基于差分的动态加权SVDD在多模态过程故障检测中的应用

传统SVDD作为一种单模态静态故障检测算法,对多模态动态过程故障的检测难以保证其检测的准确性和实时性。为了解决这一问题,提出一种基于近邻差分加权动态SVDD检测方法（NND-DWSVDD）。首先利用NND剔除数据多模态结构,保证过程数据服从单峰分布;对差分处理后的数据引入动态方法并加入权值将有用的信息凸显出来;最后利用SVDD方法建立监测模型实现在线监测。NND-DWSVDD提高了多模态动态过程故障检测率,对于多模态动态过程故障检测,NND-DWSVDD不要求多模型建模,只需单独的一个模型,符合单模态故障检测要求。通过多模态数值例子和半导体生产过程数据对该方法的有效性进行了验证。     

Fault detection and prediction scheme for nonlinear stochastic distribution systems

Model-based fault detection technique has a broad range of applications because of the small change to the system when the system state is known to be available and the low cost. For nonlinear stochastic distribution systems containing uncertain disturbance term, a model-based fault detection and failure time prediction scheme is proposed in this paper, and observers are designed to detect whether the incipient fault has occurred in the system. The residual is obtained by comparing the output of the actual system with the output of the observer. When the residual exceeds the threshold value obtained by derivation, it is determined that the fault has occurred in the system. The fault size can then be estimated in real time and used to determine the time to failure (TTF) or the remaining useful life of the system. The TTF of the system is obtained by comparing the magnitude of the current system fault with the fault threshold. Finally, the feasibility of the presented fault detection scheme is proved by the Lyapunov stability theory and the validity of the scheme is proved by computer simulation.     

Bearing fault prognosis based on health state probability estimation

In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.     

An Online Fault Detection Model and Strategies Based on SVM-Grid in Clouds

Online fault detection is one of the key technologies to improve the performance of cloud systems. The current data of cloud systems is to be monitored, collected and used to reflect their state. Its use can potentially help cloud managers take some timely measures before fault occurrence in clouds. Because of the complex structure and dynamic change characteristics of the clouds, existing fault detection methods suffer from the problems of low efficiency and low accuracy. In order to solve them, this work proposes an online detection model based on asystematic parameter-search method called SVM-Grid, whose construction is based on a support vector machine (SVM). SVM-Grid is used to optimize parameters in SVM. Proper attributes of a cloud system's running data are selected by using Pearson correlation and principal component analysis for the model. Strategies of predicting cloud faults and updating fault sample databases are proposed to optimize the model and improve its performance. In comparison with some representative existing methods, the proposed model can achieve more efficient and accurate fault detection for cloud systems.      

Weibull分布引进故障的软件可靠性增长模型

软件调试是复杂过程,可能会受到很多种因素的影响,例如调试资源分配、调试工具的使用情况、调试技巧等.在软件调试过程中,当检测到的故障被去除时,新的故障可能会被引进.因此,研究故障引进的现象对建立高质量的软件可靠性增长模型具有重要意义.但是到目前为止,模拟故障引进过程仍是一个复杂和困难的问题.虽然有许多研究者开发了一些不完美调试的软件可靠性增长模型,但是一般都是假设故障内容（总数）函数为线性、指数分布或者是与故障去除的数量成正比.这个假设与实际的软件调试过程中故障引进情况并不完全一致.提出一种基于Weibull分布引进故障的软件可靠性增长模型,考虑故障内容（总数）函数服从Weibull分布,并用相关的实验验证了提出的模型的拟合和预测性能.在用两个故障数据集进行的模拟实验中,实验结果指出：提出的模型和其他模型相比,有更好的拟合和预测性能以及更好的鲁棒性.     

A unified framework for fault detection and isolation of sensor and actuator biases in linear time invariant systems using marginalized likelihood ratio test with uniform priors

This study aims to present a fault detection and isolation (FDI) framework based on the marginalized likelihood ratio (MLR) approach using uniform priors for fault magnitudes in sensors and actuators. The existing methods in the literature use either flat priors with infinite support or the Gamma distribution as priors for the fault magnitudes. In the current study, it is assumed that the fault magnitude is a realization of a uniform prior with known upper and lower limits. The method presented in this study performs detection of time of occurrence of the fault and isolation of the fault type simultaneously while the estimation of the fault magnitude is achieved using a least squares based approach. The newly proposed method is evaluated by application to a benchmark CSTR problem using Monte Carlo simulations and the results reveal that this method can estimate the time of occurrence of the fault and the fault magnitude more accurately compared to a generalized likelihood ratio (GLR) based approach applied to the same benchmark problem. Simulation results on a benchmark problem also show significantly lower misclassification rates.     

