Raghunathan Rengaswamy

A review of process fault detection and diagnosis: Part I: Quantitative model-based methods

Fault detection and diagnosis is an important problem in process engineering. It is the central component of abnormal event management (AEM) which has attracted a lot of attention recently. AEM deals with the timely detection, diagnosis and correction of abnormal conditions of faults in a process. Early detection and diagnosis of process faults while the plant is still operating in a controllable region can help avoid abnormal event progression and reduce productivity loss. Since the petrochemical industries lose an estimated 20 billion dollars every year, they have rated AEM as their number one problem that needs to be solved. Hence, there is considerable interest in this field now from industrial practitioners as well as academic researchers, as opposed to a decade or so ago. There is an abundance of literature on process fault diagnosis ranging from analytical methods to artificial intelligence and statistical approaches. From a modelling perspective, there are methods that require accurate process models, semi-quantitative models, or qualitative models. At the other end of the spectrum, there are methods that do not assume any form of model information and rely only on historic process data. In addition, given the process knowledge, there are different search techniques that can be applied to perform diagnosis. Such a collection of bewildering array of methodologies and alternatives often poses a difficult challenge to any aspirant who is not a specialist in these techniques. Some of these ideas seem so far apart from one another that a non-expert researcher or practitioner is often left wondering about the suitability of a method for his or her diagnostic situation. While there have been some excellent reviews in this field in the past, they often focused on a particular branch, such as analytical models, of this broad discipline. The basic aim of this three part series of papers is to provide a systematic and comparative study of various diagnostic methods from different perspectives. We broadly classify fault diagnosis methods into three general categories and review them in three parts. They are quantitative model-based methods, qualitative model-based methods, and process history based methods. In the first part of the series, the problem of fault diagnosis is introduced and approaches based on quantitative models are reviewed. In the remaining two parts, methods based on qualitative models and process history data are reviewed. Furthermore, these disparate methods will be compared and evaluated based on a common set of criteria introduced in the first part of the series. We conclude the series with a discussion on the relationship of fault diagnosis to other process operations and on emerging trends such as hybrid blackboard-based frameworks for fault diagnosis.

A review of process fault detection and diagnosis: Part III: Process history based methods

In this final part, we discuss fault diagnosis methods that are based on historic process knowledge. We also compare and evaluate the various methodologies reviewed in this series in terms of the set of desirable characteristics we proposed in Part I. This comparative study reveals the relative strengths and weaknesses of the different approaches. One realizes that no single method has all the desirable features one would like a diagnostic system to possess. It is our view that some of these methods can complement one another resulting in better diagnostic systems. Integrating these complementary features is one way to develop hybrid systems that could overcome the limitations of individual solution strategies. The important role of fault diagnosis in the broader context of process operations is also outlined. We also discuss the technical challenges in research and development that need to be addressed for the successful design and implementation of practical intelligent supervisory control systems for the process industries.

A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies

In this part of the paper, we review qualitative model representations and search strategies used in fault diagnostic systems. Qualitative models are usually developed based on some fundamental understanding of the physics and chemistry of the process. Various forms of qualitative models such as causal models and abstraction hierarchies are discussed. The relative advantages and disadvantages of these representations are highlighted. In terms of search strategies, we broadly classify them as topographic and symptomatic search techniques. Topographic searches perform malfunction analysis using a template of normal operation, whereas, symptomatic searches look for symptoms to direct the search to the fault location. Various forms of topographic and symptomatic search strategies are discussed.

Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets

Abstract Recently, Maurya et al. (Ind. Eng. Chem. Res. 42 (2003b, c) 4789,4811) have presented a comprehensive framework for signed directed graph-based analysis of process systems where major theoretical results have been substantiated with simple examples or individual unit-based case studies. In this article, two case studies are presented to illustrate SDG-based analysis of process flowsheets containing many units and control loops. While the literature is replete with single unit examples, flowsheet level analysis as described in this paper is virtually non-existent. The first case study deals with prediction of initial response and its fault diagnostic application in the Tennessee Eastman (TE) flowsheet using a lumped parameter model of the process. The second case study deals with the steady-state analysis and fault diagnosis (FD) of a reaction–separation process. For this case study, the overall signed digraph for the process is developed from the digraphs for individual units and control loops in the flowsheet. It is shown that digraph-based steady-state analysis results in good diagnostic resolution.

Fault diagnosis using dynamic trend analysis: A review and recent developments

Dynamic trend analysis is an important technique for fault detection and diagnosis. Trend analysis involves hierarchical representation of signal trends, extraction of the trends, and their comparison (estimation of similarity) to infer the state of the process. In this paper, an overview of some of the existing methods for trend extraction and similarity estimation is presented. A novel interval-halving method for trend extraction and a fuzzy-matching-based method for similarity estimation and inferencing are also presented. The effectiveness of the interval halving and trend matching is shown through simulation studies on the fault diagnosis of the Tennessee Eastman process. Industrial experiences on the application of trend analysis technique for fault detection and diagnosis is also presented followed by a discussion on outstanding issues and solution approaches.

