Real-time predictive maintenance of hardware components using a stacked deep learning architecture on time-variant parameters combined with a dense neural network supplied with exogeneous static outputs

Abstract

A system, method, and computer-readable medium are provided for a hardware component failure prediction system that can incorporate a time-series dimension as an input while also addressing issues related to a class imbalance problem associated with failure data. Embodiments utilize a double-stacked long short-term memory (DS-LSTM) deep neural network with a first layer of the DS-LSTM passing hidden cell states learned from a sequence of multi-dimensional parameter time steps to a second layer of the DS-LSTM that is configured to capture a next sequential prediction output. Output from the second layer is combined with a set of categorical variables to an input layer of a fully-connected dense neural network layer. Information generated by the dense neural network provides prediction of whether a hardware component will fail in a given future time interval.