一种非线性非高斯随机系统的故障诊断方法

针对非线性非高斯随机系统在线故障诊断的问题,运用粒子滤波器提出了一种基于方差自适应粒子滤波器的非线性非高斯随机系统的故障诊断方法,可以用来解决系统的参数偏差型故障诊断问题。通过对连续搅拌釜式反应器(CSTR)的仿真研究,可以看出,该算法实现简单,易于对系统进行在线估计,对于发生缓变和突变的参数偏差型故障的检测与估计均较为有效。     

Minimum rational entropy fault tolerant control for non-Gaussian singular stochastic distribution control systems using T-S fuzzy modelling

In this paper, a new fault diagnosis (FD) and fault tolerant control (FTC) algorithm for a non-Gaussian nonlinear singular stochastic distribution control (SDC) system is studied. The rational square-root fuzzy logic model is used to approximate the output probability density function of non-Gaussian processes and a Takagi-Sugeno (T-S) fuzzy model is employed to transform the non-Gaussian nonlinear SDC system into a fuzzy SDC system. An adaptive fuzzy fault diagnosis observer is constructed to achieve reconstruction of system state and fault. Based on the estimated fault information, the controller is reconfigured by minimising the performance index with regard to the rational entropy subjected to mean constraint. Minimum rational entropy fault tolerant control is introduced to make the output of the past-fault SDC system still have the minimum uncertainty. Simulation results are provided to demonstrate the validity of the FD and minimum rational entropy FTC algorithm.     

基于数据变化率和重构贡献图的微小故障诊断方法

针对复杂工业过程的微小故障诊断问题,提出一种数据预处理与重构贡献图相结合的故障诊断方法;为了克服非高斯分布数据对故障检测准确性的影响,通过基于数据变化率的方法对样本原始数据进行预处理后,可以有效地检测过程变量的微小故障,以此建立故障诊断主元分析模型;检测出系统故障后,为了提高故障辨识准确度,采用一种平均残差差值重构贡献图的方法对故障进行辨识;通过正常样本数据和故障数据在残差子空间中的投影,获取两个数值的残差差值向量,计算重构贡献值来确定故障变量;以田纳西-伊斯曼(TE)过程为对象进行了故障诊断仿真实验,并与传统贡献图和重构贡献图方法的辨识准确率相比较,结果表明所提方法具有良好的故障诊断性能。     

Fault isolation for multivariate nonlinear non-Gaussian systems using generalized entropy optimization principle

This paper is concerned with the fault isolation (FI) problem for multivariate nonlinear non-Gaussian systems by using a novel filtering method. The generalized entropy optimization principle (GEOP) is established for non-Gaussian systems with multiple faults and disturbances, where the statistic information including entropy and mean of the residual variable is maximized in the presence of the target fault as well as all the nuisance faults and disturbances, and is minimized in the absence of the target fault but in the presence of the nuisance faults and disturbances. Different from the existing results where the output is measurable for feedback, the fault isolation filter is designed and driven by the joint output stochastic distributions rather than its deterministic value. The error dynamics is represented by a multivariate nonlinear non-Gaussian system, for which new recursive relationships are proposed to formulate the joint probability density functions (JPDFs) of the residual variable in terms of the JPDFs of the noises and the faults. Finally, a simulation example is given to demonstrate the effectiveness of the proposed multivariate FI algorithms.     

基于局部熵双子空间的多模态过程故障检测

为了提高非高斯工业过程的检测性能, 提出局部熵双子空间(LEDS)的多模态过程故障检测方法. 运用局部 概率密度估计构建数据的局部熵矩阵, 消除数据的多模态特性. 用Kolmogorov-Smirnov (KS)检验局部熵数据中变 量的正态分布特性, 对高斯分布和非高斯分布的数据分别建立基于PCA的高斯子空间和ICA的非高斯子空间故障 检测模型. 利用Bayesian决策将检测结果转化成发生故障概率的形式, 将检测结果组合成最终的统计信息, 进行故 障检测. 将该方法应用于数值例子和田纳西–伊斯曼多模态过程, 仿真结果表明, 该方法在误报率较低的情况下, 故 障检测率最高, 优于PCA、局部熵PCA(LEPCA)和局部熵ICA(LEICA)方法.     

