Predicting Remaining Useful Life with Sparse Measurement Data

Predictive maintenance is a central concept in the shift towards Industry 4.0. Accurately estimating the remaining useful life of a machine, or a machine component, is an important aspect of predictive maintenance. Deep learning models have previously been applied to this task with success. However, these models may not perform well for cases where training data is sparse. In these situations, the model should also provide some degree of uncertainty about its prediction to instill trust in the user. Hence, predictive models should accurately estimate their own uncertainty, in addition to providing correct predictions. In this paper, we propose up-sampling of sparse ballbar measurement data in order to generate adequate samples to train and evaluate deep neural networks. The inference is conducted with three different types of models, Monte Carlo Dropout, variational inference, and deep ensemble. The approaches are compared based on point prediction accuracy, and uncertainty quantification quality. It is found that both Monte Carlo Dropout and deep ensemble perform well in regards to predictive accuracy, with the deep ensemble consistently resulting in the best calibrated uncertainty estimation.