Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case study

2025-05-31

AI Summary

Addressing the dual challenges of scarce run-to-failure data and privacy-sensitive cross-airline data sharing in aircraft engine Remaining Useful Life (RUL) prediction, this paper proposes a federated learning framework tailored for RUL estimation. The framework incorporates a decentralized validation mechanism and introduces four robust parameter aggregation strategies to enhance model stability and generalization under noisy sensor measurements. Experiments involving six airlines on the N-CMAPSS dataset demonstrate that five participants achieve higher RUL prediction accuracy than their respective local standalone models. The proposed aggregation methods effectively mitigate the adverse effects of statistical heterogeneity and measurement noise across clients. This work delivers a scalable, robust, and privacy-preserving federated learning solution for high-reliability predictive modeling in sensitive industrial settings.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Safety and Robustness
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

Abstract

Complex systems such as aircraft engines are continuously monitored by sensors. In predictive aircraft maintenance, the collected sensor measurements are used to estimate the health condition and the Remaining Useful Life (RUL) of such systems. However, a major challenge when developing prognostics is the limited number of run-to-failure data samples. This challenge could be overcome if multiple airlines would share their run-to-failure data samples such that sufficient learning can be achieved. Due to privacy concerns, however, airlines are reluctant to share their data in a centralized setting. In this paper, a collaborative federated learning framework is therefore developed instead. Here, several airlines cooperate to train a collective RUL prognostic machine learning model, without the need to centrally share their data. For this, a decentralized validation procedure is proposed to validate the prognostics model without sharing any data. Moreover, sensor data is often noisy and of low quality. This paper therefore proposes four novel methods to aggregate the parameters of the global prognostic model. These methods enhance the robustness of the FL framework against noisy data. The proposed framework is illustrated for training a collaborative RUL prognostic model for aircraft engines, using the N-CMAPSS dataset. Here, six airlines are considered, that collaborate in the FL framework to train a collective RUL prognostic model for their aircraft's engines. When comparing the proposed FL framework with the case where each airline independently develops their own prognostic model, the results show that FL leads to more accurate RUL prognostics for five out of the six airlines. Moreover, the novel robust aggregation methods render the FL framework robust to noisy data samples.

Problem

• Federated learning enables collaborative RUL prognostics without data sharing
• Decentralized validation ensures model accuracy without compromising privacy
• Robust aggregation methods improve FL framework resilience to noisy data

Innovation

• Federated learning for collaborative RUL prognostics
• Decentralized validation without data sharing
• Robust aggregation methods for noisy data