(383a) Actuator and Sensor Fault Isolation of Nonlinear Process Systems | AIChE

(383a) Actuator and Sensor Fault Isolation of Nonlinear Process Systems

Authors 

Du, M. - Presenter, McMaster University
Mhaskar, P., McMaster University
Scott, J., McMaster University



The last few decades have witnessed significant improvements in efficiency and profitability of chemical process operations due to the advances in automatic control techniques. The increased level of automation, however, also makes process control systems susceptible to equipment abnormalities, such as actuator and sensor faults. If not properly handled, they can lead to consequences ranging from failures to meet product quality specifications to plant shutdowns, incurring substantial economic losses and safety hazards. It is desired that faults be detected and the faulty equipment be accurately located so that corrective control action can be taken before they turn into a catastrophic failure. This realization has motivated significant research efforts in the area of fault detection and isolation (FDI).

A typical approach to FDI is to utilize the information embodied in a process (identification or deterministic) model to detect and isolate faults (see, e.g., [1, 2] for reviews). In this approach, residuals are generated as fault indicators by exploiting the analytical redundancy in a process model. Faults are detected by checking whether or not the residuals breach their thresholds, and isolated using certain isolation logic. Along this line, the problem of FDI has been studied extensively for linear systems, and existing results include the parity space approach, the observer approach, the fault detection filter approach, and the parameter identification approach (see, e.g., [1]). These results, however, may not remain effective for process systems with strong nonlinear dynamics.

Recently, the problem of FDI has also been studied for nonlinear process systems subject to actuator or process faults. In [3], a nonlinear FDI filter is designed to generate residuals that are affected by a particular fault and not affected by disturbances and the rest of faults. The problem of actuator fault isolation is also studied by exploiting the system structure to generate dedicated residuals (see, e.g., [4]). In addition, adaptive estimation techniques are used to explicitly account for unstructured modeling uncertainty for FDI (see, e.g., [5]). The above results rely on the FDI requirements being satisfied at nominal operating conditions. Recently, the idea of active fault isolation has been proposed to enhance fault isolation by driving the process to a desired operating point where residuals can become uniquely sensitive to faults (see [6]).

Compared to actuator faults, fewer results are available for sensor FDI of nonlinear process systems. In the existing results, this problem has been studied by using nonlinear state observers [7], adaptive estimation techniques [8], and a bank of observers [9]. The observer gain in [9], however, is obtained through the first order approximation of the nonlinear dynamics. Recently, a method that uses a bank of high-gain observers was proposed to isolate and handle sensor faults (see [10]). The enhanced applicability of the state observer (see also [11]) aids in the explicit consideration of process nonlinearity in the FDI design.

In summary, while there is a plethora of results that have addressed separately the problems of isolating actuator and sensor faults, there exist limited results on distinguishing between and isolating actuator and sensor faults in a unified framework for nonlinear process systems. As both faults are concerned, an actuator (or sensor) fault could result in unexpected behavior of residuals designed for isolation of only sensor (or actuator) faults because the computation of residuals may use incorrect input (or output) information. In existing results, the problem has been studied using two unscented Kalman filters dedicated to detect actuator and sensor faults, respectively (see [12]), and a squared residual is used to diagnose if an actuator or sensor fault takes place. However, the FDI design works with the assumption that only one actuator or sensor is faulty. In contrast, the results of this work are applicable to isolation of one or two simultaneous faults with a rigorous analysis on the generation of residuals.

Motivated by the above considerations, this work considers the problem of isolating actuator and sensor faults in nonlinear process systems. The key idea is to exploit the analytical redundancy in the system through state observer design. To this end, out of possible actuator and sensor faults, we consider subsets of faults, and design state observers and associated residuals such that each is only sensitive to faults in the corresponding subset. With the ability of detecting faults in a subset, they can be isolated by checking whether the corresponding residuals breach their thresholds. The proposed method enables differentiating between actuator and sensor faults while explicitly accounting for system nonlinearity. The effectiveness of the fault isolation design is illustrated using a chemical reactor example.

References

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