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     

注塑成型过程温度启动过渡状态的性能监测与故障诊断

注塑成型过程启动阶段具有复杂动态特性,更容易遭受未知扰动、误操作甚至故障的危害。针对注塑启动阶段的机筒温度变量,建立了二维动态多元统计模型,实现了注塑启动性能的实时监测和故障诊断;并以常见的加热线圈以及传感器故障为例,详细讨论了方法的应用效果以及在安全生产方面的应用前景。     

基于非线性动态模型的质子交换膜燃料电池故障诊断

为了对质子交换膜燃料电池(proton exchange membrane fuel cell,PEMFC)系统进行故障诊断以提高系统的安全性和可靠性,针对PEMFC系统的强非线性,在九阶状态空间模型的基础上提出一种滑模观测器实时生成残差,利用故障阈值检测法建立故障特征矩阵检测故障,进而为了隔离故障,引入相对故障敏感度函数建立理论相对故障敏感度矩阵,在系统运行时实时计算各故障相对故障敏感度与理论相对故障敏感度的欧氏距离,最小欧氏距离对应的故障则为系统发生的故障,结果验证了所提出的基于模型的故障诊断方法的有效性,且所构建观测器可以估计PEMFC系统中难以直接测取的状态变量,平均相对误差在6%以内。     

基于非线性动态模型的质子交换膜燃料电池故障诊断

为了对质子交换膜燃料电池(proton exchange membrane fuel cell,PEMFC)系统进行故障诊断以提高系统的安全性和可靠性,针对PEMFC系统的强非线性,在九阶状态空间模型的基础上提出一种滑模观测器实时生成残差,利用故障阈值检测法建立故障特征矩阵检测故障,进而为了隔离故障,引入相对故障敏感度函数建立理论相对故障敏感度矩阵,在系统运行时实时计算各故障相对故障敏感度与理论相对故障敏感度的欧氏距离,最小欧氏距离对应的故障则为系统发生的故障,结果验证了所提出的基于模型的故障诊断方法的有效性,且所构建观测器可以估计PEMFC系统中难以直接测取的状态变量,平均相对误差在6%以内。     

基于小波理论的主元分析在故障诊断中的研究与应用

主要分析主元分析的原理和主要算法及其在故障诊断方面的应用 ,简单介绍数据预处理的小波分析方法 ,并把这两种方法结合用于过程故障诊断。常减压装置的应用实例表明 ,结合这两种方法进行基于过程的故障诊断取得了较好的效果     

基于改进多模型FDA的间歇生产过程的故障诊断

1 INTRODUCTION In recent decades, batch processes have been a wide concern in the chemical fields because of their low-volume, high-value products and capabilities of easily tracking changing market situations. Therefore, it is necessary to monitor them in order to ensure safe, decrease the production costs and enhance the quality of products. Batch processes are characterized by the precise sequencing and automation of all stages in the sequence. They convert raw materials into products …     

Multi-block gated recurrent unit network-based fault diagnosis method for multivariate nonlinear process

The increasing complexity of industrial processes brings new challenges to fault diagnosis tasks, and different types of faults have higher and higher requirements on the performance of fault classification models. This paper proposes a novel multivariate nonlinear temporal-related fault diagnosis method based on gated recurrent units (GRUs). First, to improve the performance of the model in local information extraction and global integration, high dimensional variables are divided into multiple sub-blocks according to the structure of chemical process units, and a new block normalization method is proposed to improve the performance of local feature extraction. Second, aiming at the slow drifting faults, the GRU network is adopted inside the sub-block to extract local sparse and nonlinear temporal features. By combining the variance features of variables after block normalization, the performance of the model on multiplicative faults will improve. Finally, aiming at the complex correlation between variables, a new recurrent matrix method is proposed to extract the time transform information inside each variable to improve the comprehensive performance of the model. Through a multi-level feature integration strategy, the model can be trained in parallel to improve the training speed. The proposed method shows good performance in the Tennessee Eastman process, and the extracted multi-class features allow the model to be trained end-to-end and simultaneously diagnose multiple types of faults.     

基于BP神经网络的变压器故障诊断

利用BP神经网络对变压器进行故障诊断.以特征气体含量的比值作为输入量,利用MATLAB软件建立故障诊断模型,利用改进的动量梯度下降法,达到了故障诊断的要求.并通过变压器故障诊断实例分析,证明了该方法的有效性.     

基于Internet的FW远程故障诊断系统

本文简述了利用Internet建立FW远程故障诊断系统模型,利用该远程故障诊断系统,可以实现FW设备的在线检测和远程故障诊断,及时解决设备故障,及时地恢复正常生产.     

