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

• URL: http://arxiv.org/abs/2207.11353v2
• Date: Mon, 5 Jun 2023 01:29:22 GMT
• Title: A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data
• Authors: Chengyu Zhou and Xiaolei Fang
• Abstract summary: This paper proposes a supervised dimension reduction methodology for tensor data which has two advantages over most image-based prognostic models. 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.
• Score: 2.538209532048867
• License: http://creativecommons.org/licenses/by/4.0/
• 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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