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arXiv · 2207.11353

A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data

Chengyu Zhou·Xiaolei Fang
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Abstract

This paper proposes a supervised dimension reduction methodology for tensor data which has two advantages over most image-based prognostic models. First, the model does not require tensor data to be complete which expands its application to incomplete data. Second, it utilizes time-to-failure (TTF) to supervise the extraction of low-dimensional features which makes the extracted features more effective for the subsequent prognostic. Besides, an optimization algorithm is proposed for parameter estimation and closed-form solutions are derived under certain distributions.

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BibTeXRIS

Chengyu Zhou, Xiaolei Fang. 2022-07-22. A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data. https://arxiv.org/abs/2207.11353

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