Integrated nonlinear model predictive fault tolerant control and multiple model based fault detection and diagnosis

In this paper, a new fault-tolerant control approach is presented for a class of nonlinear systems, which preserves system stability despite a time delay in fault detection. The faults are assumed to occur in the actuators and are modeled for the general form of affine nonlinear systems. A fault detection and diagnosis (FDD) block is designed based on the multiple model method. The bank of extended Kalman filters (EKF) is used to detect predefined actuator faults and to estimate the unknown parameters of actuator position. The estimated parameters are then used to correct the model of the faulty system and to reconfigure the controller. The reconfigurable controller is designed based on the stabilizing nonlinear model predictive control (NMPC) scheme. On the other hand, in the duration between fault occurrence and fault detection, because of the mismatch between the process and the model, the system states may go off the attraction region. The proposed method is based on designing multiple local controllers for individual predefined faults. Depending on the value of a system variable at the moment of fault detection, one of these controllers will operate. This leads to a stability region of a set of auxiliary equilibrium points (AEPs), which is larger than the attraction region. Moreover, a framework for preserving system stability is presented. Finally, a practical chemical process example is presented to illustrate the effectiveness of this method.     

基于MSPCA-KECA的冷水机组故障监测及诊断

针对冷水机组同类型不同等级故障的变量变化存在差异会造成误诊断的问题，提出一种基于多尺度主元分析-核熵成分分析（MSPCA-KECA）的故障诊断策略。MSPCA提取故障特征，其输出作为KECA分类器的输入，实现故障的实时监测与自动诊断。首先，改进的MSPCA算法通过将小波多尺度分析与主元分析相结合，筛选故障信息可能存在的尺度直接重构并采用PCA提取故障特征，获取不同类型故障之间差异的同时也保留了同类型但不同等级故障之间的相似性，提高故障诊断的可靠性。之后建立KECA非线性分类器并引入一种新的监测统计量--散度测度统计量，使降维后不同特征信息之间呈现显著的角度差异，易于分类。最后，采用支持向量数据描述（SVDD）算法确定新统计量的控制限，以克服无法获知统计量分布的问题。通过对冷水机组数据的仿真研究，验证了MSPCA-KECA方法的可行性及有效性。     

Siamese DeNPE network framework for fault detection of batch process

In batch processes, it is crucial to ensure safe production by fault detection. However, the long batch duration, limited runs, and strong nonlinearity of the data pose challenges. Incipient faults with small amplitudes further complicate the detection process. To achieve safe production, motivated by deep learning strategies, we propose a new fault detection method of batch process called Siamese deep neighbourhood preserving embedding network (SDeNPE). First, the DeNPE network is constructed by means of NPE and kernel functions, which utilizes the different types of kernel functions in the kernel mapping layer to extract diverse deep nonlinear features and overcome strong nonlinearity in the process data. Then, the Siamese network is used to obtain the different features between the data and improve the recognition of incipient faults. In addition, the deep extraction and Siamese network allow for batches of training data reduction without diminishing the performance of fault detection. Finally, we utilize monitoring statistics to complete the fault detection process. Two batch process cases involving the penicillin fermentation process and the semiconductor etching process demonstrate the superior fault detection performance of the proposed SDeNPE over the other comparison methods.     

Reconstruction in integrating fault spaces for fault identification with kernel independent component analysis

In the original fault identification methods, contribution plots are popular. However, it is not accurate because of the smearing effect. In addition, traditional contribution plots cannot be applied to nonlinear process because there seems no way to accurately calculate variable contributions. As a comparison, the reconstruction method is widely used in fault identification for finding the root causes of the fault. For fault detection and identification of actual industrial process with nonlinear and non-Gaussian features, a new reconstruction-based fault identification method with kernel independent component analysis (KICA) is developed in this article. The proposed method, reconstruction in integrating fault spaces (RIFSs), extends the classic reconstruction-based fault identification approach to KICA for the first time, and develops the reconstruction method from unidimensional faults to multidimensional ones for nonlinear cases. Furthermore, the number of reconstruction is effectively reduced on the basis of the integrating fault spaces (IFSs) which are composed of fault subspaces satisfying orthogonal to each other from the known fault set. In addition, fault magnitude, indicating the adjustment magnitude of a fault sample back to normal range, is used as index to identify faults, and it makes the fault identification problem become more straightforward than with the existing fault identification index, such as ratio (index I) or the reconstructed statistics (index II). Finally, the proposed method is applied to the fault detection and identification on cyanide leaching of gold, which shows its feasibility and efficiency for both sensor faults and complex process faults.     

