There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

Robust Model-Based Fault Diagnosis for Dynamic Systems

On robust estimation of the location parameter

The Theory of Matrices. By F R. Gantmacher. Two volumes, pp. 374 and 276. 1959. (Translated from the Russian by K. A. Hirsch; Chelsea Publishing Company, New York)

https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

/pdf/a-survey-of-fault-diagnosis-and-fault-tolerant-techniques-9uuf1uqysf.pdf

A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

It is common practice to correct the measurements taken from a process so that random or gross errors can be accounted for and reduced. Data reliability is crucial for classical operations such as regulation, control, optimization, identification, and can be secured through a balance equilibration algorithm. Historically, balance computation proved important in determining yield accounting, in allocating costs and in recording information upon which future action can be based. From the planning point of view, balance determination is important in process design, sensors location production planning.

Mass Balance Equilibration

This paper proposes a new approach for synthesizing a robust predictive controller for nonlinear uncertain process by exploiting a reduced complexity discrete-time Volterra model known as S-PARAFAC-Volterra model applied to the quadratic case. This proposed robust control copes with physical constraints and geometrical constraints due to parameter uncertainties and leads to the min-max optimization problem. The major advantage is that the objective function is convex with respect to the parameter uncertainty set which simplifies the min-max optimization problem and minimizes the worst-case value of the objective function taken over the set of uncertain models. A quadratic criterion is optimized and a new optimization algorithm, formulated as a quadratic programming (QP) under linear and nonlinear constraints, is proposed. The developed nonlinear robust control algorithm for uncertain process is illustrated on a benchmark as a continuous stirred-tank reactor system (SCTR).

Robust predictive controller for nonlinear uncertain process based on S-PARAFAC Volterra models

This paper proposes a new Proportional Integral Derivative (PID) control algorithm based on the Meixner-like model. The Meixner-like functions are an extension of Laguerre functions and are convenient when the system has a slow start. The parameters of the PID controller are auto adjusted by the model predictive control (MPC) technique. To improve system performance, the resolution of the problem of saturation of the control signal is proposed. A numerical example of a variable parameter system is given to illustrate the effectiveness of the proposed control approach.

Synthesis of a proportional integral derivative control law based on the Meixner-like model

This paper addresses the state estimation of two-time scale nonlinear systems by designing an unknown input multi-observer (UIMO). In order to design such an observer, the nonlinear system is transformed into an equivalent multiple model form and the fast dynamics are considered as unknown inputs. An application to a model of Wastewater Treatment Plants (WWTP) is considered and gives encouraging results.

State estimation for wastewater treatment plant with slow and fast dynamics using multiple models

Before improving the control of a plant, we must make sure of information coherence issued from instrument lines or sensors. In fact, these informations can be corrupted by errors and can also deviate from the optimum functioning range. The operator must make precautions not to be outside of this range. The detection of errors is used to point out the deviations. The detection, the location, the different error characterizations and the estimation of true values are steps in the data reconciliation problem. Process measurements are subject to two types of errors: first random errors generally taken to be independent and gaussian with zero mean and secondly gross errors which are caused by non-random events such as malfunctioning sensors, instrument biases and inexact process models. Various methods for detection and location of gross errors in process data have been proposed in recent years including the parity space approach, the standardized least square residuals approach and the standardized imbalance residuals approach. Process data reconciliation and their relationship to process monotoring have been the subject of many publications. For recent ones, see for example, Mah (1982), Gertler (1988) or Ragot (1990). In this survey, the authors try to summarize the various aspects of data reconciliation, to point out the main diffculties and to present the state of the art in this field. The authors present the steps of the data reconciliation problem in the following order: techniques of data reconciliation, classification of the data by the observability concept, gross error detection and localisation, variance of measurement error estimation.

https://hal.archives-ouvertes.fr/hal-00302871/document

José Ragot

Papers

Mass Balance Equilibration

Robust predictive controller for nonlinear uncertain process based on S-PARAFAC Volterra models

Synthesis of a proportional integral derivative control law based on the Meixner-like model

State estimation for wastewater treatment plant with slow and fast dynamics using multiple models

Data reconciliation for process flow