Raghunathan Rengaswamy

Bio: Raghunathan Rengaswamy is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topic(s): Proton exchange membrane fuel cell & Fault detection and isolation. The author has an hindex of 39, co-authored 210 publication(s) receiving 9632 citation(s). Previous affiliations of Raghunathan Rengaswamy include Indian Institute of Technology Bombay & Bosch.

A Review of Process Fault Detection and Diagnosis Part I : Quantitative Model-Based Methods

Abstract: 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

Abstract: 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

Abstract: 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.

A syntactic pattern-recognition approach for process monitoring and fault diagnosis

Abstract: Process operators often deal with vast amounts of sensor data that are typically updated every few minutes. From such real-time data, operators extract interesting and important qualitative trends and features that describes the essential aspects of the process behavior. This level of understanding is essential for performing causal reasoning about process behavior. To aid this decision-making process of operators, a syntactic pattern-recognition approach for process monitoring has been developed. The syntactic pattern-recognition approach has two main parts: (i) a set of primitives that form the trend description language to represent basic changes in trends and (ii) a grammar to perform error correction and explanation generation. The syntactic approach to process monitoring provides a capability to describe complex patterns using a small set of simple primitive patterns. In this work, a backpropagation-based neural network was trained to identify the presence of the appropriate primitives in a trend of noisy process data. A process grammar which can utilize both contextual and non-contextual information to perform error correction and explanation generation has also been developed. These are discussed with the aid of a FCCU case study.

Locating sensors in complex chemical plants based on fault diagnostic observability criteria

Abstract: Fault diagnosis is an important task for the safe and optimal operation of chemical processes. Hence, this area has attracted considerable attention from researchers in the past few years. A variety of approaches have been proposed for solving this problem. All approaches for fault detection and diagnosis in some sense involve the comparison of the observed hehavior of the process to a reference model. The process behavior is inferred using sensors measuring the important variables in the process. Hence, the efficiency of the diagnostic approach depends critically on the location of sensors monitoring the process variables. The emphasis of most of the work on fault diagnosis has been more on procedures to perform diagnosis given a set of sensors and less on the actual location of sensors for efficient identification of faults. A digraph-based approach is proposed for the problem of sensor location for identification of faults. Various graph algorithms that use the developed digraph in deciding the location of sensors based on the concepts of observability and resolution are discussed. Simple examples are provided to explain the algorithms, and a complex FCCU case study is also discussed to underscore the utility of the algorithm for large flow sheets. The significance and scope of the proposed algorithms are highlighted.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Abstract: The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

Bibliographical review on reconfigurable fault-tolerant control systems

Abstract: In this paper, a bibliographical review on reconfigurable (active) fault-tolerant control systems (FTCS) is presented. The existing approaches to fault detection and diagnosis (FDD) and fault-tolerant control (FTC) in a general framework of active fault-tolerant control systems (AFTCS) are considered and classified according to different criteria such as design methodologies and applications. A comparison of different approaches is briefly carried out. Focuses in the field on the current research are also addressed with emphasis on the practical application of the techniques. In total, 376 references in the open literature, dating back to 1971, are compiled to provide an overall picture of historical, current, and future developments in this area.