A Predictive Maintenance Strategy Using Deep Learning Quantile Regression and Kernel Density Estimation for Failure Prediction

Failure prediction and maintenance decision-making are two core activities in a prognostics and health management (PHM) system. However, they are often studied independently and hierarchically. The main goal of this article is to combine system failure prediction with maintenance decision-making to develop a predictive maintenance strategy. System failure prediction is achieved by constructing an ensemble model of deep autoencoder (DAE), long short-term memory (LSTM), quantile regression (QR), and kernel density estimation (KDE), namely DAE-LSTMQR-KDE. Then, based on the probability density of system failure time obtained from the ensemble model, a replacement cost function (RCF) and an ordering cost function (OCF) are proposed to support maintenance and inventory decisions. Finally, optimal decisions are determined by minimizing the two cost functions. A score equal to 246.59 and a coverage width-based criterion (CWC) index equal to 0.35 were obtained when the DAE-LSTMQR-KDE ensemble model was applied to the C-MAPSS dataset, while the average maintenance cost rate (MCR) of the proposed maintenance strategy was 0.74. The results demonstrated that the proposed prediction and maintenance method outperforms several state-of-the-art methods. In addition, different cost structure scenarios are also investigated to illustrate the flexibility of maintenance decisions based on failure prediction information.

Automated summary

This study combines system failure prediction with maintenance decision-making to develop a predictive maintenance strategy. System failure prediction is achieved by constructing an ensemble model (DAE-LSTMQR-KDE). Replacement cost and ordering cost functions are proposed based on the probability density of system failure time obtained from the ensemble model to support maintenance and inventory decisions. Optimal decisions are determined by minimizing these cost functions. Experimental results show the superiority of the proposed prediction and maintenance method compared to state-of-the-art methods. Different cost structure scenarios are also investigated to demonstrate the flexibility of maintenance decisions based on failure prediction information.