Image-Based Prognostics Using Penalized Tensor Regression

ABSTRACT

This article proposes a new methodology to predict and update the residual useful lifetime of a system using a sequence of degradation images. The methodology integrates tensor linear algebra with traditional location-scale regression widely used in reliability and prognostics. To address the high dimensionality challenge, the degradation image streams are first projected to a low-dimensional tensor subspace that is able to preserve their information. Next, the projected image tensors are regressed against time-to-failure via penalized location-scale tensor regression. The coefficient tensor is then decomposed using CANDECOMP/PARAFAC (CP) and Tucker decompositions, which enables parameter estimation in a high-dimensional setting. Two optimization algorithms with a global convergence property are developed for model estimation. The effectiveness of our models is validated using two simulated datasets and infrared degradation image streams from a rotating machinery.

KEYWORDS:

• Image streams
• (Log)-location-scale distribution
• Penalized tensor regression
• Residual useful lifetimes

Acknowledgment

The authors thank the reviewers and editors for their constructive comments and suggestions, which have considerably improved the article.

Supplementary Material

The proofs of all propositions and code for simulation studies are given in the online supplementary materials.

Additional information

Funding

This work was supported by National Science Foundation Grants CMMI-1536555.