故障检测与诊断技术在暖通空调领域的应用和展望

介绍了暖通空调系统(HVAC)故障检测与诊断(FDD)的概念,发生故障的原因以及主要的故障检测与诊断方法,比较了常用的故障检测与诊断方法的优缺点,分析了国内、外暖通空调故障检测与诊断的研究现状和应用情况,对暖通空调故障检测与诊断的发展过程与方向进行了总结和展望。     

基于遗传算法和支持矢量机参数优化的制冷机组故障检测与诊断研究

针对制冷机组故障诊断中特征多、诊断准确率低的特点,提出一种复合诊断模型,利用遗传算法搜索特征空间,与带参数优化的支持矢量机(Support vector machine,SVM)结合,同时进行故障特征提取和模型训练。用该模型研究7种典型的制冷机组故障,从64个原始特征中筛选出8个与试验辅助系统关系甚微、均十分靠近核心制冷循环的特征,作为故障指示特征,总体诊断准确率从96.95%提高到99.53%,测试时间下降70%以上。用命中率和虚警率评价模型对各故障的诊断性能,所提复合模型除个别故障外,均优于无特征提取及带主元分析特征提取的SVM模型。复合模型在制冷机组故障诊断中有良好的应用前景。     

基于粗糙集的逻辑故障树方法及其应用

故障树是一种分析复杂系统可靠性、诊断系统故障的有效方法。在传统的故障树方法中 ,故障树的生成通常靠人工完成。故障知识的获取以及故障树结构的确定一直是有待解决的瓶颈问题。这里结合粗糙集、专家系统及人工智能等理论 ,提出了构造逻辑故障树进行故障诊断的方法并给出了相应的故障树评价标准。这种方法利用粗糙集对知识系统的知识发现和知识提取能力 ,从系统运行状态样本中建立基于知识的故障树模型。通过实例讨论了如何运用该方法对工业监控过程进行故障建模 ,检测系统运行过程中所发生的故障。     

基于观测器的故障检测与诊断

本文论述了基于观测器的故障检测与诊断技术的研究现状,重点放在最新的成就:自适应观测器,未知输入观测器,以及产生鲁棒性残差的各种方法。还论述了故障诊断的整个过程,最后指出了该方法有待进一步研究的主要问题及其发展趋势。     

基于Petri网的现场总线系统故障容错分析

在现场总线系统的使用过程中 ,可能会出现各种故障 ,这不仅会影响到设备的安全运行 ,也会对人身安全造成威胁。为此 ,在介绍 Profibus现场总线系统的基础上 ,定义了现场总线系统的故障类型 ,并建立了相应的基于 Petri网的现场总线系统的故障容错模型。通过分析容错模型的运行机理 ,清晰的揭示了系统在发生故障后的行为 ,为进一步的研究打下了良好的基础 ,提供一个新的思路。     

X2010A龙门铣床故障分析与解决措施

针对X2010A龙门铣床在使用多年后出现的爬行问题,提出了从电器、机械及液压3个方面逐个进行排查的解决方案,该方案可以准确诊断龙门铣床在运行过程中的故障,提高了龙门铣床的使用寿命。     

液压AGC系统故障诊断专家系统的实现

针地板带轧机液压ＡＧＣ系统,根据专家系统基本原理,结合古典信号处理方法、模糊理论、神经网络理论,建立了系统故障诊断系统的结构形式和学习算法。利用模糊诊断理论进行模糊推理以解决系统故障的实时诊断,利用神经网络对模糊推理模型进行训练以提高诊断的准确率,并可对未知的知识进行了学习和补充。开发了液压厚度自动控制（ＡＧＣ）系统故障诊断专家系统软件,通过实验证明所用方法有效。     

多变量统计分析方法在制冷故障检测与诊断中的应用研究

研究了多变量统计分析方法在制冷装置故障检测和诊断中的应用。对ASHRAE资助下的一组实验数据进行预处理,对其进行故障检测和故障诊断。对于故障检测,可利用平方预测误差(Q统计量)等统计控制变量来判断系统是否在正常运行状态。而对于故障诊断,第一次尝试采用各变量对于平方预测误差的负荷结合各变量的变化率来得到其对于平方预测误差异变的贡献率。从而快速利用变量的变化方向和程度判断故障类型。从结果分析,这种方法可以在众多变量中过滤掉不显著的变化,迅速找到故障主因。利用多变量统计分析方法可以实现对制冷装置的运行状态进行实时监控和诊断。     

