基于一致性自组织模型的剩余使用寿命预测迁移学习
Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models
摘要：
传统的开发机器性能的范例通常依赖于在设备部署之前在受控条件下从实验中获得的数据的概括。以这种方式检测或预测故障并估计机器健康假设未来的现场数据将具有与实验数据非常相似的分布。然而，许多复杂的机器在动态环境条件下运行，并以许多不同的方式使用。这使得收集全面的数据非常具有挑战性，部署前数据和部署后数据遵循非常相似的分布的假设不太可能成立。迁移学习（TL）是指将在一个环境（源域）中学习的知识转移到另一个环境（目标域）的方法。在这项工作中，我们提出了一个TL方法预测剩余使用寿命（RUL）的设备，假设标签只适用于源域，而不是目标域。该设置对应于将部署前执行的有限数量的运行至故障实验概括为使用来自在多个新操作条件下使用并经历以前未见过的故障的已部署设备的数据进行预测。我们采用偏差检测方法，共识自组织模型（COSMO），创建可转移的功能，建立RUL回归模型。这些功能捕捉目标设备与其同类设备相比的差异。使用NASA涡扇发动机退化模拟数据集证明了所提出的TL方法的有效性。当目标域比源域复杂时，使用COSMO可转移特征的模型在预测RUL上表现出比其他方法更好的性能。
英文摘要：
The traditional paradigm for developing machine prognostics usually relies on generalization from data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this way assumes that future field data will have a very similar distribution to the experiment data. However, many complex machines operate under dynamic environmental conditions and are used in many different ways. This makes collecting comprehensive data very challenging, and the assumption that pre-deployment data and post-deployment data follow very similar distributions is unlikely to hold. Transfer Learning (TL) refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain). In this work, we present a TL method for predicting Remaining Useful Life (RUL) of equipment, under the assumption that labels are available only for the source domain and not the target domain. This setting corresponds to generalizing from a limited number of run-to-failure experiments performed prior to deployment into making prognostics with data coming from deployed equipment that is being used under multiple new operating conditions and experiencing previously unseen faults. We employ a deviation detection method, Consensus Self-Organizing Models (COSMO), to create transferable features for building the RUL regression model. These features capture how different target equipment is in comparison to its peers. The efficiency of the proposed TL method is demonstrated using the NASA Turbofan Engine Degradation Simulation Data Set. Models using the COSMO transferable features show better performance than other methods on predicting RUL when the target domain is more complex than the source domain.
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