An ensemble model considering health index based classification for remaining useful life prediction

Abstract

Accurate prediction of remaining useful life (RUL) plays an important role in the formulation of maintenance strategies. However, due to the diversity of the failure mode of equipment, there are significant differences between the degradation data, which greatly affects the accuracy of RUL prediction. In this case, an ensemble prediction model considering health index-based (HI-based) classification is proposed in this paper. Firstly, the stacked autoencoder (SAE) is employed to construct the HI. Then, the time window is used to sequentially process the HI sequence, so that many data segments with the same length can be achieved. To differentiate the data with the similar degradation process, K-means and Xgboost are selected to construct offline and online data classification models respectively. Finally, according to the results of the data classification, the ensemble model that integrates multiple machine learning methods are separately trained and then adaptively used for RUL prediction. In addition, integrating multiple methods helps to improve the generalization ability of the model. The NASA C-MAPSS dataset is applied to verify the effectiveness of the proposed method, and the results show that the proposed method achieves a higher prediction accuracy and shorter computational time than other existing prediction models.

Remaining useful life prediction method based on stacked autoencoder and generalized wiener process for degrading bearing

“…Experimental validation on the Xi'an Jiaotong University bearing dataset confirms the method's effectiveness in achieving reliable RUL predictions. Han et al [11] achieved the necessary one-dimensional HI construction by enhancing the feature extraction capabilities of the SAE through increased neural network depth. Francisco et al introduced an integrated approach based on DAE and Local Linear Embedding to construct an HI that reflects the dynamic degradation of bearings, which was employed in their research for lifetime prediction.…”

Non‐periodic inspection and replacement policy of system subject to non‐homogeneous gamma degradation process

“…The literature on preventive maintenance planning can be broadly categorized into two types: time-based maintenance (TBM) 5,6 and condition-based maintenance (CBM). [7][8][9] For systems whose degradation states are not available, TBM is often employed. 10 TBM involves performing a series of maintenance actions at predetermined time epochs.…”