposed method can achieve much better estimation performance than the conventional extended fractional Kalman filter (EFKF).  相似文献   

9.
This paper is concerned on the fault detection (FD) problem in finite frequency domain for networked control systems (NCSs) with missing measurements. By virtue of the stopping time, the considered NCSs are firstly modeled as Markov jumping systems (MJSs). The notion of finite frequency stochastic H−H− index is then introduced to measure the sensitivity of the residuals. Taking into account a new sensor fault model, which is valid to express the failures of stuck, loss of effectiveness as well as outage ones, a novel FD scheme is developed with simultaneous consideration of sensitivity performance and attenuation performance in finite frequency domain, such that it is valid for all admissible sensor faults. In addition, new convex conditions in terms of linear matrix inequalities (LMIs), which can be reduced to some previous results, are presented to cope with this FD problem. Further, fault detection filters (FDFs) can be constructed by solving the derived LMIs. Finally, such an FD scheme is utilized to an aircraft model, and the effectiveness of proposed method is demonstrated by the simulation results.  相似文献   

10.
This paper concerns the fault detection (FD) problem for a class of discrete-time systems subject to data missing and randomly occurring nonlinearity modeled by two independent Bernoulli distributed random variables. We propose to design a set of fault detection filters, or residual generation systems, corresponding to each of the fault components, to guarantee that each subsystem is mean square stable and satisfies a prescribed disturbance attenuation level. Sufficient conditions are established in the form of linear matrix inequalities (LMIs). System faults can be effectively detected by generating the residues and comparing them with the dynamic fault thresholds. A quadrotor vehicle example with faults on angles and angular rates illustrates and verifies the effectiveness of the proposed algorithm.  相似文献   

11.
In this paper, identification of discrete-time power spectra of multi-input/multi-output (MIMO) systems in innovation models from output-only time-domain measurements is considered.A hybrid identification algorithm unifying mixed norm minimization with subspace estimation method is proposed. The proposed algorithm first estimates a covariance matrix from measurements. A significant dimension reduction is achieved in this step. Next, a regularized nuclear norm optimization problem is solved to enforce sparsity on the selection of most parsimonious model structure. A modification of the covariance estimates in the proposed algorithm generates yet another algorithm capable of handling data records with sequentially and intermittently missing values. The new and the modified identification algorithms are tested on a numerical study and a real-life application example concerned with the estimation of joint power spectral density (PSD) of parallel road tracks.  相似文献   

12.
This paper is concerned with the event-based fault detection for the networked systems with communication delay and nonlinear perturbation. We propose an event-triggered scheme, which has some advantages over existing ones. The sensor data is transmitted only when the specified event condition involving the sampled measurements of the plant is violated. An event-based fault detection model is firstly constructed by taking the effect of event-triggered scheme and the network transmission delay into consideration. The main purpose of this paper is to design an event-based fault detection filter such that, for all unknown input, communication delay and nonlinear perturbation, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions for the existence of the desired fault detection filter are established in terms of linear matrix inequalities. Based on these conditions, the explicit expression is given for the designed fault detection filter parameters. A numerical example is employed to illustrate the advantage of the introduced event-triggered scheme and the effectiveness of the proposed method.  相似文献   

13.
The identification problem of output-error autoregressive (OEAR) systems with scarce measurements is considered in this paper. In order to overcome the massive absence of outputs, an interval-varying recursive identification algorithm is proposed through changing the sampling interval and skipping the missing outputs. Based on the maximum likelihood principle, a maximum likelihood interval-varying recursive least squares algorithm is proposed. The effectiveness of the proposed algorithm is tested by a numerical simulation example, and an application example about the heading motion control of underwater vehicle.  相似文献   

14.
As an important technology to improve network reliability, fault diagnosis has gained wide attention in complex dynamical networks. However, few studies focused on detecting the structure of broken edges when faults occur. In this paper, due to the natural sparsity of complex dynamical networks, a completely data-driven method based on compressive sensing is established to detect the structure of faulty edges from limited measurements. The least absolute shrinkage and selection operator algorithm is applied to solve the reconstruction problem. In addition, the method is also applicable to multilayer networks. The faulty edges in both the intralayer network and the interlayer network can be fully identified. Compared with other methods, the main advantages of the proposed method lie in two aspects. First, the structure of faulty edges can be obtained directly with limited measurements. Second, the proposed method is less time consuming and more efficient due to less data processing. Numerical simulations involving single-layer, multilayer and real-world complex dynamical networks are given to demonstrate the accuracy of detecting the structure of faulty edges from the proposed method.  相似文献   