Fuzzy-logic based trend classification for fault diagnosis of chemical processes

Abstract In this paper, fault diagnosis based on patterns exhibited in the sensors measuring the process variables is considered. The temporal patterns that a process event leaves on the measured sensors, called event signatures, can be utilized to infer the state of operation using a pattern-matching approach. However, the qualitative nature of the features leads to imprecise classification boundaries at the trend-identification stage and hence at the trend-matching stage. Moreover, noise and other underlying phenomena may lead to non-reproducibility of the same trends chosen to represent an event. Thus, a crisp inference process might lead to a large knowledge-base of signatures; it could also cause misclassification. To overcome this, a fuzzy-reasoning approach is proposed to ensure robustness to the inherent uncertainty in the identified trends and to provide succinct mapping. A two-staged strategy is employed: (i) identifying the most likely fault candidates based on a similarity measure between the observed trends and the event-signatures in the knowledge-base and, (ii) estimation of the fault magnitude. The fuzzy-knowledge-base consists of a set of physically interpretable if–then rules providing physical insight into the process. The technique provides multivariate inferencing and is transparent. We illustrate the application of the proposed approach in the fault diagnosis of an exothermic reactor case study.

New nonlinear residual feedback observer for fault diagnosis in nonlinear systems

The increased complexity of plants and the development of sophisticated control systems have necessitated the parallel development of efficient Fault Detection and Isolation (FDI) systems. This paper discusses a model based technique, viz., observers for detecting and isolating parametric and sensor faults. In this paper, a novel diagonal nonlinear residual feedback observer is proposed which is valid for a certain class of nonlinear systems where, subject to other conditions, the state depends nonlinearly on the fault. A number of typical chemical engineering systems can be represented by models of this form. The structure of the observer ensures that the residuals are diagonally affected by the faults. Conditions for exact decoupling of residuals are presented and convergence of the observer in the presence of step faults is proved using Lyapunov like analysis. Multiple observers and a decision logic module are used for FDI when there are un-monitored faults. Results are presented from numerical simulations of an illustrative example and a typical chemical engineering system: a counter-current heat exchanger.

Approaches for efficient stiction compensation in process control valves

Limit cycles caused due to valve nonlinearity such as stiction can be eliminated with proper valve maintenance. Valve maintenance is undertaken during production stops, which are scheduled once every 6 months to 3 years. The loss of energy and product quality during this intermediate period can be quite high. Stiction compensation algorithms can mitigate this problem to a large extent. In this paper, two novel approaches for stiction compensation are proposed: (a) a simple two-move approach and (b) an optimization based approach much in the spirit of predictive control strategies. Both the approaches are based on a data-driven model for stiction. The merits and demerits of both these approaches are discussed. The results are illustrated using simulation case studies. The two-move approach is also validated on a liquid level system.

A Signed Directed Graph and Qualitative Trend Analysis-Based Framework for Incipient Fault Diagnosis

In this article a combined signed directed graph (SDG) and qualitative trend analysis (QTA) framework for incipient fault diagnosis has been proposed. The SDG is the first level in this framework and provides a possible candidate set of faults based on the incipient response of the process. The search for the actual fault is performed based on a QTA (level 2), which uses the temporal evolution of the sensors for further resolution. Thus, this framework combines the completeness property of SDG with the high diagnostic resolution property of QTA. Methods to address the problem of incorrect diagnosis arising due to incorrect measurement of initial response have also been presented. The proposed approach is tested on the Tennessee Eastman (TE) case study. Correct fault diagnosis is performed in all possible single fault scenarios. It is shown that this framework provides fast, reliable and accurate incipient fault diagnosis.

A framework for on-line trend extraction and fault diagnosis

Qualitative trend analysis (QTA) is a process-history-based data-driven technique that works by extracting important features (trends) from the measured signals and evaluating the trends. QTA has been widely used for process fault detection and diagnosis. Recently, Dash et al. [2004. A novel interval-halving framework for automated identification of process trends. AIChE Journal 50 (1), 149-162] presented an interval-halving-based algorithm for off-line automatic trend extraction from a record of data, a fuzzy-logic based methodology for trend-matching and a fuzzy-rule-based framework for fault diagnosis (FD). In this article, an algorithm for on-line extraction of qualitative trends is proposed. A framework for on-line fault diagnosis using QTA also has been presented. Some of the issues addressed are: (i) development of a robust and computationally efficient QTA-knowledge-base, (ii) fault detection, (iii) estimation of the fault occurrence time, (iv) on-line trend-matching, and (v) updating the QTA-knowledge-base when a novel fault is diagnosed manually. A prototype QTA-based diagnostic system has been developed in Matlab^(R). Results for fault diagnosis of the Tennessee Eastman process using the developed framework are presented.