Weighted Reconstruction-Based Contribution for Improved Fault Diagnosis

In this article, a new fault diagnosis method is proposed based on weighted reconstruction-based contribution (WRBC) analysis. The new method reduces fault smearing and, therefore, improves diagnosis accuracy. The current RBC analysis finds the sensor directions and amplitudes that, if removed, would minimize certain quadratic fault indices. However, the estimate of the RBC coefficient along a certain fault direction is typically subject to contamination by the effects of other directions. This is because the RBC kernel matrices reside in a subspace onto which the projections of data from various orthogonal fault directions become collinear, which leads to smearing of the corresponding fault coefficient estimates. This is especially the case for faults involving several sensor variables. Motivated by this observation, we propose to first filter the test data by a set of orthogonalized fault directions and then perform the reconstruction-based contribution calculation. This weighted RBC analysis method can reduce fault coefficient smearing and, therefore, adaptively improve diagnosis accuracy. The filter coefficient allows a flexible tradeoff between fault smearing across fault directions and bias contribution from normal data. These results are demonstrated through numerical experiments with both single-sensor and multisensor faults.