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Wheels coating process monitoring in the presence of nuisance parameters using sequential change-point detection method

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Abstract

The paper addresses the problem of monitoring a non-stationary online process to detect an abrupt failure. The process studied in this paper is the one of wheels coating, but the proposed method can be extended to a broad range of processes. Using a camera, a picture of every wheel is captured for traceability. This image is used, in our problem, to measure the coating intensity via pixels mean value. In our operational context, it is wished to control the false alarm probability over a long period (typically a day) as well as to keep the detection delay under a given number of observations, which corresponds to a small number of wheels with defective coating. The problem of abrupt coating problem detection is addressed using a sequential method that takes into account those two requirements while it is also able to adapt to the non-stationnarity of the process. Numerical results on a large set of wheels images show the efficiency of the proposed approach.

Dates and versions

hal-02573432 , version 1 (14-05-2020)

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Karim Tout, Florent Retraint, Rémi Cogranne. Wheels coating process monitoring in the presence of nuisance parameters using sequential change-point detection method. 2017 25th European Signal Processing Conference (EUSIPCO), Aug 2017, Kos, Greece. pp.196-200, ⟨10.23919/EUSIPCO.2017.8081196⟩. ⟨hal-02573432⟩
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