基于DPCA方法的故障检测与诊断分析

主元分析(Prlnclpal Component Analysis，PCA)已广泛应用于复杂工业过程的运行状态监控。然而，传统的PCA方法仅构造了生产过程的静态线性关系，无法从根本上有效处理具有较强动态特性的实际工业生产过程。动态主元分析(Dynamic PCA，DPCA)是一种将传统PCA分析推广到动态多变量过程的方法，但其较大的计算负荷阻碍了其实际应用。本文对文献中的DPCA作了算法上的简化，减少了实施中的计算量，并将其应用于重油分馏塔的动态运行故障监测与诊断。研究结果表明了方法的有效性。     

Graphical enhancement to support PCA-based process monitoring and fault diagnosis

Principal component analysis (PCA) for process modeling and multivariate statistical techniques for monitoring, fault detection, and diagnosis are becoming more common in published research, but are still underutilized in practice. This paper summarizes an in-depth case study on a chemical process with 20 monitored process variables, one of which reflects product quality. The analysis is performed using the PLS - Toolbox 2.01 with MATLAB, augmented with software which automates the analysis and implements a statistical enhancement that uses confidence limits on the residuals of each variable for fault detection rather than just confidence limits on an overall residual. The newly developed graphical interface identifies and displays each variable's contribution to the faulty behavior of the process; and it aids greatly in analyzing results. The case study analyzed within shows that using the statistical enhancement can reduce the fault detection time, and the automated graphical interface implements the enhancement easily.     

Decision tree and PCA-based fault diagnosis of rotating machinery

After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.     

Improved RBF network application in analog circuit fault isolation

One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function (RBF) neural network is proposed,which is applied to analog circuit fault isol...     

Sensor fault detection and isolation via high-gain observers: application to a double-pipe heat exchanger

This paper deals with fault detection and isolation (FDI) in sensors applied to a concentric-pipe counter-flow heat exchanger. The proposed FDI is based on the analytical redundancy implementing nonlinear high-gain observers which are used to generate residuals when a sensor fault is presented (as software sensors). By evaluating the generated residual, it is possible to switch between the sensor and the observer when a failure is detected. Experiments in a heat exchanger pilot validate the effectiveness of the approach. The FDI technique is easy to implement allowing the industries to have an excellent alternative tool to keep their heat transfer process under supervision. The main contribution of this work is based on a dynamic model with heat transfer coefficients which depend on temperature and flow used to estimate the output temperatures of a heat exchanger. This model provides a satisfactory approximation of the states of the heat exchanger in order to allow its implementation in a FDI system used to perform supervision tasks.     

基于双解析模型的故障隔离与估计方案及应用

系统分析了基于观测器的故障检测与隔离方法存在的问题,创新性提出了一种基于标称模型和未知输入观测器的故障诊断方案—双解析方案。它从故障观测而非状态估计的角度设计未知输入观测器,并根据未知输入观测器与标称系统之间的状态误差修正并替换未知输入观测器的状态估计误差,从而构成实际可行的故障隔离与估计的解析表达式。该方案应用于三轴稳定卫星执行器/敏感器故障诊断,仿真设置的五种飞轮/陀螺组合故障均被准确隔离及估计,表明了该方案的有效性。从设计及应用过程可以看出,双解析方案设计简单、无存在性条件约束,有效拓展了基于观测器故障诊断的应用范围及深度。     

基于PCA的系统故障监测方法及其三相异步电动机仿真研究

利用主元分析，建立主元模型，可以在保留原有数据信息特征的基础上消除变量关联和部分系统干扰，简化分析复杂度。基于主元模型，结合平方预测误差统计图，可以实现对运行系统的故障监测与诊断分析。通过在一个三相绕组异步电动机上的应用，说明了此方法的有效性。     

柱塞泵的合理使用与故障分析

本文根据对冶金液压设备中柱塞泵应用实践与研究，提出合理使用柱塞泵的基本原则，对柱塞泵常见故障产生的原因、排除方法进行了分析，以利于柱塞泵研究设计、生产制造、使用维护者提高柱塞泵的技术性能，对故障进行预防、诊断与排除。     

Multiple manifolds analysis and its application to fault diagnosis

A novel approach to fault diagnosis is proposed using multiple manifolds analysis (MMA) to extract manifold information from the vibration signals collected from a mechanical system. The basic idea of MMA is to reconstruct a manifold by embedding time series into a high-dimensional phase space. The tangent direction of the neighborhood for each point is then used to approximate its local geometry. The variation of the multiple manifolds representing different states of the mechanical system can be revealed by performing multi-way principal component analysis. The vibration signals acquired from roller bearings are employed to validate the proposed algorithms. Test results show that the proposed MMA-based approach can interpret different machine conditions and is effective to the fault diagnosis, and the MMA-based fault clustering and trend analysis algorithms have outperformed the conventional fault diagnosis methods.     

