Sensor Fault Detection and Measurement Reconstruction Using an Analytical Optimization Approach

Abstract

This paper presents a generalization of multi-dimensional linear regression to facilitate multi-sensor fault detection and measurement reconstruction via analytical optimization method. Key benefits of the proposed technique are that it facilitates (i) Real-time detection of sensor faults in a multi-sensor system; (ii) Reconstruction of measurements that would normally be expected from the sensor at fault―thereby facilitating improved unit availability; (iii) Determining the minimum number of non-faulty sensors that are required to be available to continue unit operation without unduly compromising performance. The use of an analytical formulation of the optimal correlation matrix to determine (i)-(iii) means that the resulting technique incurs low computational overhead and is readily applied to real-time monitoring and subsequent remedial action. Experimental results demonstrate the efficacy of the developed procedures to facilitate continued unit operation in the event of sensor faults. It should be noted that the proposed techniques are much more widely applicable to numerous industrial and commercial systems.