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arXiv · 2311.07474

A Federated Data Fusion-Based Prognostic Model for Applications with Multi-Stream Incomplete Signals

Madi Arabi·Xiaolei Fang
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Abstract

Most prognostic methods require a decent amount of data for model training. In reality, however, the amount of historical data owned by a single organization might be small or not large enough to train a reliable prognostic model. To address this challenge, this article proposes a federated prognostic model that allows multiple users to jointly construct a failure time prediction model using their multi-stream, high-dimensional, and incomplete data while keeping each user's data local and confidential. The prognostic model first employs multivariate functional principal component analysis to fuse the multi-stream degradation signals. Then, the fused features coupled with the times-to-failure are utilized to build a (log)-location-scale regression model for failure prediction. To estimate parameters using distributed datasets and keep the data privacy of all participants, we propose a new federated algorithm for feature extraction. Numerical studies indicate that the performance of the proposed model is the same as that of classic non-federated prognostic models and is better than that of the models constructed by each user itself.

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BibTeXRIS

Madi Arabi, Xiaolei Fang. 2023-11-13. A Federated Data Fusion-Based Prognostic Model for Applications with Multi-Stream Incomplete Signals. https://arxiv.org/abs/2311.07474

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