Fault Detection and Diagnosis in a Food Pasteurization Process with Hidden Markov Models

Hidden Markov Models (HMM) are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The method is illustrated by monitoring the operation of a pasteurization plant and diagnosing causes of abnormal operation. Process data collected under the influence of faults of different magnitude and duration in sensors and actuators are used to illustrate the use of HMM in the detection and diagnosis of process faults. Case studies with experimental data from a high‐temperature‐short‐time pasteurization system showed that HMM can diagnose the faults with certain characteristics such as fault duration and magnitude.     

基于可拓理论的故障模式识别研究

针对变压器潜伏性故障,提出一种以物元模型和关联函数为基础的可拓故障诊断法。物元模型的建立是基于变压器的三比值法和改进特征气体法,分析故障机理,选取较优的有代表性的故障特征气体或比值,作为物元模型的特征量。然后,通过可拓关联函数计算待诊变压器故障参数与标准故障模式的关联度,从而进行故障判断。用具体实例进行了诊断验证,结果表明该方法具有故障分类识别的有效性和可行性。     

基于加权互信息主元分析算法的质量相关故障检测

质量相关的故障检测已成为近几年研究热点,它的目标是在过程监测中,对质量相关的故障检测率更高,对质量无关的故障少报警或不报警。传统主元分析算法的故障检测会对所有故障均报警,不能达到上述要求。另外,在实际工业生产中,质量变量通常难以实时获得,需要后续分析或延时得到。为此,提出一种融合贝叶斯推断与互信息的加权互信息主元分析算法。首先利用贝叶斯推断的加权方法将度量过程变量和质量变量之间相关关系的互信息进行融合,选出包含质量变量信息量最大的一组过程变量。然后对过程变量利用主元分析（principal component analysis,PCA）进行统计建模,再次根据加权互信息选出包含质量变量信息量最大的主元,建立统计量进行故障检测。最后,通过实验验证该方法的可行性和有效性。     

线性分类-重构网及其在化工过程早期故障诊断中的应用

提出了一种以线性递推学习为基础的分类-重构神经网络。网络具有学习算法简单、速度快、学习与分类并行,以及可自动积累知识等基本功能,尤其适用于生产过程的早期故障诊断一类实时系统。给出了化工过程早期故障诊断的应用实例,研究结果证明了网络的有效性。     

基于滚动MPCA的青霉素发酵过程的在线监控

To reduce the variations of the production process in penicillin cultivations, a rolling multivariate statistical approach based on multiway principle component analysis （MPCA） is developed and used for fault diagnosis of penicillin cultivations. Using the moving data windows technique, the static MPCA is extended for use in dynamic process performance monitoring. The control chart is set up using the historical data collected from the past successful batches, thereby resulting in simplification of monitoring charts, easy tracking of the progress in each batch run, and monitoring the occurrence of the observable upsets. Data from the commercial-scale penicillin fermentation process are used to develop the rolling model. Using this method, faults are detected in real time and the corresponding measurements of these faults are directly made through inspection of a few simple plots （t-chart, SPE-chart, and T2-chart）. Thus, the present methodology allows the process operator to actively monitor the data from several cultivations simultaneously.     

一种针对间歇过程过渡状态的故障诊断方法

针对间歇过程过渡状态下具有的复杂过程特性,提出一种基于二维动态主成分分析（2DDPCA）的故障诊断方法。该方法将故障信息划分为“批次内”和“批次间”信息,采用变量贡献图方法隔离故障变量,并依据2DDPCA模型支撑区域中故障变量的相关性变化具体分析故障成因。仿真结果验证了该方法的可行性和有效性。     

Active fault isolation of nonlinear process systems

This work considers the problem of designing an active fault‐isolation scheme for nonlinear process systems subject to uncertainty. The faults under consideration include bounded actuator faults and process disturbances. The key idea of the proposed method is to exploit the nonlinear way that faults affect the process evolution through supervisory feedback control. To this end, a dedicated fault‐isolation residual and its time‐varying threshold are generated for each fault by treating other faults as disturbances. A fault is isolated when the corresponding residual breaches its threshold. These residuals, however, may not be sensitive to faults in the operating region under nominal operation. To make these residuals sensitive to faults, a switching rule is designed to drive the process states, upon detection of a fault, to move toward an operating point that, for any given fault, results in the reduction of the effect of other faults on the evolution of the same process state. This idea is then generalized to sequentially operate the process at multiple operating points that facilitate isolation of different faults for the case where the residuals are not simultaneously sensitive to faults at a single operating point. The effectiveness of the proposed active fault‐isolation scheme is illustrated using a chemical reactor example and demonstrated through application to a solution copolymerization of methyl methacrylate and vinyl acetate. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2435–2453, 2013