电池系统的故障特征以及多故障的诊断与识别

高比能的锂电池系统广泛应用于储能与动力电源,电池系统的故障诊断技术是其安全、长效工作的重要保障。但锂电池化学性质特殊,故障类型难以识别,增加了电池系统的安全风险。为提高故障诊断与类型识别的准确性,提高电池系统安全性,需要认识发生不同故障时的电、热、化学特征。综述了电池系统的故障类型,并系统地总结和分析了电池系统单电池、连接、传感器等故障的电、热、化学信号特征。提出了内部电化学参数是可靠判别传感器故障与各种电池早期故障的关键特征,电化学阻抗谱是获取内部特征参数的有效方法;从电压波动性出发,电流与电压相关系数是判别传感器故障与连接故障的关键;此外,电池系统的特殊连接结构也是区分不同故障的重要手段。     

基于KPCA特征集成算法的SOFC系统多故障识别

针对固体氧化物燃料电池系统多模式、非线性及高维等特点,提出基于数据驱动的模式识别方法。首先用核主成分分析特征集成算法提取故障特征,然后在特征空间中使用多项式逻辑斯谛回归算法进行故障诊断。实验结果表明:核主成分分析特征集成算法可以全面提取出故障特征,能够大幅提高后续分类器故障的识别效果。     

辊压机液压系统的故障特征诊断

辊压机液压系统的故障特征诊断王卫东江苏省南京龙潭水泥厂（２１００３４）我厂于１９９４年初在生料车间与水泥车间安装使用了３台辊压机，通过近一年的调试，在提高粉磨效率方面起到了很大的作用。我厂的３台辊压机均采用两辊单独驱动相向旋转，一个辊固定一个辊浮动。...     

大型装备液压系统的故障再现及诊断平台的设计

通过分析液压系统和电气系统的相似性,以电气系统代替液压系统为原理,实现了液压系统故障的再现,以电压和电流仿真压力和流量参数进行故障诊断。通过故障的分析查找和定位等步骤,可以进行教学和维修人员培训。     

基于SVM的制冷系统多故障并发检测与诊断

因现实中单发故障的多样性,以及各故障并发时可能存在的协同作用,使并发故障成为故障诊断界难点之一。利用支持向量机（SVM）优良的模式识别能力,分别与单标识（mL）及多标识（ML）技术结合,构建可用于并发故障检测与诊断的模型,应用于制冷机组双故障并发时的检测与诊断。结果表明,ML-SVM模型表现突出,训练时无需并发故障数据,却可用于并发故障的检测与诊断,且性能优良,总体诊断准确率（CR）达99.902%,故障检测及对并发故障的识别率甚至高于采用并发故障训练时的模型,具有良好应用前景。     

UPS电源的电池老化造成DCS系统规律性跳停故障

##正##我公司回转窑系统的控制采用杭州和利时公司的SMARTPRO系统,并分为2个控制站,10号站主要负责立磨部分,因为距离较远,所以通过光纤与上位机联系;11号站主要负责窑系统,通过网线与上位机联系,此系统运行将近三年,一直都很稳定。     

基于加权统计特征KICA的故障检测与诊断方法

针对核独立元分析（kernel independent component analysis, KICA）在非线性动态过程中对微小故障检测率低的问题,提出一种基于加权统计特征KICA（weighted statistical feature KICA, WSFKICA）的故障检测与诊断方法。首先,利用KICA从原始数据中捕获独立元数据和残差数据;然后,通过加权统计特征和滑动窗口获取改进统计特征数据集,并由此数据集构建统计量进行故障检测;最后,利用基于变量贡献图的方法进行过程故障诊断。与传统KICA统计量相比,所提方法的统计量对非线性动态过程中的微小故障具有更高的故障检测性能。应用该方法对一个数值例子和田纳西-伊斯曼（Tennessee-Eastman, TE）过程进行仿真测试,仿真结果显示出所提方法相对于独立元分析（ICA）、KICA、核主成分分析（kernel principal component analysis, KPCA）和统计局部核主成分分析（statistical local kernel principal component analysis, SLKPCA）检测的优势。     

