Closed-Loop Active Model Diagnosis Using Bhattacharyya Coefficient
Application to Automated Visual Inspection
Jacques Noom (TU Delft - Team Michel Verhaegen)
Hieu Thao Thao (TU Delft - Team Michel Verhaegen)
Oleg Soloviev (TU Delft - Team Michel Verhaegen)
MHG Verhaegen (TU Delft - Team Michel Verhaegen)
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Abstract
This manuscript presents an improvement of state-of-the-art Closed-Loop Active Model Diagnosis (CLAMD). The proposed method utilizes weighted Bhattacharyya coefficients evaluated at the vertices of the polytopic constraint set to provide a good trade-off between computational efficiency and satisfactory input choice for separation of candidate models of a system. A simulation of a dynamical system shows the closed-loop performance not being susceptible to the combination of candidate models. Additionally, the broad applicability of CLAMD is shown by means of a demonstrative application in automated visual inspection. This application involves sequential determination of the optimal object inspection region for the next measurement. As compared to the conventional approach using one full image to recognize handwritten digits from the MNIST dataset, the novel CLAMD-approach needs significantly (up to 78%) less data to achieve similar accuracy.