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A new concept of reliability of estimation of variables is introduced which relates to the estimability of variables in the presence of sensor failures. Based on this concept, a method for optimal location of sensors in apureflowprocess is developed. A graph-theoretic algorithm, SENNET, developed for this purpose, is shown to perform robustly and give globally optimum solutions for realistic processes.

Coventional control schemes are developed under the assumption that the sensors and actuators are free from faults. However, the occurrence of faults will cause degradation in the closed-loop performance and also will have an impact on safety, productivity, and plant economy. In the present work, we have proposed a fault-tolerant control scheme (FTCS) by integrating a fault detection and identification (FDI) technique with conventional control. The principal component of our proposed FTCS is a compensation strategy (supervisory system) which uses the information provided by the FDI to appropriately modify the controller as well as the model used in FDI. This allows online application of the FTCS without causing significant degradation in the closed-loop performance due to the occurrences of biases in sensors and actuators or due to changes in unmeasured disturbance variables and due to moderate change in process parameters. Through stochastic simulation studies of a continuous stirred tank reactor process, we demonstrate that our proposed FTCS leads to significant improvement in the closed-loop performance in comparison to a conventional control scheme, especially as the fault magnitude increases. The proposed compensation scheme also allows identification of multiple faults that occur sequentially in time and is also found to be robust with respect to moderate plant−model mismatch.

In part 2 of this two-part series, an approach for diagnosis and quantification of stiction using a simple single-parameter model is proposed. The stiction model, in conjunction with an identified process model from routine operating data, is shown to successfully facilitate stiction diagnosis. An optimization approach is used to jointly identify the process model and the stiction parameter. This approach is based on the identification of a Hammerstein model of the system comprising the sticky valve and the process. In this work, a new identification procedure for Hammerstein systems that supports stiction diagnosis is proposed. Industrial and simulation case studies are shown to demonstrate the application of the proposed approach for diagnosing stiction.

A new method for detecting, identifying, and estimating gross errors in steady state processes is described in this paper. The generalized likelihood ratio method is based on the likelihood ratio statistical test and provides a general framework for identifying any type of gross error that can be modeled. The procedure is illustrated with gross errors caused by measurement biases and leaks. One significant advantage of the method is that the identification of gross errors is not confounded by departure from steady state conditions, which may now be accounted for by "leaks." Also proposed is a new strategy for identifying multiple gross errors using serial compensation of gross errors, which may be applied to all types of gross errors including leaks and biases and which requires less computing time than the existing strategies.