水泥厂电气故障的查找

水泥厂电气设备种类多,容量大,电压级别高,电气设备的维护难度大.电源故障是电气装置的整体性故障,隐性危险大,偶然因素较小;而电气设备和元件的结构、性能、类别、功能等千差万别,没有固定的模式查找其故障.处理故障时,要分清是经常性故障还是偶然发生的;是启动状态还是运转状态或是调速状态发生的;要以初步分析,缩小故障范围.     

基于KECA的非线性工业过程故障检测与诊断新方法

提出了一种基于核熵成分分析(kernel entropy component analysis,KECA)的非线性过程故障检测与诊断新方法。该方法首先利用KECA获取过程数据的得分向量及非线性特征子空间;然后鉴于KECA可以以角结构的方式揭示数据中潜在的集群结构,设计了基于角度的监测指标VoA。该指标通过各得分向量之间的角度方差来描述变换后数据间的结构差异,并根据角度方差的变化情况实现故障检测;接着,为了在检测到故障后有效地进行故障识别,构建了KECA相似度因子来度量特征子空间的相似程度以识别故障模式;最后,以非线性数值案例及Tennessee Eastman过程进行仿真测试研究,结果验证了所提方法的可行性及有效性。     

Deep learning technique for process fault detection and diagnosis in the presence of incomplete data

In modern industrial processes, timely detection and diagnosis of process abnormalities are critical for monitoring process operations. Various fault detection and diagnosis (FDD) methods have been proposed and implemented, the performance of which, however, could be drastically influenced by the common presence of incomplete or missing data in real industrial scenarios. This paper presents a new FDD approach based on an incomplete data imputation technique for process fault recognition. It employs the modified stacked autoencoder, a deep learning structure, in the phase of incomplete data treatment, and classifies data representations rather than the imputed complete data in the phase of fault identification. A benchmark process, the Tennessee Eastman process, is employed to illustrate the effectiveness and applicability of the proposed method.     

Deep learning technique for process fault detection and diagnosis in the presence of incomplete data

In modern industrial processes, timely detection and diagnosis of process abnormalities are critical for monitoring process operations. Various fault detection and diagnosis (FDD) methods have been proposed and implemented, the performance of which, however, could be drastically influenced by the common presence of incomplete or missing data in real industrial scenarios. This paper presents a new FDD approach based on an incomplete data imputation technique for process fault recognition. It employs the modified stacked autoencoder, a deep learning structure, in the phase of incomplete data treatment, and classifies data representations rather than the imputed complete data in the phase of fault identification. A benchmark process, the Tennessee Eastman process, is employed to illustrate the effectiveness and applicability of the proposed method.     

基于MAF的传感器故障检测与诊断

针对工业控制系统中变量之间既存在线性相关性,且在时间结构上呈现自相关的特点,提出了一种基于最小/最大自相关因子(min/max autocorrelation factors, MAF)分析的传感器故障检测与诊断方法。首先,利用正常工况下的历史数据进行自相关因子分析,获得强自相关因子和弱自相关因子;在此基础上构造故障检测统计量,由核密度估计方法获得故障检测控制限,根据贡献图进行传感器故障定位。将所提出的方法应用于连续反应釜仿真过程的传感器故障检测与诊断,与经典的多变量统计方法——主元分析方法相比,所提出的方法能避免虚警,更快地检测缓变故障,并能更好地诊断和解释复杂故障。     

Design and control for recycle plants with heat-integrated separators

This work analyzes the tradeoff between steady-state economics and dynamic controllability for heat-integrated recycle plants. The process consists of one reactor, two distillation columns, and two recycle streams first studied by Tyreus and Luyben (Ind. Eng. Chem. Res. 32 (1993) 1154) and further explored by Cheng and Yu (A.I.Ch.E. J. 49 (2003) 682) and, in this work, the two distillation columns are heat integrated. The design problem differs from typical column sequencing and heat-integration design, because we can design the reactor composition. Optimal trajectories for heat-integrated recycle plants with direct and indirect sequences are analyzed as the reactor composition of C (z_C) varies. Provided with correct direction for heat integration, at any given z_C, the flowsheet is established for both sequences. It turns out the heat-integrated recycle plant with direct sequence is economically optimal throughout the entire range of z_C. For dynamic controllability, the reachable production range is identified as the recycle ratios (recycle flow rate/production rate) vary. Results show that the steady-state controllability deteriorates gradually as the degree of heat integration increases and, to the extreme, at the 50% energy saving line, we have lost one control degree of freedom. However, if the recycle plant is optimally designed (z_C≈0.6), acceptable turndown ratio is observed and little tradeoff between steady-state economics and dynamic operability may result. Finally, rigorous nonlinear simulations are used to test control performance of different process configurations (with and without heat integration). The results reveal that improved control can be achieved for well-designed heat-integrated recycle plants (compared to the plants without energy integration). More importantly, better performance is achieved with up to 40% energy saving and close to 20% saving in total annual cost.     

