Distributed control of plantwide chemical processes

Complex process plants increasingly appear in modern chemical industry. The wide use of material recycles and heat integration (with recycle and bypass streams) profoundly alters plantwide process dynamics and further increases their complexity. The interactions between process units may lead to poor performance of decentralized control systems. On the other hand, the complexity of plantwide systems prohibits the use of centralized controllers that reply on the complex model of the entire plantwide process. This paper addresses the plantwide chemical process control problem from a network perspective. The entire chemical plant is modeled as a network of process units linked by physical mass and energy flow and controlled by controllers that communicate with each other (i.e., distributed controllers). A two-port linear time-invariant representation is proposed to describe the dynamics of each process unit and its corresponding distributed controller. A two-step plantwide linear control design approach is developed. By using the dissipativity theory, the plantwide stability and control performance is translated into the closed-loop dissipativity condition that each distributed controller has to achieve. This allows the distributed controllers to be designed independently and to operate autonomously. The proposed approach is illustrated by a case study of a process network that consists of a reactor and a distillation column.     

Distributed plantwide control based on differential dissipativity

Modern chemical plants are becoming very complex, often consisting of a number of nonlinear process units (subsystems) with strong interactions due to material recycle and energy integration. The operation setpoint may need to be adjusted from time to time based on the market demand. To address the aforementioned challenges, a plantwide distributed nonlinear control scheme based on differential dissipativity is proposed in this paper, which can ensure plantwide incremental exponential stability and achieve bounded incremental L₂ gain performance. As a non‐unique property, the differential dissipativity of individual subsystem is shaped by a setpoint‐independent control structure – differential state feedback control. The dissipativity properties of subsystems and individual controllers are determined simultaneously as a large‐scale feasibility problem to ensure the plantwide stability and performance. It is converted into an LMI condition for plantwide supply rate planning and small‐scale sum‐of‐squares programming problems for individual subsystem dissipativity shaping, by using the alternating direction method of multipliers method. The proposed approach is illustrated using a chemical reactor network with a recycle stream. Copyright © 2016 John Wiley & Sons, Ltd.     

局部Fisher判别分析算法及仿真研究*

复杂化工过程常被多种类型的故障损坏,正常的训练数据无法建立准确的操作模型。为了提高复杂化工过程中故障的检测和分类能力,传统无监督Fisher判别分析(Fisher Discriminant Analysis,FDA)算法无法在多模态故障数据中的应用,本文提出基于局部Fisher判别分析(Local Fisher Discriminant Analysis,LFDA)的故障诊断方法。首先计算训练数据的局部类内和类间离散度矩阵,寻找LFDA的投影方向;其次把训练数据和测试数据向投影向量上投影,提取特征向量;最后计算特征向量间的欧氏距离,运用KNN分类器进行分类。把提出的LFDA方法应用到Tennessee Eastman(TE)过程,监控结果表明,LFDA的效果好于FDA和核Fisher判别分析(Kernel Fisher Discriminant Analysis,KFDA),说明LFDA方法在分类及检测不同类的故障方面具有高准确性及高灵敏度的优势。     

On‐Line Fault Diagnosis Based on B‐Spline Neural Networks Using Asymptotic Local Approach

Fault diagnosis is crucial in monitoring industrial processes. Faults con be often detected from residuals generated from the system model. For systems with known models, residuals can be readily generated. However, for systems with unknown models, neural networks can be used to model the system. For small or incipient faults, it is difficult to detect faults directly from the residuals. The asymptotic local approach, which transforms the fault diagnosis problem into one that detects statistical changes in a random variable, is proposed here. The proposed scheme is illustrated by a simulation example, and comparison with faults obtained directly from the residuals is also made.     

基于多块核主元分析的复杂过程的分散故障诊断

提出多块核主元分析算法, 基于此算法针对复杂过程提出了新的故障检测和诊断方法. 通过对整体过程分块统计残差实现非线性过程的分散故障诊断目的, 相应的控制限用来分离引起故障的位置或发现引起故障的变量. 提出的方法应用到田纳西过程得出的结论为: 该方法能够有效地提取块内和块间的非线性信息并显示出优越的故障诊断能力.     

