Remaining Useful Life Estimation Using CNN-XGB With Extended Time Window

The remaining useful life estimation has been widely studied for engineering systems. A system commonly works under varying operating conditions, which may affect the system degradation trajectory differently and consequently reduce the accuracy of remaining useful life estimation. In this paper, we propose CNN-XGB with extended time window to tackle this issue. Firstly, the extended time window is created by feature extension and time window processing in data preprocessing. In feature extension, multiple degradation features are extracted by an improved differential method, and these features are appended to the raw data as additional features. To make the time window cover more information for better prognostic accuracy, a time window padding method is used considering the problem of missing data in some samples. Secondly, a convolutional neural network architecture with multichannel 1 * 1 filter kernel is proposed considering the effect of varying operating conditions. Furthermore, to improve the prognostic robustness and avoid the sensitivity to the abnormal data, convolutional neural network and extreme gradient boosting are fused by model averaging (CNN-XGB). The validity of the proposed method is verified using aero-engine datasets from NASA.

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