基于流程模拟的化工故障检测技术

提出了一种使用MATLAB仿真工具箱Simulink与动态模拟软件Aspen Dynamics相互调用来实现化工过程监测的方法.该方法具有以下优点:Aspen Dynamics能够快速建立精确的动态模型,具有完善的物性数据库,同时可以方便根据实际的化工过程对模型进行调整;使用Simulink仿真工具箱可以实时采集数据作为模型输入,同时完成对数据的必要处理.为了检测方法的可行性,将其应用于一个虚拟精馏过程来检验监测效果,结果表明, 其可以实现对存在生产计划变更过程的故障监测和无生产计划变更过程中故障的监测.     

用于过程故障诊断的自适应kernel学习网络分类器

提出一种统一的最小二乘kernel学习框架,将自适应kernel学习（AKL）网络辨识器推广为分类器,用于化工过程的故障诊断。推导了AKL分类器在向后缩减和向前增长两种情况下的递推算法,实现了对记忆样本长度的控制。该分类器无需利用历史故障数据,即可进行在线学习并建立过程诊断模型。通过对Tennessee Eastman（TE）过程的5种典型故障的诊断分析,验证了该方法的有效性。     

Nonstationary fault detection and diagnosis for multimode processes

Fault isolation based on data‐driven approaches usually assume the abnormal event data will be formed into a new operating region, measuring the differences between normal and faulty states to identify the faulty variables. In practice, operators intervene in processes when they are aware of abnormalities occurring. The process behavior is nonstationary, whereas the operators are trying to bring it back to normal states. Therefore, the faulty variables have to be located in the first place when the process leaves its normal operating regions. For an industrial process, multiple normal operations are common. On the basis of the assumption that the operating data follow a Gaussian distribution within an operating region, the Gaussian mixture model is employed to extract a series of operating modes from the historical process data. The local statistic T² and its normalized contribution chart have been derived for detecting abnormalities early and isolating faulty variables in this article. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Multi-linear model-based fault detection during process transitions

Process transitions due to startup, shutdown, product slate changes, and feedstock changes are frequent in the process industry. Experienced operators usually execute transitions in the manual mode as transitions may involve unusual conditions and nonlinear process behavior. Processes are therefore more prone to faults as well as inadvertent operator errors during transitions. Fault detection during transition is critical as faults can lead to abnormal situations and even cause accidents. This paper proposes a model-based fault detection scheme that involves decomposition of nonlinear transient systems into multiple linear modeling regimes. Kalman filters and open-loop observers are used for state estimation and residual generation based on the resulting linear models. Analysis of residuals using thresholds, faults tags, and logic charts enables on-line detection and isolation of faults. The multi-linear model-based fault detection technique has been implemented using Matlab and successfully tested to detect process faults and operator errors during the startup transition of highly nonlinear pH neutralization reactor in the laboratory.     

基于双近邻标准化和PCA的多阶段过程故障检测

现代工业产品的生产往往需要多个生产阶段,多阶段生产过程的故障检测成为一个重要问题。多阶段过程数据具有多中心、各工序数据结构不同等特征。针对多阶段过程数据的特征,提出了基于双近邻标准化和主元分析的故障检测方法（DLNS-PCA）。首先寻找样本的双层局部近邻集;其次使用双层局部近邻集的信息标准化样本,得到标准样本;最后在标准样本集上使用主元分析方法进行故障检测。双局部近邻标准化能够将各阶段数据的中心平移到同一点,并且调整各阶段数据的离散程度,使之近似相等,从而将多阶段过程数据融合为服从单一多元高斯分布的数据。进行了青霉素发酵过程故障检测实验,实验结果表明DLNS-PCA方法相对于PCA、KPCA、FDkNN等方法对多阶段过程故障具有更高的检测率。DLNS-PCA方法提高了多阶段过程故障检测能力。     

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...     

基于KSFDA-SVDD的非线性过程故障检测方法

慢特征分析(SFA)是一种无监督的线性学习算法,没有考虑过程数据的类别信息和非线性特征。针对此问题,提出一种基于核慢特征判别分析(KSFDA)和支持向量数据描述(SVDD)的非线性过程故障检测方法KSFDA-SVDD。该方法首先利用核技巧将数据从原始空间映射到高维空间,然后通过最大化正常工况数据和故障模式数据之间伪时间序列的时间变化同时最小化正常工况数据内部伪时间序列的时间变化计算判别矩阵,最后利用SVDD描述采用判别矩阵降维后的正常工况数据的分布域,构建监控统计量检测过程故障。在连续搅拌反应器(CSTR)过程上的仿真结果表明所提出方法的故障检测性能优于传统的KPCA方法。     