Fault Diagnosis and Statistical Information Tracking Fault Tolerant Control for Non-Gaussian Non-linear Stochastic Systems

International Journal of Control, Automation and Systems - A novel fault diagnosis and fault tolerant control algorithm for non-Gaussian non-linear stochastic systems is presented in this paper....     

基于粒子群优化支持向量机的电梯故障诊断

电梯故障时,具有故障特征提取困难和故障类型识别率低的问题。因此,拟提取其振动信号并进行分析,找到故障特征。然而,鉴于其振动信号为非平稳、非高斯且背景噪声较大的信号,给有效辨识造成很大困难,所以,提出应用最优小波包分解和最小二乘支持向量机相结合进行电梯智能故障诊断的方法。借助最优小波包理论,首先提取电梯故障振动信号的能量分布;然后将其能量分布与时域指标相结合,构造故障特征向量;最后,将故障特征向量作为粒子群算法优化最小二乘支持向量机的输入对电梯故障类型进行识别。仿真结果表明,最优小波包理论与最小二乘支持向量机相结合的故障诊断技术发挥了两者的优势,证明了该方法的有效性和实用性。     

Statistical process monitoring with independent component analysis

In this paper we propose a new statistical method for process monitoring that uses independent component analysis (ICA). ICA is a recently developed method in which the goal is to decompose observed data into linear combinations of statistically independent components [1 and 2]. Such a representation has been shown to capture the essential structure of the data in many applications, including signal separation and feature extraction. The basic idea of our approach is to use ICA to extract the essential independent components that drive a process and to combine them with process monitoring techniques. I², I_e² and SPE charts are proposed as on-line monitoring charts and contribution plots of these statistical quantities are also considered for fault identification. The proposed monitoring method was applied to fault detection and identification in both a simple multivariate process and the simulation benchmark of the biological wastewater treatment process, which is characterized by a variety of fault sources with non-Gaussian characteristics. The simulation results clearly show the power and advantages of ICA monitoring in comparison to PCA monitoring.     

Identification of quadratic systems using higher order cumulants and neural networks: Application to model the delay of video-packets transmission

This work concerns the development of two approaches for the identification of diagonal parameters of quadratic systems from only the output observation. The systems considered are excited by an unobservable independent identically distributed (i.i.d), stationary zero mean, non-Gaussian process and corrupted by an additive Gaussian noise. The proposed approaches exploit higher order cumulants (HOC) (fourth order cumulants) and are the extension of the algorithms developed in the linear version 1D, which uses a non-Gaussian signal input. For test and validity purpose, these approaches are compared to recursive least square (RLS), least mean square (LMS) and neural network identification algorithms using non-linear model in noisy environment. To demonstrate the applicability of the theoretical methods on real processes, we applied the developed approaches to search for models able to describe the delay of the video-packets transmission over IP networks from video server. The simulation results show the correctness and the efficiency of the developed approaches.     

Fault detection,isolation and identification for hybrid systems with unknown mode changes and fault patterns

This article presents a solution to the problem of multiple fault detection, isolation and identification for hybrid systems without information on mode change and fault patterns. Multiple faults of different patterns are considered in a complex hybrid system and these faults can happen either in a detectable mode or in a non-detectable mode. A method for multiple fault isolation is introduced for situation of lacking information on fault pattern and mode change. The nature of faults in a monitored system can be classified as abrupt faults and incipient faults. Under abrupt fault assumption, i.e. constant values for fault parameters, fault identification is inappropriate to handle cases related to incipient fault. Without information on fault nature, it is difficult to achieve fault estimation. Situation is further complicated when mode change is unknown after fault occurrence. In this work, fault pattern is represented by a binary vector to reduce computational complexity of fault identification. Mode change is parameterized as a discontinuous function. Based on these new representations, a multiple hybrid differential evolution algorithm is developed to identify fault pattern vector, abrupt fault parameter/incipient fault dynamic coefficient, and mode change indexes. Simulation and experiment results are reported to validate the proposed method.     