基于多重局部重构模型的连续过程故障诊断

为了提高故障诊断性能, 本文对故障特征随时间发展变化的多样性进行了探讨分析. 本文揭示了故障过程呈现时变特性, 即故障过程在不同时段反映出不同的变量相关性, 提出了一种故障时段划分算法. 该算法将故障划分为不同时段, 在每一个时段中, 故障特征被认为是基本类似的. 在此基础上, 针对不同时段建立了不同的故障分解模型, 并揭示了不同故障状态与正常状态的关系. 通过划分不同故障特征, 可以区分不同的故障特征, 建立更精确的重构模型. 该方法很好地阐述了故障的演变行为特征, 能够更精确地进行故障重构从而确定故障原因. 通过在田纳西伊士曼仿真过程上的应用验证了该方法的可行性及诊断性能.     

基于DPCA方法的传感器故障检测与诊断

针对多传感器的相关时序测量数据,在假设只存在传感器故障的前提下,提出了一种基于动态主成分分析（DPCA）的传感器故障检测方法。根据测量数据建立传感器的DPCA模型,在该模型基础上利用T2和SPE统计量进行传感器的故障检测。同时,将基于主成分分析（PCA）模型的传感器有效度指标SVI推广应用于DPCA模型中。通过对污水处理系统中重要传感器的故障诊断仿真实验表明：该方法能有效地检测和识别出故障传感器。     

基于演算子的热过程执行机构故障诊断系统设计

This paper proposes an operator based fault detection method for an actuator fault of an aluminum plate thermal process with input constraints. Operator-based robust right coprime factorization (RCF) approach is utilized in this method. After developing a mathematical model of the thermal process, a robust tracking operator system is designed for the process with input constraints. Following this, design of the fault detection system is given by using operator-based robust RCF approach. Finally, experiment is conducted to support the proposed design method.     

一种基于非线性主成分回归的过程监测及故障诊断方法

近年来，统计过程监测方法在多变量过程监测领域得到广泛应用，但对于存在显著 非线性的过程，这类方法的性能往往不尽人意，而神经网络在处理非线性问题上具有卓越的 优势．本文将多变量投影方法和径向基神经网络良好的逼近能力结合起来，提出了一种基于 嵌入径向基网络的非线性主成分回归算法的过程监测及故障诊断方法．在三水箱实验装置上 进行的实验结果说明该方法确实能够有效地实现过程监测、快速地检测并诊断出故障状态．     

A possibilistic clustering approach to novel fault detection and isolation

In this paper, a new approach for fault detection and isolation that is based on the possibilistic clustering algorithm is proposed. Fault detection and isolation (FDI) is shown here to be a pattern classification problem, which can be solved using clustering and classification techniques. A possibilistic clustering based approach is proposed here to address some of the shortcomings of the fuzzy c-means (FCM) algorithm. The probabilistic constraint imposed on the membership value in the FCM algorithm is relaxed in the possibilistic clustering algorithm. Because of this relaxation, the possibilistic approach is shown in this paper to give more consistent results in the context of the FDI tasks. The possibilistic clustering approach has also been used to detect novel fault scenarios, for which the data was not available while training. Fault signatures that change as a function of the fault intensities are represented as fault lines, which have been shown to be useful to classify faults that can manifest with different intensities. The proposed approach has been validated here through simulations involving a benchmark quadruple tank process and also through experimental case studies on the same setup. For large scale systems, it is proposed to use the possibilistic clustering based approach in the lower dimensional approximations generated by algorithms such as PCA. Towards this end, finally, we also demonstrate the key merits of the algorithm for plant wide monitoring study using a simulation of the benchmark Tennessee Eastman problem.     

基于改进偏最小二乘法的多模态过程故障检测方法

针对传统的数据驱动方法偏最小二乘法（PLS）中存在的多模态数据故障检测效果不佳的问题,提出了一种新的故障检测方法——基于局部近邻标准化（LNS）的PLS（LNS-PLS）。首先,利用LNS方法对原始数据进行高斯化处理,在此基础上建立PLS的监控模型,确定T²和平方预测误差（SPE）的控制限;其次,对测试数据同样进行LNS标准化处理,再计算出测试数据的PLS监控指标来进行过程监视及故障检测,解决了PLS中无法处理多模态的问题。将所提方法应用于数值例子和青霉素生产过程,并将其测试结果与主成分分析（PCA）、K最近邻（KNN）、PLS等方法进行对比分析。实验结果表明,所提方法的故障检测效果优于PLS、KNN、PCA,该方法在分类及多模态过程故障检测方面有较高的准确性。     

基于空间故障网络的系统故障发生潜在可能性研究

为了在系统故障演化过程(system fault evolution process,SFEP)中,根据系统故障事件累计数据,获得当一些事件发生后对系统最终故障发生的影响,提出一种基于空间故障网络(SFN)的系统故障发生潜在可能性分析方法.该方法的特点是根据系统运行期间发生各类事件及事件间关系,建立事件关系数据库,绘制SFN.当某种工况下已发生一些事件后,根据这些事件的因果逻辑关系和传递概率,得到这些事件能否引起系统故障、故障模式及发生可能性.通过典型实例说明了该方法的使用过程和分析效果,表明其可适应大规模故障数据处理和SFEP故障发生潜在性分析.     