砼泵分配阀故障的可靠性诊断

通过对混凝土泵分配阀不动作故障的分析,运用故障树理论,建立了混凝土泵分配阀不动作故障的故障树模型,通过对故障树模型的定量计算,找出了故障发生的部位,可以得出故障树在分析机械设备故障方面是十分有效的。     

螺杆泵及其应用

螺杆泵问世较晚,1930年法国工程师雷涅-摩伊诺创造了第一台单螺杆泵。1931年瑞典IMO公司发明三螺杆泵。1943年德国鲍曼（Bornemann）公司生产外置轴承的双螺杆泵,从1944年开始批量生产。1959年以来,德国费里茨-基伯格对单螺杆泵的发展有重大的影响,他设计和制造了德国的第一台单螺杆泵．     

基于改进HSMM的设备故障预测方法研究

针对传统隐半马尔科夫模型(HSMM)在故障诊断和预测应用中存在的不足,对传统HSMM做了以下改进:一是将状态持续时间概率分布和监测值概率分布连续化,并假定其服从威布尔分布;二是基于状态开始时间的识别,提出了状态剩余持续时间;三是提出了时变转移概率的概念,给出了各时刻转移概率的计算方法。确立了基于改进HSMM的故障诊断和预测的方法体系,给出了故障诊断判据和设备剩余寿命的计算式。案例研究表明方法是合理有效的。     

Denoising with advanced stepwise false discovery rate control and its application to fault diagnosis

To extract defect features from the signal with background noise for fault diagnosis, a novel approach is proposed by using advanced false discovery rate procedure (AFDR). The main idea is based on controlling false discovery rate (FDR) through combination of all three stepwise procedures (step-up, step-down, step-up-down) and estimation of the number of true null hypotheses. The AFDR procedure differs from the standard FDR procedure in two respects, i.e., enhancing the efficiency by reducing the number of tested hypotheses and improving the power. The proposed procedure consists of two main steps: firstly, the signal is represented more parsimoniously in wavelet domain; secondly, a most appropriate stepwise FDR procedure is selected according to the character of wavelet coefficients. Both the numerical simulation results and the experimental results for bearing defect diagnosis show that the proposed approach is a competitive shrinkage method compared with other popular techniques.     

Process fault isolation based on transfer entropy algorithm

Complexity of industrial plants and their stringent environmental and safety regulations have necessitated early detection and isolation of process faults. All the existing fault isolation methods can be categorized into two general groups: model-based and data-based. Transfer entropy is a data-based method for measuring propagation direction of disturbance and finding its root cause. In this paper, a new transfer entropy-based method is proposed to isolate different process faults. The novelty of this paper lies in using the transfer entropy idea to generate distinct patterns of information flow among process variables, recognize their correlations in the context of the transferred information in any abnormal condition, and finally isolate different process faults. The experimental results clearly demonstrate the superiority of the proposed method to the conventional methods.     

基于改进重构贡献图的故障定位方法

针对重构贡献(RBC)方法仅适合单变量故障的定位及贡献图中出现拖尾效应(SE)的问题,本文提出一种基于改进重构贡献图(MRBCP)的故障定位方法。采用概率主元分析(PPCA)建立监视模型和统一量度的监视统计量,克服PCA方法中不同量度的监视统计量造成的诊断结果不一致的缺点。对于故障样本,以变量的重构监视统计量为贡献统计量,通过组合最大化思想对故障变量进行逐次定位。在历史故障信息未知的情况下,能够进行多变量故障的定位,然后在定位出的故障变量中进行贡献图分析,进一步对故障变量实现准确定位,从而避免了拖尾效应。通过数值案例和TE过程——实际化工过程的真实模拟过程进行实验,并与基本RBC方法、基于PCA的MRBCP方法进行比较,结果表明了所提方法的有效性。     

A fault detection and isolation filter for discrete linear systems

The problem of fault and/or abrupt disturbances detection and isolation for discrete linear systems is analyzed in this work. A strategy for detecting and isolating faults and/or abrupt disturbances is presented. The strategy is an extension of an already existing result in the continuous time domain to the discrete domain. The resulting detection algorithm is a Kalman filter with a special structure. The filter generates a residuals vector in such a way that each element of this vector is related with one fault or disturbance. Therefore the effects of the other faults, disturbances, and measurement noises in this element are minimized. The necessary stability and convergence conditions are briefly exposed. A numerical example is also presented.     

Sensor fault detection and isolation system for a condensation process

This article presents the design of a sensor Fault Detection and Isolation (FDI) system for a condensation process based on a nonlinear model. The condenser is modeled by dynamic and thermodynamic equations. For this work, the dynamic equations are described by three pairs of differential equations which represent the energy balance between the fluids. The thermodynamic equations consist in algebraic heat transfer equations and empirical equations, that allow for the estimation of heat transfer coefficients. The FDI system consists of a bank of two nonlinear high-gain observers, in order to detect, estimate and to isolate the fault in any of both outlet temperature sensors. The main contributions of this work were the experimental validation of the condenser nonlinear model and the FDI system.     

一种改进的小波神经网络在故障诊断中的应用

针对现有BP网络在故障诊断中存在的问题,提出将小波函数与神经网络结合构成小波网络,代替BP网络用于故障诊断。对其存在的易将未知故障化为某一已知故障的问题,提出将小波网络加以改进,对诊断结果做最后的验证,以确保诊断结果的正确,同时也能准确地发现新的故障,并将其另开新类。仿真实验表明小波网络较BP网络更适用于故障诊断,且对小波网络进行的改进对新故障的发现也很有效。     

Constrained independent component analysis and its application to machine fault diagnosis

For machine fault diagnosis the signals from working machine are always numerous, even uncountable, but there contains only a little useful information. Hence how to find out the useful signal from numerous signals, including noises, that is, how to only extract the desired fault signal is very attractive. This paper shows that the constrained independent component analysis (cICA) can solely extract desired faulty signal using some prior mechanical information. The methods of creating reference of cICA for machine diagnostics are discussed, and the effectiveness of the method is successfully verified by simulations and experiments.