  Rotating machinery is essential to modern life, from power generation to
transportation and a host of other industrial applications. Since such
equipment generally operates under challenging working conditions, which can
lead to untimely failures, accurate remaining useful life (RUL) prediction is
essential for maintenance planning and to prevent catastrophic failures. In
this work, we address current challenges in data-driven RUL prediction for
rotating machinery. The challenges revolve around the accuracy and uncertainty
quantification of the prediction, and the non-stationarity of the system
degradation and RUL estimation given sensor data. We devise a novel
architecture and RUL prediction model with uncertainty quantification, termed
VisPro, which integrates time-frequency analysis, deep learning image
recognition, and nonstationary Gaussian process regression. We analyze and
benchmark the results obtained with our model against those of other advanced
data-driven RUL prediction models for rotating machinery using the PHM12
bearing vibration dataset. The computational experiments show that (1) the
VisPro predictions are highly accurate and provide significant improvements
over existing prediction models (three times more accurate than the second-best
model), and (2) the RUL uncertainty bounds are valid and informative. We
identify and discuss the architectural and modeling choices made that explain
this excellent predictive performance of VisPro.