Particle filtering for sensor fault diagnosis and identification in nonlinear plants

We propose a novel method for sensor monitoring and fault-tolerant estimation in systems described by general stochastic nonlinear and/or non-Gaussian state-space models. Faults are defined as abruptly occurring calibration errors, causing the sensor readings to be biased or scaled. Actuators and the process itself are assumed to be fault free. The main novelty of the work is an adaptive particle filter, whose configuration changes in order to diagnose sensor faults and to compensate for their effects. The presence, type and magnitude of sensor faults are determined through hypothesis testing and maximum likelihood estimation, based on the difference between the measurements and the particle filter estimates. The validity of the proposed approach was demonstrated through simulations on a drum-boiler model, although its effectiveness is not conditioned on any particular feature of the considered example.     

Particle filtering-based fault detection in non-linear stochastic systems

Much of the development in model-based fault detection techniques for dynamic stochastic systems has relied on the system model being linear and the noise and disturbances being Gaussian. Linearized approximations have been used in the non-linear systems case. However, linearization techniques, being approximate, tend to suffer from poor detection or high false alarm rates. A novel particle filtering based approach to fault detection in non-linear stochastic systems is developed here. One of the appealing advantages of the new approach is that the complete probability distribution information of the state estimates from particle filter is utilized for fault detection, whereas, only the mean and covariance of an approximate Gaussian distribution are used in a coventional extended Kalman filter-based approach. Another advantage of the new approach is its applicability to general non-linear system with non-Gaussian noise and disturbances. The effectiveness of this new method is demonstrated through Monte Carlo simulations and the detection performance is compared with that using the extended Kalman filter on a non-linear system.     

Fault detection via factorization approach

Problems of designing fault detection and identification filters in the frequency domain are formulated and solved. Using the factorization approach a characterization of all fault detection filters is derived. This enables the derivation of necessary and sufficient conditions for the existence of fault identification as well as detection and isolation filters. It is shown that these conditions are a generalization of existing results. The formulas of constructing the filters are also derived. In comparison with the algorithms given in previous work they are computationally straightforward and simple. Finally, the proposed method for designing fault identification filters is extended so that more practical cases can be handled.     

Fault diagnosis and fault-tolerant control for non-Gaussian nonlinear stochastic systems via entropy optimisation

ABSTRACT

In this paper, the fault diagnosis (FD) and fault tolerant control (FTC) problems are studied for non-linear stochastic systems with non-Gaussian disturbance and fault. Unlike classical FD algorithms, the minimum entropy FD is adopted to minimise the residual entropy and control the shape of the probability density function (PDF) of the residual signal. The observation error system can be proved to be locally and ultimately bounded in the mean square sense. Since entropy can be used to characteriSe the uncertainty of the tracking error for non-Gaussian stochastic systems, the FTC controller is obtained by minimising the performance function with regard to the entropy of the tracking error in this paper. The PDF of the output tracking error is approximated by the B-spline model. An illustrative example is utilised to demonstrate the effectiveness of the FD and FTC algorithm, and satisfactory results have been obtained.     

TMN告警信息存储及预处理技术

对原始告警信息的完整存储以及预处理是本地电话网网络管理系统故障管理的核心功能。本文提出了一种基于有限状态机的告警信息预处理模型 ,并应用该模型对检测到的告警消息在时间和空间上进行相关处理。工程应用表明 ,该模型对故障的辨识、诊断和定位提供了强有力的支持。     

Weighted preliminary-summation-based principal component analysis for non-Gaussian processes

For industrial chemical process, preliminary-summation-based principal component analysis (PS-PCA), an amended PCA method was recently provided for coping with both Gaussian and non-Gaussian characteristics. By summing the training and monitoring data respectively, PS-PCA is capable of resolving the issue of non-Gaussian processes and achieves higher fault detection rate than the traditional PCA. However, in the PS-PCA summation operation, all data samples are regarded as the same weight, which results in the fault information of newly-samples may be diluted, leading to significant detection delays. To address this challenge, in this paper, we propose a novel weighted PS-PCA (WPS-PCA) method that employs an exponential weighting scheme to put more emphasis on recent information. Subsequently, a mathematical argument demonstrates that when the number of variables is enough plentiful, the obtained summation combined with the generalized central limit theorem conforms to approximately a Gaussian distribution. The kurtosis relationships indicate this conversion will bring out well-pleasing feasibility for conventional PCA. Ultimately, the proposed technique verifies detection performance using the Tennessee Eastman process, which is compared with the existing PCA and PS-PCA schemes, in terms of the fault detection time and fault detection rate. The simulation studies reveal that the proposed method is efficient and superior.     

