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

• URL: http://arxiv.org/abs/2311.07474v2
• Date: Tue, 9 Apr 2024 21:13:46 GMT
• Title: A Federated Data Fusion-Based Prognostic Model for Applications with Multi-Stream Incomplete Signals
• Authors: Madi Arabi, Xiaolei Fang,
• Abstract summary: This article proposes a federated prognostic model that allows multiple users to jointly construct a failure time prediction model. Numerical studies indicate that the performance of the proposed model is the same as that of classic non-federated prognostic models.
• Score: 1.2277343096128712
• License: http://creativecommons.org/licenses/by/4.0/
• 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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