Sensor selection in data modeling is an important research topic for prognostics. The performance of prediction model may vary considerably under different variable subset. Hence it is of great important to devise a systematic sensor selection method that offers guidance on choosing the most representative sensors for prognostics. This paper proposes a sensor selection method based on the improved grey correlation analysis. From empirical observation, all the continuous-value sensors with a consistent monotonic trend are firstly selected for data fusion, and a linear regression model is used to convert the multi-dimensional sensor readings into one-dimensional health factor (HF). The correlation between HF and each of the selected sensors is evaluated by calculating the grey correlation degree defined on two time series. The optimal sensor subset with a relatively large correlation degree is selected to execute the final fusion. The effectiveness of the proposed method was verified experimentally on the turbofan engine simulation data supplied by NASA Ames, using instance-based learning methodology, and the experimental results showed that RUL prediction with fewer sensor inputs can obtain a more accurate prognostics performance than using all sensors initially considered relevant.
How to Cite
sensor selection, grey correlation analysis, residual useful life prediction, similarity measures
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