A dynamic predictive maintenance approach using probabilistic deep learning for a fleet of multi-component systems

Empowered by the ubiquitous sensing infrastructure, predictive maintenance (PdM) has received increasing attention. However, designing predictive maintenance for multiple systems remains a challenging problem as it requires effective integration of the prediction and scheduling under uncertainty and constraints. This paper proposes a dynamic PdM approach for a fleet of multi-component systems, which integrates deep learning-based (DL-based) probabilistic remaining useful life (RUL) prognostics into maintenance planning. First, a DL-based framework is designed for obtaining predictive distributions of RULs, and a probabilistic gated recurrent unit model is proposed for time series data. The Gaussian distribution is used to model prediction uncertainties, and the negative log-likelihood loss is used for model training, which inclines to emphasize the late degradation period. Then, an integer program is established and embedded in a rolling horizon approach for maintenance optimization. The concept of wasted residual values is proposed to quantify the impact of early retirement of components on total costs, which enables customized group maintenance planning under different cost settings. The applicability of the overall approach is illustrated for a fleet of aircraft based on a practical dataset. The experimental results show that our approach can provide accurate RUL prognostics and schedule maintenance flexibly to reduce costs.