Shankar Narasimhan S

In this contribution we propose an active Fault Tolerant Control (FTC) strategy which enables the isolation and identification of valve stiction and valve blocking, in addition to the additive faults like sensor and actuator biases. The developed method is an extension of the original method proposed by Prakash et al. (2002). This method is based on the Kalman filter and is developed under the assumption that the monitored system is Linear Time Invariant (LTI). It has been shown to work well for additive faults such as sensor and actuator biases. Within this method the fault isolation and identification task is based on the Generalized Likelihood Ratio (GLR) test by which the most plausible fault type in a library of faults is selected following estimation of fault parameters. © 2010 Elsevier B.V.

In this contribution, we present initial developments in view of model-based fault-tolerant control (FTC). In this context, we use an original method based on the Kalman-filter by which fault detection, diagnosis and accommodation is possible provided that an accurate model is available. Since this is not generally true, we attempt to alleviate this necessity by means of accounting for uncertainty, in both model as well as in the measurements used for fault diagnosis. Our preliminary results are focused on the diagnosis step in the FTC scheme. © 2009 IEEE.

Identification of input-output models from data is of utmost relevance in chemical process industries and has applications in process monitoring, control and fault diagnosis. Input-output data used in such identification exercises often has measurement errors in both the variables. Model identification under such conditions translates to solving an errors-in-variables (EIV) problem which is difficult to solve using classical system identification techniques. A recently proposed method - Dynamic Iterative Principal Component Analysis (DIPCA) uses PCA framework to identify the process order, delay, model parameters, and error variances. DIPCA, however, has certain shortcomings under small sample conditions which limit its practical applications. In this work, we address these shortcomings, namely ambiguity in order determination under small sample cases and arbitrary selection of stacking lag which leads to sub-optimal parameter estimates. We define a metric called 'd-selective eigenvalue ratio', or d-SEVR that sharply identifies the true order even for small sample cases. We also demonstrate the existence of an optimal stacking lag corresponding to the lowest error in estimation of error-covariance matrix. Finally, we use the identified model to obtain reconciled estimates of variables using Kalman Filter.

This paper is concerned with the application of Kalman filter based methods for Fault Detection and Identification (FDI). The original Kalman based method, formulated for bias faults only, is extended for three more fault types, namely the actuator or sensor being stuck, sticky or drifting. To benchmark the proposed method, a nonlinear buffer tank system is simulated as well as its linearized version. This method based on the Kalman filter delivers good results for the linear version of the system and much worse for the nonlinear version, as expected. To alleviate this problem, the Extended Kalman Filter (EKF) is investigated as a better alternative to the Kalman filter. Next to the evaluation of detection and diagnosis performance for several faults, the effect of dynamics on fault identification and diagnosis as well as the effect of including the time of fault occurrence as a parameter in the diagnosis task are investigated. © 2011 Elsevier Ltd.