15.
In this paper, the fault detection problem is studied in finite frequency domain for constrained networked systems under multi-packet transmission. The considered transmission mechanism is that only one packet including parts of the measured information can be transmitted through the communication channel and their accessing is scheduled by a designed stochastic protocol. Then by virtues of the introduced performance indices in finite frequency domain, a novel effective fault detection scheme is presented, in which the fault detection filters completing the task with partially available measurements are designed to make sure that the residual is sensitive to the reference input and the fault in faulty cases and robust to the reference input in fault-free case. Further, convex conditions in terms of time-domain inequalities are developed to handle the proposed fault detection scheme. The theoretical results are validated by the simulation to detect the sensor fault on a lateral-directional aerodynamic model.  相似文献   

16.
In traditional system identification methods, it is often assumed that the output data are corrupted by Gaussian white noise which is independent and identically distributed (i.i.d.). However, this assumption may lead to poor robustness since the noise characteristic often varies throughout the sampling process. In this work, output measurements affected by switching Gaussian noise are considered. In addition, a Markov chain model is utilized to describe the multi-mode behavior of the noises. Meanwhile, the collected data are usually incomplete in practice. Taking these circumstances into account, a new algorithm for Gaussian process regression (GPR) with switching noise mode and missing data is introduced. The parameters of the model are estimated by expectation maximization (EM) algorithm via conjugate gradient (CG) method. Two numerical examples along with a continuous stirred tank reactor simulation are employed to verify the effectiveness of the proposed algorithm. The superior performance is demonstrated by comparing the proposed algorithm with other existing relevant methods.  相似文献   

17.
A novel interval observer filtering-based fault diagnosis method for linear discrete-time systems with dual uncertainties is proposed to detect actuator faults. The idea of minimization is adopted to design a fault-free state estimator by merging unknown but bounded and Gaussian disturbances and noises according to the signal average power principle. Using a fault-free state interval and measurement residual of the system, a fault detection indicator is designed based on the residual probability ratio, to achieve dynamic fault detection, isolation and identification. Finally, various simulation examples are provided to demonstrate the accuracy and effectiveness of the proposed method.  相似文献   

18.
This paper concerns the issues of fault diagnosis and monitoring for an automobile suspension system where only accelerator sensors in the four corners of the car body are available. A clustering based method is proposed to detect the fault happened in the spring, and the Fisher discriminant analysis is applied to isolate the root factor for the fault. Different from most of the existing approaches, the pure data-driven characteristic enables this method to serve as an on-line fault diagnosis and monitoring tool without suspension model or fault features known as a prior. Moreover, this method can classify different reductions in the spring coefficient into one fault rather than different faults. The effectiveness of the proposed method is finally illustrated on an automobile suspension benchmark.  相似文献   

19.
This paper proposes a novel particle filter based gradient iterative algorithm for the identification of dual-rate nonlinear systems. The novel particle filter is applied to estimate the missing outputs, and the measurable outputs are utilized to adjust the weights of particles during each interval of the slow sampled rate. Then the missing outputs and the unknown parameters can be estimated iteratively by the novel particle filter based gradient iterative algorithm. The simulation results indicate that the proposed method is more effective than the classical auxiliary model method.  相似文献   

20.
In this paper, the reliable control design is considered for networked control systems (NCSs) against probabilistic actuator fault with different failure rates, measurements distortion, random network-induced delay and packet dropout. A new distribution-based fault model is proposed, which also contains the probability distribution information of the random delay and packet dropout. By using Lyapunov functional and new technique in dealing with time delay, stability and stabilization criteria are derived in terms of linear matrix inequalities. The provided numerical example and vertical takeoff and landing (VTOL) aircraft system illustrate that: firstly, using the distribution information of the delay, the maximum effective delay bound (MEDB) can be greatly improved, secondly, the proposed reliable controller can stabilize the NCSs with probabilistic actuator fault and measurements distortion, which may be unstable under the controller designed without considering the unreliable cases.  相似文献   

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