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。     

基于RISOMAP的非线性过程故障检测方法

化工过程监控数据存在非线性特点,且过程常常运行于多个模态,针对该类问题,提出基于相对等距离映射(relative isometric mapping, RISOMAP)的过程故障检测方法,该方法采用相对测地距离构造高维空间的距离关系阵,运用多维尺度变换(MDS)计算其低维嵌入输出,从高维数据中提取子流形信息和残差信息分别构造监控统计量进行故障检测,同时运用核ridge回归在线计算测试数据的低维输出,核矩阵通过综合相似度进行更新。数值算例和TE过程的仿真结果表明,RISOMAP方法可以更为有效地实施故障检测,故障检测的灵敏度较高,同时也为基于流形学习的多模态过程故障检测的实施提供了一条思路。     

基于改进KFDA与DE优化SOM的故障诊断模型及其化工过程诊断

针对化工过程故障诊断数据存在高维度、故障特征不易区分、自组织映射（self-organizing map,SOM）网络易陷入局部最优等问题,提出了一种基于改进核Fisher判别分析（kernel Fisher discriminant analysis,KFDA）与差分进化算法（differential evolution,DE）优化SOM神经网络相结合的故障诊断方法。该方法首先利用欧氏距离对类间距进行加权处理,以避免因类间距离过大造成投影后的数据存在重叠的问题,使故障数据样本获得较好的投影效果,优化分类性能;然后,利用DE算法对SOM神经网络的权值向量进行动态调整,有效避免了由于“死神经元”的出现陷入局部最优的问题;最后,通过对田纳西-伊斯曼（tennessee-eastman,TE）过程和对二甲苯（paraxylene,PX）歧化工艺过程的故障数据进行诊断测试。结果表明,与传统SOM网络相比,提出的KFDA-DE-SOM算法具有较高的分类诊断精度,可有效应用于化工过程的故障诊断。     

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TGNPE算法的间歇过程故障诊断

间歇过程数据是由批次、变量和时间构成的三维数据,数据内包含了丰富的对过程监控有用的全局和局部结构信息,如何充分提取间歇过程的特征信息是故障诊断的关键。传统方法处理三维数据都是将其展开成二维数据,展开过程必然会导致数据内在结构破坏,并且通常只考虑了数据的全局信息或者只考虑了数据的局部信息,这就不能充分提取过程的有用信息导致诊断效果欠佳。针对以上问题,提出了张量全局-局部邻域保持嵌入(TGNPE)算法,首先用张量分解的方法直接对三维数据进行建模,而不对数据进行展开,这就有效地保存了数据的内部结构,再用邻域保持嵌入算法充分提取数据局部结构信息的同时兼顾数据的全局信息,这就实现了对数据特征信息更加充分地提取,用TGNPE算法检测到故障后用贡献图法诊断出故障变量。通过青霉素发酵过程验证了本文提出的算法对间歇过程数据信息提取更加充分,更利于故障诊断。     

Robust fault detection and monitoring of hybrid process systems with uncertain mode transitions

A methodology for fault detection and monitoring of a class of hybrid process systems modeled by switched nonlinear systems with control actuator faults, uncertain continuous dynamics, and uncertain mode transitions is presented. A robust hybrid monitoring scheme that distinguishes reliably between faults, mode transitions, and uncertainty is developed using tools from unknown input observer theory and results from Lyapunov stability theory. The monitoring scheme consists of (1) a family of dedicated mode observers that locate the active operating mode at any given time and detect mode switches, (2) a family of robust Lyapunov‐based fault detection schemes that detect the faults within the continuous modes, and (3) a supervisor that synchronizes the switching between different controllers and different fault detectors as the process transitions from one mode to another. A key idea of the developed framework is to design the mode observers in a way that facilitates the identification of the active mode without information from the controllers and renders the residuals insensitive to the faults and uncertainties in the constituent subsystems. The implementation of the developed monitoring scheme is demonstrated using a simulated model of a chemical reactor that switches between multiple operating modes. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Tensor sequence component analysis for fault detection in dynamic process

For dynamic processes, using sequence information to augment the data can improve fault detection performance. Traditional approaches transform raw data into augmented vectors, which leads to losses in structural information in the variables and increases the data dimension. This paper proposes a novel data dimension reduction algorithm called tensor sequence component analysis (TSCA) and applies it to dynamic process fault detection. The algorithm extends each sample into a matrix comprising current and past process data, and simultaneously reduces the dimensions of time delay and the variables for feature extraction, solving the problem of the curse of dimensionality. For the dimension reduction of time delay, in order to extract similar information from the samples, each sample is reconstructed with time neighbourhoods. For the dimension reduction of the variables, considering the information of different variables variance information of the latent variables is maximized for feature extraction. Finally, a numerical example and the Tennessee Eastman process are used to demonstrate the efficacy of the proposed method.