(348b) Enhancing Data-Based Fault Isolation Through Feedback Control
Successful management of abnormal situations encountered in the operation and control of chemical processes requires fast and accurate information regarding the nature and location of any failures that have occurred. This information along with multiple available control configurations allows failures to be handled efficiently through fault-tolerant control (see, for example, ) to minimize economic, environmental, health and safety repercussions. As a key element in fault-tolerant control, fault detection and isolation is an area of great importance that has received a significant amount of attention in recent years (see [2, 3] for reviews).
In general, methods of fault diagnosis can be divided into two major categories: model-based and data-based. Model-based methods use a mathematical model of the process to design dynamic filters that compute residuals using process measurements; in this way, fault detection and isolation can be accomplished for specific model and fault structures (see, for example, ). Data-based methods are based primarily on process measurements. Using measured process data, it is possible to extract information about a fault by comparing the location and/or direction of the system in the state-space with past behavior under faulty operation (e.g., ); this approach works particularly well when the process exhibits linear behavior and the faults are not complex functions/disturbances of time.
The present work  focuses on a broad class of nonlinear process systems subject to control actuator faults and disturbances and presents a method for data-based fault detection and isolation that takes into account the design of the feedback control law. The isolability of particular faults is explicitly characterized including the conditions under which the faults may become isolable. This method allows isolating specific complex faults in the closed-loop system using a purely data-based approach, without requiring prior knowledge of fault history. This is achieved through the design of appropriate nonlinear, model-based state-feedback control laws that decouple the dependency between certain process state variables in the closed-loop system in order to satisfy the isolability conditions of the specific faults. Both Lyapunov-based explicit control laws and model predictive control laws with suitable isolability constraints are proposed and compared. In this sense, the proposed approach constitutes a departure from the available data-based fault detection and isolation techniques which do not take advantage of the design of the feedback control law to enforce a closed- loop system structure that enhances fault isolation. The theoretical results are successfully demonstrated through a chemical reactor example and a gas-phase polyethylene reactor example.
 Mhaskar P., Gani A., McFall C., Christofides P. D., Davis J. F. "Fault-Tolerant Control of Nonlinear Systems Subject to Sensor Data Losses" AIChE Journal, vol., 53, pp. 654-668, 2007.
 Venkatasubramanian V., Rengaswamy R., Kavuri S.N., Yin K. "A review of process fault detection and diagnosis Part III: Process history based methods" Computers and Chemical Engineering, vol. 27, pp. 327-346, 2003.
 Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S.N. "A review of process fault detection and diagnosis Part I: Quantitative model-based methods" Computers and Chemical Engineering, vol. 27, pp. 293-311, 2003.
 Mhaskar P., McFall C., Gani A., Christofides P. D., Davis J. F. "Isolation and Handling of Actuator Faults in Nonlinear Systems", regular paper in press, Automatica, 2007.
 Yoon S., MacGregor J.F. "Fault diagnosis with multivariate statistical models part I: using steady state fault signatures", Journal of Process Control, vol. 11, pp. 387-400, 2001.
 Ohran B. J., Muñoz de la Peña D., Christofides P. D., Davis J. F. "Enhancing Data-based Fault Isolation Through Nonlinear Control", submitted, AIChE Journal, 2007.