Journal
TECHNOMETRICS
Volume 60, Issue 4, Pages 484-496Publisher
AMER STATISTICAL ASSOC
DOI: 10.1080/00401706.2017.1383310
Keywords
Condition monitoring signals; Convolution process; Multitask learning; Multivariate Gaussian process; Remaining useful life
Categories
Funding
- National Science Foundation [1335129, 1343969]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1335129] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1343969] Funding Source: National Science Foundation
Ask authors/readers for more resources
Condition monitoring (CM) signals play a critical role in assessing the remaining useful life of in-service components. In this article, an alternative view on modeling CM signals is proposed. This view draws its roots from multitask learning and is based on treating each CM signal as an individual task. Each task is then expressed as a convolution of a latent function drawn from a Gaussian process (GP), and the transfer of knowledge is achieved through sharing these latent functions between historical and in-service CM signals. Aside from being nonparametric, the flexible and individualistic approach in our model can account for heterogeneity in the data and automatically infer the commonalities between the new testing observations and CM signals in the historical dataset. The robustness and advantageous features of the proposed method are demonstrated through numerical studies and a case study with real-world data in the application to find the remaining useful life prediction of automotive lead-acid batteries. Technical details and additional numerical results are available in the supplementary materials.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available