In the aeronautical field, one of the major concerns is the availability of systems. To ensure availability, Prognosis and Health Management algorithms are developed. The aim of these algorithms is twofold. The first one is to detect and locate degradation premise of “no go” condition occurrence. The second one is to predict the health state of the system at a given time horizon. Before introducing PHM algorithms in operation, it is necessary to assess their performances. This is accomplished thank to a “maturation” phase. This phase consists in defining the performance metrics from an operational relevance point of view, in estimating this performance indicator and finally in proposing improvements to meet the airline companies requirements. We consider that the maturation of the detection function has already been completed and that we are interested in the maturation of the prognosis function. This paper deals with the performance assessment of a prognosis function using two operational metrics. A performance estimation procedure is developed. It is applied to the prognosis of turbofan engine lubricant over-consumption. The considered prognosis function is the probability to cross “no go” condition threshold at a given time horizon. This prediction is made thanks to an indicator of the health state of the system. Then it is compared with a threshold in order to trigger an alarm and give rise to a removal if necessary. Within this framework, we have defined two operational metrics for assessing the performance of this prognosis function. These metrics are the “ratio of justified removals” (P(Alarm|Crossing)) and the “ratio of not justified removals” (P(No-crossing|Alarm)). These metrics require the availability of observed lubricant over-consumption to compare the prediction results to the observed values. In the absence of lubricant over-consumption values in operation, a way is to simulate values.
This communication describes the procedure to estimate the performance of the prognosis function and presents the obtained results. The performances estimations trigger improvements. It appears that we have to enhance the precision of the considered health indicator before continuing to assess the performance of the considered prognosis function.
How to Cite
maturation, performance evaluation, PHM, failure prognosis, Turbofan engine, Engine Health Monitoring, Prognostic Evaluation
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