Multiple sensor fault diagnosis for dynamic processes

Modern industrial plants are usually large scaled and contain a great amount of sensors. Sensor fault diagnosis is crucial and necessary to process safety and optimal operation. This paper proposes a systematic approach to detect, isolate and identify multiple sensor faults for multivariate dynamic systems. The current work first defines deviation vectors for sensor observations, and further defines and derives the basic sensor fault matrix (BSFM), consisting of the normalized basic fault vectors, by several different methods. By projecting a process deviation vector to the space spanned by BSFM, this research uses a vector with the resulted weights on each direction for multiple sensor fault diagnosis. This study also proposes a novel monitoring index and derives corresponding sensor fault detectability. The study also utilizes that vector to isolate and identify multiple sensor faults, and discusses the isolatability and identifiability. Simulation examples and comparison with two conventional PCA-based contribution plots are presented to demonstrate the effectiveness of the proposed methodology.     

Fault detection and diagnosis for singular stochastic systems via B-spline expansions

This paper deals with the problem of fault detection and diagnosis (FDD) for singular stochastic systems. The outputs of singular stochastic systems are described by probability density functions (PDFs) based on square root B-spline expansions. Then, two non-linear observers are designed for the FDD. The conditions of stability of the correlative error estimation systems are given by using linear matrix inequalities (LMIs). Finally, the simulation results are presented to indicate that the approach can detect faults and estimate the size of faults.     

基于模糊神经网络的液压系统故障诊断方法

根据专家系统基本原理,结合古典信号处理方法、模糊理论、神经网络理论,建立了系统故障诊断系统的结构形式和学习算法。以此为基础编制了厚度自动控制（AGC）系统故障诊断专家系统软件。利用模糊诊断理论进行模糊推理以解决系统故障的实时诊断,利用神经网络对模糊推理模型训练以提高诊断的准确率,并可对未知的知识进行学习和补充。通过实验证明所用方法有效。     

Creating an automated chiller fault detection and diagnostics tool using a data fault library

Reliable, automated detection and diagnosis of abnormal behavior within vapor compression refrigeration cycle (VCRC) equipment is extremely desirable for equipment owners and operators. The specific type of VCRC equipment studied in this paper is a 70-ton helical rotary, air-cooled chiller. The fault detection and diagnostic (FDD) tool developed as part of this research analyzes chiller operating data and detects faults through recognizing trends or patterns existing within the data. The FDD method incorporates a neural network (NN) classifier to infer the current state given a vector of observables. Therefore the FDD method relies upon the availability of normal and fault empirical data for training purposes and therefore a fault library of empirical data is assembled. This paper presents procedures for conducting sophisticated fault experiments on chillers that simulate air-cooled condenser, refrigerant, and oil related faults. The experimental processes described here are not well documented in literature and therefore will provide the interested reader with a useful guide. In addition, the authors provide evidence, based on both thermodynamics and empirical data analysis, that chiller performance is significantly degraded during fault operation. The chiller's performance degradation is successfully detected and classified by the NN FDD classifier as discussed in the paper's final section.     

主元分析方法的故障可检测性研究

主元分析（PCA）是一种有效的多元统计过程监测方法，PCA监测方法不依赖于过程的精确数学模型，这使得其难以对故障的可检测性问题进行系统的研究，基于故障子空间的描述方式，本文在主要元子空间的残差子空间中分别讨论了PCA故障可检测性的充分和必要条件，并提出了临界故障值的概念，通过对双效蒸发过程的仿真故障检测，表明所获得的结果能较好地刻画PCA的故障检测行为。     

An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity Fault Diagnosis in Induction Motors

Induction motors (IMs) are commonly used in various industrial applications. To improve energy consumption efficiency, a reliable IM health condition monitoring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is proposed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are synthesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air-gap eccentricity diagnosis. The effectiveness of the proposed harmonic synthesis technique is examined experimentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.     

