Physics-Based Gaussian Process Method for Predicting Average Product Lifetime in Design Stage


The average lifetime or the mean time to failure (MTTF) of a product is an important metric to measure the product reliability. Current methods of evaluating the MTTF are mainly based on statistics or data. They need lifetime testing on a number of products to get the lifetime samples, which are then used to estimate the MTTF. The lifetime testing, however, is expensive in terms of both time and cost. The efficiency is also low because it cannot be effectively incorporated in the early design stage where many physics-based models are available. We propose to predict the MTTF in the design stage by means of a physics-based Gaussian process (GP) method. Since the physics-based models are usually computationally demanding, we face a problem with both big data (on the model input side) and small data (on the model output side). The proposed adaptive supervised training method with the Gaussian process regression can quickly predict the MTTF with a reduced number of physical model calls. The proposed method can enable continually improved design by changing design variables until reliability measures, including the MTTF, are satisfied. The effectiveness of the method is demonstrated by three examples.


Mechanical and Aerospace Engineering

Research Center/Lab(s)

Center for High Performance Computing Research

Keywords and Phrases

Adaptive training; Average lifetime; Gaussian process model; Machine learning for engineering applications; Mean time to failure; Physicsbased simulations; Supervised learning

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


File Type





© 2021 American Society of Mechanical Engineers (ASME), All rights reserved.

Publication Date

01 Aug 2021