An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks

This paper presents an approach to implement vibration, pressure, and current signals for fault diagnosis of the valves in reciprocating compressors. Due to the complexity of structure and motion of such compressor, the acquired vibration signal normally involves transient impacts and noise. This causes the useful information to be corrupted and difficulty in accurately diagnosing the faults with traditional methods. To reveal the fault patterns contained in this signal, the Teager–Kaiser energy operation (TKEO) is proposed to estimate the amplitude envelopes. In case of pressure and current, the random noise is removed by using a denoising method based on wavelet transform. Subsequently, statistical measures are extracted from all signals to represent the characteristics of the valve conditions. In order to classify the faults of compressor valves, a new type of learning architecture for deep generative model called deep belief networks (DBNs) is applied. DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines (RBMs) and works through a greedy layer-by-layer learning algorithm. In pattern recognition research areas, DBN has proved to be very effective and provided with high performance for binary values. However, for implementing DBN to fault diagnosis where most of signals are real-valued, RBM with Bernoulli hidden units and Gaussian visible units is considered in this study. The proposed approach is validated with the signals from a two-stage reciprocating air compressor under different valve conditions. To confirm the superiority of DBN in fault classification, its performance is compared with that of relevant vector machine and back propagation neuron networks. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery.     

Fault detection in dynamic systems using the Kullback–Leibler divergence

This paper proposes detecting incipient fault conditions in complex dynamic systems using the Kullback–Leibler or KL divergence. Subspace identification is used to identify dynamic models and the KL divergence examines changes in probability density functions between a reference set and online data. Gaussian distributed process variables produce a simple form of the KL divergence. Non-Gaussian distributed process variables require the use of a density-ratio estimation to compute the KL divergence. Applications to recorded data from a gearbox and two distillation processes confirm the increased sensitivity of the proposed approach to detect incipient faults compared to the dynamic monitoring approach based on principal component analysis and the statistical local approach.     

基于并发改进偏最小二乘的质量相关和过程相关的故障诊断

并发潜结构投影(CPLS)与传统贡献图法是多元统计过程监控中常用的故障检测与诊断方法.过程监控通常要求监测的时效性与诊断的准确性,然而,由于CPLS计算复杂以及传统贡献图诊断结果易受初始贡献较大的变量影响,因此它们反馈的监控结果可能并不准确.针对上述问题分别提出一种并发改进偏最小二乘(CMPLS)方法和新的相对贡献图法...     

Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants’ productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning (HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building’s energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC systems. Modeling the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.      

Hybrid causal analysis combining a nonparametric multiplicative regression causality estimator with process connectivity information

Industrial processes are often subjected to abnormal events such as faults or external disturbances which can easily propagate via the process units. Establishing causal dependencies among process measurements has a key role in fault diagnosis due to its ability to identify the root cause of a fault and its propagation path. This paper proposes a hybrid nonlinear causal analysis based on nonparametric multiplicative regression (NPMR) for identifying the propagation of an oscillatory disturbance via control loops. The NPMR causality estimator addresses most of the limitations of the linear model-based methods and it can be applied to both bivariate and multivariate estimations without any modifications to the method parameters. Moreover, the NPMR-based estimations can be used to pinpoint the root cause of a fault. The process connectivity information is automatically integrated into the causal analysis using a specialized search algorithm. Thereby, it enables to efficiently tackle industrial systems with a high level of connectivity and enhance the quality of the results. The proposed approach is successfully demonstrated on an industrial board machine exhibiting oscillations in its drying section due to valve stiction and. The NPMR-based estimator produced highly accurate results with relatively low computational effort compared with the linear Granger causality and other nonlinear causality estimators.     

Improved canonical correlation analysis-based fault detection methods for industrial processes

Recent research has emphasized the successful application of canonical correlation analysis (CCA) to perform fault detection (FD) in both static and dynamic processes with additive faults. However, dealing with multiplicative faults has not been as successful. Thus, this paper considers the application of CCA to deal with the detection of incipient multiplicative faults in industrial processes. The new approaches incorporate the CCA-based FD with the statistical local approach. It is shown that the methods are effective in detecting incipient multiplicative faults. Experiments using a continuous stirred tank heater and simulations on the Tennessee Eastman process are provided to validate the proposed methods.     

