Early detection of faults in process plants is essential for safe and economic operation. After the initiation of a fault, process variables reveal the effects of fault as time passes. However, the measurement of every variable is not economically or practically feasible. Furthermore, placing an optimal number of sensors for proper detection and identification of faults becomes challenging for large and complicated process.

Most of the sensor location problems in the literature for large processes have been solved using qualitative models of the plant because of the easy translation of Cause-Effect (CE) relations into graphical and mathematical representations. Directed Graph (DG) and Signed Directed Graph(SDG) are commonly practiced approaches for sensor network design. In DG and SDG, variables are chosen for optimal sensor network for fault observabilty and resolution from the sets of responding variables against faults. However, for fault resolution, variables are selected from the response sets for a pair of faults. In DG, when two or more different faults yield same set of responding variables, those faults cannot be distinguished from each other. Even SDG cannot distinguish these faults if the response variables deviate in the same direction in each set.

To overcome the fault resolution limitation, the magnitude of deviation of response variables and fault propagation time through variables are included in developing the sensor placement algorithm. Even though two different faults may yield the same set of responding variables. with the same directionality for each variable, there is a very high chance the deviation of variables will differ in magnitude for each fault. The pair-wise ratio of response variable magnitudes is demonstrated to successfully distinguish between otherwise faults that can’t be resolved with DG or SDG alone. The another idea is that different variables will respond to the faults at different times. For a particular fault, one variable may respond much earlier than another variable. Selection of this pair of variables for the sensor network helps to identify that particular fault. Therefore , observation of fault propagation time of pair-wise variables can resolve additional faults in the system.

In this work, a sensor placement algorithm was developed using variable magnitude ratios and fault propagation times for fault resolution. A set cover problem is formulated subject to fault observability and resolution. The proposed algorithm is employed to find optimal sensor network for a simple CSTR as well as the Tennessee Eastman process.