In the last years, predictive maintenance has gained a central position in condition-based maintenance tasks planning. Machine learning approaches have been very successful in simplifying the construction of prognostic models for health assessment based on available historical labeled data issued from similar systems or specific physical models. However, if the collected samples suffer from lack of labels (small labeled dataset or not enough samples), the process of generalization of the learning model on the dataset as well as on the newly arrived samples (application) can be very difficult. In an attempt to overcome such drawbacks, a new deep supervised learning approach is introduced in this paper. The proposed approach aims at extracting and learning important patterns even from a small amount of data in order to produce more general health estimator. The algorithm is trained online based on local receptive field theories of extreme learning machines using data issued from a propulsion system simulator. Compared to extreme learning machine variants, the new algorithm shows a higher level of accuracy in terms of approximation and generalization under several training paradigms.

在过去的几年中，预测性维护已在基于状态的维护任务计划中占据中心位置。机器学习方法已成功地简化了基于类似系统或特定物理模型发布的可用历史标记数据的健康评估预测模型的构建。但是，如果收集的样本缺少标签（小标签数据集或样本不足），则在数据集以及新到达的样本（应用程序）上推广学习模型的过程将非常困难。为了克服这些缺点，本文引入了一种新的深度监督学习方法。所提出的方法旨在甚至从少量数据中提取和学习重要模式，以产生更一般的健康估计量。该算法是根据极限学习机的局部接受场理论使用推力系统模拟器发出的数据在线进行训练的。与极限学习机的变体相比，新算法在几种训练范式下的逼近和泛化方面显示出更高的准确性。