Remaining Useful Life Prediction of Turbo Actuators for Predictive Maintenance of Diesel Engines

Abstract

Prognostics applications in the automotive industry are growing rapidly and customers have begun to expect this capability. Remaining useful life (RUL) models are an important aspect of a prognostic as they affect both how far in advance and with what confidence failures can be predicted. Model selection and design must include technical considerations such as mathematical complexity and training data availability, as well as business considerations such as implementation plans, constraints, and risks of inaccurate predictions.

This paper compares different RUL models that have been developed for turbo actuators on diesel engines, with the business objective of advising bus fleet customers on preventive maintenance intervals. The design, development, validation, and resulting prediction accuracy of each RUL model is detailed. A selection process is then applied to choose the model best suited to the intended purpose. In doing so, the paper sheds light on strengths and weaknesses of deep learning RUL models over statistical RUL models. The paper also focuses on the state-of-the-art deep learning network “Tabnet” and its results for useful life predictions. Among the different methods, Accelerated Weibull Failure Time model provides better predictions with a concordance of 0.94 and ~15% less error than any other model.