Two layered mixture Bayesian probabilistic PCA for dynamic process monitoring

In this article, two layer mixture Bayesian probabilistic principal component analyser model is developed and proposed for fault detection. It is suitable for the data driven process monitoring applications where data with non-Gaussian distribution and temporal correlations are encountered. Model development involves modifying the original observation matrix to make it suitable for building dynamic models and followed by two stages of estimation. In the first stage, the data is divided into a manageable number of clusters and in the second stage, a mixture model is built over each cluster. This strategy provides a scalable mixture model that can have multiple local models. It has the potential to provide a parsimonious model and be less susceptible to local optima compared to the existing approaches that build mixture models in a single stage. Dimension reduction during the estimation is automated using the Bayesian regularization approach. The proposed model essentially provides a probability density function for the training data. It is deployed for fault detection and the performance highlights are demonstrated in two real datasets, one is from the oil sands industry and the other is a publicly available experimental dataset.     

A new gear fault feature extraction method based on hybrid time–frequency analysis

Gear is one of the popular and important components in the rotary machinery transmission. Vibration monitoring is the common way to take gear feature extraction and fault diagnosis. The gear vibration signal collected in the running time often reflects the characteristics such as non-Gaussian and nonlinear, which is difficult in time domain or frequency domain analysis. This paper proposed a novel gear fault feature extraction method based on hybrid time–frequency analysis. This method combined the Mexican hat wavelet filter de-noise method and the auto term window method at the first time. This method can not only de-noise noise jamming in raw vibration signal, but also extract gear fault features effectively. The final experimental analysis proved the feasibility and the availability of this new method.     

Integrating independent component analysis and support vector machine for multivariate process monitoring

This study aims to develop an intelligent algorithm by integrating the independent component analysis (ICA) and support vector machine (SVM) for monitoring multivariate processes. For developing a successful SVM-based fault detector, the first step is feature extraction. In real industrial processes, process variables are rarely Gaussian distributed. Thus, this study proposes the application of ICA to extract the hidden information of a non-Gaussian process before conducting SVM. The proposed fault detector will be implemented via two simulated processes and a case study of the Tennessee Eastman process. Results demonstrate that the proposed method possesses superior fault detection when compared to conventional monitoring methods, including PCA, ICA, modified ICA, ICA–PCA and PCA–SVM.     

Principal component analysis based signal-to-noise ratio improvement for inchoate faulty signals: Application to ball bearing fault detection

This paper addresses the development of an algorithm that can improve the signal-to-noise ratio (SNR) in inchoate faulty signals. The removal of noise and preservation of fault information components cannot be easily achieved. Many techniques for SNR improvement in healthy signals rely on frequency bands. Such techniques have been proven to be efficient in improving the SRN by filtering out frequency bands (FoFBs). However, these techniques cannot reduce noise and preserve fault information when dealing with inchoate faulty signals. Thus, a feature extraction technique based on statistical parameters, which are free from Gaussian noise, is proposed in this paper. The proposed signal subspace-based approach for SNR improvement in inchoate faulty signals is based on a modified principal component analysis (PCA), in which the optimal subspace is selected via a cumulative percent of variance (CPV) criterion and the test statistic condition of the true information loss, which has the tendency to alleviate the impact of Gaussian and non-Gaussian noise and provides useful time domain analysis for non-stationary signals such as vibration, in which spectral contents vary with respect to time. Furthermore, the modified PCA algorithm is combined with a low-pass filter (LPF) to achieve an optimum balance between noise reduction efficiency and the conservation of inchoate fault information. The proposed PCA-LPF algorithm is compared with different filters under different noise levels to find the most efficient approach in terms of optimizing the trade-off between noise reduction efficiency and precision of inchoate fault information conservation, with the final goal of improving the fault detection capability. Further, the performance of the proposed PCA-LPF algorithm was demonstrated with an experimental study on vibration-based ball bearing fault detection.