液压系统数据采集与故障预测研究

机械装备液压系统的参数数据,从装备出厂到使用出现显性故障,都在随使用时间演变着。液压系统性能的好坏,可以用泄漏量这一关键的参数变化来度量。利用液压数据采集器进行采集液压系统泄漏量,结合数据分析,就可以实现液压故障的预测。以某注塑机液压系统为研究对象,在无故障和有故障状态下,分别采集了液压系统的泄漏量数据和油液的污染状态数据,通过数据分析,可以明显地发现液压系统的故障部位,验证了液压数据采集技术对液压故障预测的有效性。     

液压系统压力故障的诊断与排除

介绍液压系统压力故障的诊断及排除方法,重点介绍液压泵,溢流阈,减压 及其它原因引起的故障,对其进行诊断并提出排除的方法。     

基于特征聚类与特征选择算法的SOFC系统故障定位

提出一种基于DBSCAN特征聚类的改进随机森林特征选择算法对SOFC系统的故障进行定位。该方法通过DBSCAN聚类算法挖掘变量间的相关关系,将变量聚类,再挑选出特征选择结果。实验表明:该方法不仅可以筛选出与故障强相关的特征,还能尽可能地减少特征间的冗余,可以高效、快速、准确地对故障进行定位。     

Distributed fault diagnosis of heating,ventilation, and air conditioning systems

The problem of distributed fault detection and isolation (FDI) for heating, ventilation, and air conditioning (HVAC) systems has been addressed in this work. First, a linear model is identified for subunits of an HVAC system. Next, a local FDI (LFDI) framework is designed for each unit under consideration. A distributed FDI architecture is designed where the LFDI frameworks communicate to exchange information to achieve enhanced FDI in each unit. As a result, each LFDI framework functions as intended even in the presence of faults that affect multiple units. Effectiveness of the proposed distributed FDI framework is shown for various commonly occurring fault scenarios. © 2018 American Institute of Chemical Engineers AIChE J, 65: 640–651, 2019     

基于模型的锂离子电池SOC及SOH估计方法研究进展

电池管理系统是保证锂离子电池高效、安全运行的重要手段。在电池管理系统功能中,电池状态估计,特别是荷电状态（state of charge,SOC）估计和健康状态（state of health,SOH）估计至关重要。SOC/SOH不仅与全生命周期内电池安全运行直接相关,也是其他功能有效实现的必要前提。本文围绕模型类电池状态估计方法,综述了国内外在锂离子电池模型构建、SOC及SOH估计方法方面的研究进展;指出了模型类状态估计方法存在的难点和局限,提出了今后研究重点。     

Estimation and fault diagnosis for non-linear system with time-varying faults and measurement noises: Application on two CSTRs in series

A continuously stirred tank reactor (CSTR) is largely used in water treatment and in chemical and biological processes. It is characterized by a complex non-linear behaviour. Operating large reactors in industry can be expensive, so a common trick used to reduce costs is to operate multiple CSTRs in series. Consequently, the CSTR is usually exposed to faults and noises. This paper addresses the design of a robust observer for estimation and fault diagnosis strategy on two CSTRs in series. The considered system is affected simultaneously by time-varying actuator and sensor faults with measurement noises. The Takagi-Sugeno multimodel approach is proposed to transform the non-linear model into an interpolation of several linear sub-models with non-measurable premise variables. The purpose of this brief is to provide the state and the fault estimation for the considered system using a proportional multiple integral (PMI) unknown input observer. The exponential stability conditions are studied with the Lyapunov theory and L2 optimization and formulated in terms of linear matrix inequalities. In addition, a comparative study with a PMI observer is conducted. Finally, the proposed observer is used for time-varying fault detection and isolation.     

Hierarchical monitoring of industrial processes for fault detection,fault grade evaluation,and fault diagnosis

Traditional process monitoring methods cannot evaluate and grade the degree of harm that faults can cause to an industrial process. Consequently, a process could be shut down inadvertently when harmless faults occur. To overcome such problems, we propose a hierarchical process monitoring method for fault detection, fault grade evaluation, and fault diagnosis. First, we propose fault grade classification principles for subdividing faults into three grades: harmless, mild, and severe, according to the harm the fault can cause to the process. Second, two‐level indices are constructed for fault detection and evaluation, with the first‐level indices used to detect the occurrence of faults while the second‐level indices are used to determine the fault grade. Finally, to identify the root cause of the fault, we propose a new online fault diagnosis method based on the square deviation magnitude. The effectiveness and advantages of the proposed methods are illustrated with an industrial case study. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2781–2795, 2017     

