Sensor Selection Embedded in Active Fault Diagnosis Algorithms

A methodology is presented for simultaneous sensor selection and design of fault diagnosis tests in complex systems, for which steady-state or dynamic models are available. The method assesses all possible sensor combinations for their information with respect to system faults in the presence of uncertainty and chooses sensors based on their contribution to information gain. In particular, sensors are cast as binary variables in the normalized Fisher information matrix, which is used to calculate the optimal fault detection and isolation (FDI) test designs by manipulating admissible system inputs. Test design optimization is always naturally embedded in the proposed method. Then, the Kullback-Leibler divergence and the Hellinger distance are used to explore the isolation capability of an FDI test when uncertainty is considered for the system inputs and parameters. The FDI tests are deployed and verified using a principal component analysis and the k-nearest neighbor classification algorithm. The capability of the method to detect and isolate faults at high correct classification rates and low false alarm rates is demonstrated. The tool-chain proposed is tested on a benchmark three-tank system at various levels of measurement noise and uncertainty. The benefits and drawbacks of each step of the proposed method are assessed and discussed along with their computational footprint.

Automated summary

The methodology presented in this study combines sensor selection and fault diagnosis test design for complex systems. By assessing sensor combinations and optimizing test designs based on information gain, the method demonstrates high correct classification rates and low false alarm rates in fault detection and isolation. The proposed approach effectively detects and isolates faults in systems with uncertainty.