Robust stochastic process models and parameter estimation for industrial end-of-line-testing

Common data-based methods for fault detection are often static and non-robust against outliers. To facilitate industrial end-of-line (EOL) testing of complex products with hundreds of potentially drifting / non-stationary features using automatically determined test limits, we investigate robust stochastic statespace models. We compare different filtering algorithms, and implement parameter learning and the calculation of test limits. Variable prediction horizons allow for realistic scenarios where tests are irregularly spaced over time. Exemplary application results are shown for industrial EOL tests of car engines.

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