A Bayesian approach to retrospective identification of change-points

For processing data relating to the performance of an apparatus, the data is analyzed using a Kalman Filter. After a first pass of data through the filter, the results are refined by discarding at least one less significant component performance change and/or sensor bias. The Kalman Filter is then re-run using the modified data. As further runs of the Kalman Filter are per formed, as required, the input of each successive run is refined by discarding from the preceding run at least one further component performance change and/or sensor bias. For each run, an objective function is evalu ated for the amount of unexplained measurement change and/or the amount of component performance change and sensor bias. The run whose results show an acceptable value for the objective function is selected as the best solution. In this way, the tendency of the Kal man Filter to distribute the cause of any sensed perfor mance change over all the possible sources of that change is avoided. The sets of measurement data are then analyzed to determine levels and/or trends in com ponent performance and sensor bias.

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