基于核主元分析与核密度估计的非线性过程故障监测与识别

在针对将核主元分析(kernel principal components analysis, KPCA)与基于高斯分布的控制限(control limits, CLS)相结合会降低其性能的问题, 提出了一种基于核主元分析与核密度估计(kernel principal components analysis-kernel density estimation, KPCA-KDE)相结合的非线性过程故障监测与识别方法. 该方法采用核密度估计(kernel density estimation, KDE)技术来估计基于KPCA的非线性过程监控的CLS. 通过研究KPCA和KPCA-KDE所有20个故障的检出率发现, 与相应的基于高斯分布的方法进行比较, KDE具有较高的故障检出率; 此外, 基于KDE的检测延迟等于或低于其他方法. 通过改变带宽和保留的主元数量进行故障检测, KPCA记录的FAR值较高, 相反, KPCA-KDE方法仍然没有记录任何假报警. 在田纳西伊斯曼过程(Tennessee Eastman, TE)上的应用表明, KPCA-KDE比基于高斯假设的CLS的KPCA在灵敏度和检测时间上都具有更好的监控性能.     

动车组转向架轴箱剩余寿命预测方法研究

动车组转向架轴箱的寿命作为衡量转向架性能的重要指标,主要受材料、工艺、质量、载荷、保养、工况等因素影响。为解决单一工况预测轴箱故障发生时间不准确问题,需充分考虑多工况因素,基于全生命周期构建转向架轴箱剩余寿命预测模型。本文通过对比分析多工况与轴箱的相互影响关系,采用大数据和机器学习算法,设计出一种基于长短记忆神经网络(LSTM)的轴箱相对温升与里程的剩余寿命预测方法。该方法能精确地刻画轴箱性能退化特征模型,可在动车运行过程中实时预测转向架轴箱故障发生率,较大幅度地提高动车组转向架轴箱剩余寿命预测的实效性、准确性。     

基于输出时滞方法的非均匀采样数据系统传感器故障检测(英文)

研究一般非均匀采样数据系统鲁棒传感器故障检测设计问题.首先,基于输出时滞方法将非均匀采样数据系统转换成具有时变时滞输出的连续系统;然后,选择故障检测滤波器作为残差产生器,并将故障检测设计问题描述成一个多目标优化问题,即连续时间过程噪声和离散时间测量噪声对残差信号的H∞范数小于一个给定值,同时传感器故障对残差信号的l2增益大于一个给定值,基于输入输出方法以矩阵不等式的形式给出该多目标优化问题有解的充分条件;进一步的,提出一个迭代算法来权衡噪声鲁棒性与故障灵敏度,并将矩阵不等式转换成可解的线性矩阵不等式.最后,对某型飞控系统的仿真实验验证了所提方法的有效性.     

基于动态EM-SHSMM的异常数据下设备健康预测研究*

针对设备退化过程中异常数据下的剩余有效寿命预测问题,提出了一种基于动态的期望最大化算法(EM)-分段隐半马尔可夫模型(SHSMM)预测方法。首先,基于SHSMM的理论框架,采用期望最大化参数自适应估计算法估计模型中的未知参数。其次,基于WGM(1,1)模型,提出动态前向后向灰色填充算法处理样本中的异常数据,并利用健康预测过程预测设备的剩余有效寿命。最后,通过实例分析对模型进行评价和验证。结果表明,提出的设备健康预测方法能有效解决异常数据的问题。     

Comprehensive subspace decomposition and isolation of principal reconstruction directions for online fault diagnosis

Reconstruction based fault diagnosis isolates the fault cause by finding fault subspace to bring the faulty data back to normal. However, the conventional reconstruction model was often defined using principal component analysis (PCA) to extract the general distribution information of fault data and may not well discriminate fault from normal status. It thus may fail to recover the fault-free data efficiently. To overcome the above problem, a relative principal component of fault reconstruction (RPCFR) modeling algorithm is proposed in the present work for fault subspace extraction and online fault diagnosis. Instead of directly modeling fault data to extract the reconstruction directions, the algorithm gives the original fault space a comprehensive decomposition according to its relationship with the normal process information. Those fault directions that can more efficiently characterize the effects of fault deviations relative to normal data are separated from the others and used for fault reconstruction. Its performance on online fault diagnosis is illustrated by the data from the Tennessee Eastman process.     

故障检测率不规则变化的软件可靠性模型

传统的NHPP(non-homogeneous Poisson process)模型在实际的测试当中被证明是成功的.但是,由于传统的NHPP模型用的是理想的假设,例如,假设故障检测率是常数、平稳变化和规律变化,模型的性能在实际的测试环境中总是受到损害.因此,提出一个基于NHPP的软件可靠增长模型,并且考虑故障检测率的不规则变化情况,这种变化符合故障检测率在实际的软件测试过程中的变化.通过相关的实验验证了所提出的NHPP模型的拟合和预测能力.实验结果表明:在用实际的故障数据进行拟合和预测的过程中,所提出的模型与传统的NHPP模型相比,有更好的拟合和预测性能.同时,也给出了所提出模型相应的置信区间.