Detection,isolation and handling of actuator faults in distributed model predictive control systems

In this work, we focus on monitoring and reconfiguration of distributed model predictive control systems applied to general nonlinear processes in the presence of control actuator faults. Specifically, we consider nonlinear process systems controlled with a distributed control scheme in which two Lyapunov-based model predictive controllers manipulate two different sets of control inputs and coordinate their actions to achieve the desired closed-loop stability and performance specifications. To deal with control actuator faults which may reduce the ability of the distributed control system to stabilize the process, a model-based fault detection and isolation and fault-tolerant control system which detects and isolates actuator faults and determines how to reconfigure the distributed control system to handle the actuator faults while maintaining closed-loop stability is designed. A detailed mathematical analysis is carried out to determine precise conditions for the stabilizability of the fault detection and isolation and fault-tolerant control system. A chemical process example, consisting of two continuous stirred tank reactors and a flash tank separator with a recycle stream and involving stabilization of an unstable steady-state, is used to demonstrate the approach.     

基于T-PLS贡献图方法的故障诊断技术

多变量统计过程监控对于复杂工业过程是一种有效的故障检测和诊断技术. 最小二乘(或称潜空间投影)模型是多变量统计过程监控中常用的一种投影模型, 能够同时对过程数据和质量数据进行建模. 讨论了一种新的基于全潜空间投影模型的故障诊断技术. 全潜空间投影模型中有4个检测统计量. 提出了一种新的T2贡献图计算方法, 对于所有检测统计量, 得到了相应的贡献图算法. 为了确定一个变量是否发生了故障, 计算所有变量贡献图的控制限. 该技术可以将辨识到的故障变量分为与Y有关和与Y无关的两类. 基于Tennessee Eastman过程的案例研究表明了该技术的有效性.     

基于规范变量残差的化工过程微小故障检测与诊断

微小故障因其幅值低而易被噪声和过程扰动所掩盖,并且会随时间慢慢演变成过程中的严重故障.因此,微小故障的检测和诊断变得越来越重要.为了更有效地监测和诊断微小故障,提出了基于规范变量残差的化工过程微小故障检测和诊断方法.首先,对Hankel矩阵执行奇异值分解来获得主元和残差空间并根据过去和未来数据的差异,求得两个不同的规范变量残差d_1, d_2.其次,考虑数据的时间序列特性,提出了基于规范变量残差的两个加权平均统计量W_(D1), W_(D2)及其控制限,进行故障检测;然后,计算出各个统计量的归一化贡献并绘制二维贡献图,进行故障诊断.最后,在连续搅拌釜式反应器(CSTR)过程中进行两种微小故障的应用研究.结果表明,与传统的统计量T~2,Q以及规范变量差异分析(CVDA)中统计量D相比,基于规范变量残差的加权平均统计量W_(D1), W_(D2)不仅能够及时检测到微小故障,而且在故障检测率和诊断率方面,均有不同程度的提高.     

基于局部 ? 整体相关特征的多单元化工过程分层监测

针对一类多单元化工过程的监测问题, 提出基于局部?整体相关特征的分层故障检测与故障定位方法, 通过表征单元内部变量相关性、单元与单元间相关性、局部单元与过程整体相关性, 对过程运行状态进行判断, 以提升过程监测的准确性与可靠性. 首先, 采用典型相关分析, 通过引入邻域单元相关变量提取每个单元的独有特征和外部相关特征; 其次, 对每个单元的独有特征和所有单元的外部相关特征建立统计模型实现分层故障检测; 然后, 建立单元?变量分层贡献图, 对故障单元以及故障变量实现分层定位. 通过在Tennessee Eastman仿真过程和一个实验室级甘油精馏过程中的应用说明所提分层监测方法的有效性.     

A statistically based fault detection and diagnosis approach for non-residential building water distribution systems