基于相关系数的过程系统故障检测与诊断方法

故障检测和故障诊断对提高控制系统的安全性具有重要意义。通过对过程变量之间的相关性变化与过程装置故障之间关系进行深入分析,提出了一种基于过程变量相关系数约束的过程故障诊断方法。对相关过程变量定义基于相关系数（含相关系数、多重相关系数、偏相关系数）约束的过程诊断函数,通过考察相关系数和诊断函数的变化,对与其所涉及变量相关的装置是否发生故障做出判断。如果装置或系统发生故障,则会引起相关系数和诊断函数值发生变化,可通过诊断函数值进行逻辑推断,最终确定故障位置和故障装置。最后通过一个精馏塔的实际工程案例,验证了该方法的有效性。     

A novel imbalanced fault diagnosis method based on area identification conditional generative adversarial networks

As a vital technology for ensuring the stable operation of industrial equipment, fault diagnosis has received a lot of research in recent years. Most complex industrial processes are in normal working conditions during operation, so the amount of data collected under normal working conditions is much larger than that under fault working conditions. The uneven number of samples will lead to the imbalance of datasets and make it a challenging task to assure the overall accuracy. To address the issue, an innovative imbalanced fault diagnostic approach based on area identification conditional generative adversarial networks (AICGAN) is proposed. First, considering the imbalance between normal data (majority data) and fault data (minority data), a hybrid data generation method combining over-sampling and AICGAN generator is proposed, which effectively extends the limited minority data and overcomes the inclination to majority data to some extent. On one hand, the over-sampling algorithm reduces the impact of dataset imbalance on the AICGAN training process by linear interpolation. On the other hand, the trainable generator can create samples similar to real samples by learning the generation principle so as to enrich the minority data information and reduce the sample stacking caused by linear synthesis. The two sample production methods complement each other. Combining the raw samples, over-sampled samples, and samples generated by generator, a new dataset is constructed. Second, the new dataset is used to train the AICGAN discriminator. In addition, in order to generate samples with higher value, an auxiliary discrimination layer is added to the discriminator to control the pattern of generated samples. Third, the balanced dataset containing the linear synthesis samples and the samples generated by the trained generator are put into the classifier to obtain the fault diagnosis. The effectiveness of the proposed approach for fault diagnosis based on AICGAN is verified using the three-phase flow facility (TFF) dataset and the Tennessee Eastman (TE) dataset. The experimental results demonstrate that the AICGAN-based fault diagnosis method achieves high F1 scores on the imbalanced dataset.     

A novel dynamic bayesian network‐based networked process monitoring approach for fault detection,propagation identification,and root cause diagnosis

A novel networked process monitoring, fault propagation identification, and root cause diagnosis approach is developed in this study. First, process network structure is determined from prior process knowledge and analysis. The network model parameters including the conditional probability density functions of different nodes are then estimated from process operating data to characterize the causal relationships among the monitored variables. Subsequently, the Bayesian inference‐based abnormality likelihood index is proposed to detect abnormal events in chemical processes. After the process fault is detected, the novel dynamic Bayesian probability and contribution indices are further developed from the transitional probabilities of monitored variables to identify the major faulty effect variables with significant upsets. With the dynamic Bayesian contribution index, the statistical inference rules are, thus, designed to search for the fault propagation pathways from the downstream backwards to the upstream process. In this way, the ending nodes in the identified propagation pathways can be captured as the root cause variables of process faults. Meanwhile, the identified fault propagation sequence provides an in‐depth understanding as to the interactive effects of faults throughout the processes. The proposed approach is demonstrated using the illustrative continuous stirred tank reactor system and the Tennessee Eastman chemical process with the fault propagation identification results compared against those of the transfer entropy‐based monitoring method. The results show that the novel networked process monitoring and diagnosis approach can accurately detect abnormal events, identify the fault propagation pathways, and diagnose the root cause variables. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2348–2365, 2013     

基于深度学习的化工过程故障检测与诊断研究综述

化工过程的故障检测与诊断对于现代化工系统的可靠性和安全性具有重要意义.深度学习作为一项新兴的技术,引起了学术界和工业界的广泛关注.从方法的角度出发,将基于深度学习的化工过程故障检测与诊断技术分为:基于自动编码器的方法、基于深度置信网络的方法、基于卷积神经网络的方法和基于循环神经网络的方法,并分别对4种方法的最新研究进展...