Isolability of faults in sensor fault diagnosis

A major concern with fault detection and isolation (FDI) methods is their robustness with respect to noise and modeling uncertainties. With this in mind, several approaches have been proposed to minimize the vulnerability of FDI methods to these uncertainties. But, apart from the algorithm used, there is a theoretical limit on the minimum effect of noise on detectability and isolability. This limit has been quantified in this paper for the problem of sensor fault diagnosis based on direct redundancies. In this study, first a geometric approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input-output or a Principal Component Analysis (PCA) based model. The simplicity of this technique, compared to the existing methods of sensor fault diagnosis, allows for more rational formulation of the isolability concepts in linear systems. Using this residual generator and the assumption of Gaussian noise, the effect of noise on isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Finally, some numerical examples are presented to clarify this approach.     

不平衡分布的数据驱动故障诊断的研究进展

故障诊断是工业系统健康监测的重要内容，现有的数据驱动故障诊断方法多是利用类别平衡的数据集进行建模的。但在实际应用中，工业系统往往产生大量类别不平衡的样本，给数据驱动故障诊断带来挑战。这一问题受到了学术界和工业界的广泛关注，围绕该方面的研究取得了丰硕的成果。但是，目前针对不平衡分布的数据驱动故障诊断的研究进展综述相对较少，因此无法明确现实的挑战以及未来的研究方向。本文针对不平衡分布的约束问题，从数据驱动诊断方法和诊断应用场景这两个方面综述了国内外的研究进展，并提出了面临的挑战及未来的展望，为故障诊断的研究与应用提供参考。     

Fault detection and diagnosis based on particle filters combined with interactive multiple-model estimation in dynamic process systems

A new approach to fault detection and diagnosis (FDD) is developed for nonlinear stochastic dynamic process systems in this paper. It is called PFs-IMM, which combines particle filters (PFs) and the interactive multiple model (IMM) estimation. In this method, a multiple-model estimation scheme is first formulated to describe the complex process system poorly represented by a single model. The IMM algorithm can deal with abrupt changes in the behavior of operating processes. The residuals of the multiple models are examined for the likelihood of each model. A decision rule is employed to adaptively determine which model is the most appropriate one at each time step. Then based on IMM, a set of PFs run in parallel is used to estimate the states and the reconciled measurements even when the operating mode changes. Each of the PFs utilizes a particular mode to derive the estimation of the state variables as well as the reconciliation of the measured variables based on the probabilistic weighting scheme. From the multiple filters, the interaction among PFs allows the fusing of dynamic estimates. To achieve higher sensitivity to faults and more robustness to disturbances and noises, a new fault index function is developed for FDD. The proposed PFs-IMM approach provides an integrated framework. It can estimate the current operational or faulty mode of the system and derive the overall state estimation and the measurement reconciliation as well. The simulation solutions to the problems are obtained to demonstrate the effectiveness of the proposed method in highly nonlinear dynamic processes.     

汽车发动机常见故障与处理方法分析

汽车行驶和应用过程中发动机属最常见的故障发生位置,一旦发动机出现故障,很有可能导致车身整体瘫痪,从而无法行驶。而汽车发动机比较常见的故障类型也比较多,包括像点火系统故障问题、发动机漏油、发动机积碳严重、发动机漏水、发动机高温、发动机烧油或者发动机机油增多、异响、异常抖动等等,本文基于汽车发动机比较常见的故障类型分析了具体的故障处理方法,以供参考。     

Detection of stator winding faults in induction motors using three-phase current monitoring

The objective of this paper is to propose a new method for the detection of inter-turn short circuits in the stator windings of induction motors. In the previous reported methods, the supply voltage unbalance was the major difficulty, and this was solved mostly based on the sequence component impedance or current which are difficult to implement. Some other methods essentially are included in the offline methods. The proposed method is based on the motor current signature analysis and utilizes three phase current spectra to overcome the mentioned problem. Simulation results indicate that under healthy conditions, the rotor slot harmonics have the same magnitude in three phase currents, while under even 1 turn (0.3%) short circuit condition they differ from each other. Although the magnitude of these harmonics depends on the level of unbalanced voltage, they have the same magnitude in three phases in these conditions. Experiments performed under various load, fault, and supply voltage conditions validate the simulation results and demonstrate the effectiveness of the proposed technique. It is shown that the detection of resistive slight short circuits, without sensitivity to supply voltage unbalance is possible.