Large non-residential buildings can contain complex and often inefficient water distribution systems. As requirements for water increase due to water scarcity and industrialization, it has become increasingly important to effectively detect and diagnose faults in water distribution systems in large buildings. In many cases, if water supply is not impacted, faults in water distribution systems can go unnoticed. This can lead to unnecessary increases in water usage and associated energy due to pumping, treating, and heating water. The majority of fault detection and diagnosis studies in the water sector are limited to municipal water supply and leakage detection. The application of detection and diagnosis for faults in building water networks remains largely unexplored and the ability to identify and distinguish between routine and non-routine water usage at this scale remains a challenge. This study using case-study data, presents the application of principal component analysis and a multi-class support vector machine to detect and classify faults for non-residential building water networks. In the absence of a process model (which is typical for such water distribution systems), principal component analysis is proposed as a data-driven fault detection technique for building water distribution systems for the first time herein. Hotelling T²-statistics and Q-statistics were employed to detect abnormality within incoming data, and a multi-class support vector machine was trained for fault classification. Despite the relatively limited training data available from the case-study (which would reflect the situation in many buildings), meaningful faults were detected, and the technique proved successful in discriminating between various types of faults in the water distribution system. The effectiveness of the proposed approach is compared to a univariate threshold technique by comparison of their respective performance in the detection of faults that occurred in the case-study site. The results demonstrate the promising capabilities of the proposed fault detection and diagnosis approach. Such a strategy could provide a robust methodology that can be applied to buildings to reduce inefficient water use, reducing their life-cycle carbon footprint.     

基于精简并发潜结构映射的竖炉焙烧过程综合故障诊断

竖炉焙烧过程因运行条件异常变化或操作不当会造成上火、冒火、过还原和欠还原等运行故障.这些故障直接影响过程运行安全和产品质量（比如,磁选管回收率）,但难以采用基于模型和基于知识的方法建模故障与产品质量的关系,以及诊断故障变量.针对上述问题,本文提出数据驱动的基于并发潜结构映射（Concurrent projection to latent structures,CPLS）的竖炉焙烧过程综合故障诊断方法.首先,将并发潜结构映射分解的过程变量共有子空间与残差空间精简合并来建立磁选管回收率相关的过程变化空间,提出基于精简并发潜结构映射模型的竖炉焙烧过程综合监控方法;接下来,定义相应的重构贡献图并与竖炉焙烧过程相结合,提出CPLS精简重构贡献方法用于竖炉焙烧过程故障变量诊断;最后,利用竖炉焙烧过程半实物仿真平台采集的数据进行实验研究,结果表明所提方法不仅可以诊断出质量相关的故障,而且可诊断出回路设定值之外的故障变量.     

Model-free direct fault detection and classification

The present paper deals with the problem of fault detection and diagnosis in large scale engineering processes. These processes are typically equipped with database management systems and data logging servers whereby the measurement data is cleaned and stored. The expert knowledge of engineers and technicians as well as historical data records about abnormal scenarios experienced in the past is often available at hand. In this work we propose a framework where fault detection and classification can be done online directly on new data record without dimensionality reduction or any distributional assumptions. The proposed algorithm is based on a two-sample test via kernel mean embeddings of probability distributions. The Tennessee Eastman benchmark process is used to assess this new data-driven approach on different simulated faults.     

Variable-weighted Fisher discriminant analysis for process fault diagnosis

Variable-weighted Fisher discriminant analysis (VW-FDA) is proposed to improve the fault diagnosis performance of the conventional FDA. VW-FDA incorporates the variable weighting into FDA. The variable weighting is used to find out each weight vector for all faults. After all fault data are weighted by the corresponding weight vectors, the summed fault data can be constructed to magnify each fault’s local characteristics. Then, VW-FDA is performed on the summed fault data rather than the original fault data. It is helpful to extract discriminative features from overlapping fault data. Moreover, the partial F-values with the cumulative percent variation are used for exactly variable weighting, which is indispensable to VW-FDA. The proposed approach is applied to Tennessee Eastman process. The results demonstrate that VW-FDA shows better fault diagnosis performance than the conventional FDA.     

Diagnosis of multiple and unknown faults using the causal map and multivariate statistics

Feature extraction is crucial for fault diagnosis and the use of complementary features allows for improved diagnostic performance. Most of the existing fault diagnosis methods only utilize data-driven and causal connectivity-based features of faults, whereas the important complementary feature of the propagation paths of faults is not incorporated. The propagation path-based feature is important to represent the intrinsic properties of faults and plays a significant role in fault diagnosis, particularly for the diagnosis of multiple and unknown faults. In this article, a three-step framework based on the modified distance (DI) and modified causal dependency (CD) is proposed to integrate the data-driven and causal connectivity-based features with the propagation path-based feature for diagnosing known, unknown, and multiple faults. The effectiveness of the proposed approach is demonstrated on the Tennessee Eastman process.