Title: Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case study Show Chinese title

Title: 用于协作剩余有用寿命预测的联邦学习框架：一项飞机发动机案例研究

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. Show Chinese abstract

Abstract: 复杂系统（如航空发动机）由传感器持续监测。 在预测性航空维护中，收集的传感器测量值用于估计此类系统的健康状况和剩余可用寿命（RUL）。 然而，在开发预测技术时，一个主要挑战是运行至故障数据样本的数量有限。 如果多家航空公司能够共享其运行至故障的数据样本，则可以克服这一挑战，从而实现足够的学习效果。 然而，由于隐私问题，航空公司不愿意在集中式环境中共享其数据。 因此，本文提出了一种协作联邦学习框架。 在此框架中，多家航空公司合作训练一个集体的RUL预测机器学习模型，而无需集中共享其数据。 为此，提出了一种去中心化的验证程序，以在不共享任何数据的情况下验证预测模型。 此外，传感器数据通常存在噪声且质量较低。 因此，本文提出了四种新颖的方法来聚合全局预测模型的参数。 这些方法增强了联邦学习框架对噪声数据的鲁棒性。 所提出的框架通过使用N-CMAPSS数据集，展示了如何训练航空发动机的协作RUL预测模型。 在这里，考虑了六家航空公司，它们在联邦学习框架中合作，为其飞机发动机训练一个集体的RUL预测模型。 当将所提出的联邦学习框架与每家航空公司独立开发其自身预测模型的情况进行比较时，结果显示，联邦学习在六家航空公司中的五家实现了更准确的RUL预测。 此外，新型的鲁棒聚合方法使联邦学习框架对噪声数据样本具有鲁棒性。