Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function. Due to the requirements of system safety and reliability, the correct diagnosis or prognosis of abnormal condition plays a vital role in the maintenance of industrial systems. It is expected that new requirements in regard to autonomous ships will push suppliers of maritime equipment to provide more insight into the conditions of their systems. One of the stated challenges with these systems is having enough run-to-failure examples to build accurate-enough prognostic models. Due to the scarcity of enough reliable data, transfer learning is established as a successful approach to improve and reduce the need to labelled examples. Transfer learning has shown excellent capabilities in image classification problems. Little work has been done to explore and exploit the use of transfer learning in prognostics. In this paper, various deep learning models are used to predict the remaining useful life (RUL) of air compressors. Here, transfer learning is applied by building a separate prognostics model trained on turbofan engines. It has been found that several of the explored transfer learning architectures were able to improve the predictions on air compressors. The research results suggest transfer learning as a promising research field towards more accurate and reliable prognostics.

中文翻译：

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船用空气压缩机的预测和健康管理（PHM）的转移学习

Prognostics是一门工程学科，专注于预测系统或组件不再执行其预期功能的时间。由于系统安全性和可靠性的要求，对异常状况的正确诊断或预后在维护工业系统中起着至关重要的作用。预计有关自动驾驶船的新要求将推动海上设备供应商提供有关其系统状况的更多见解。这些系统面临的挑战之一是要有足够的运行失败示例来建立足够准确的预测模型。由于缺乏足够可靠的数据，转移学习被认为是改善和减少标记示例需求的成功方法。转移学习在图像分类问题中显示出卓越的能力。在预测学中探索和利用转移学习的工作还很少。在本文中，各种深度学习模型用于预测空气压缩机的剩余使用寿命（RUL）。在这里，通过建立在涡扇发动机上训练的单独的预测模型来应用转移学习。已经发现，探索的几种转移学习体系结构能够改善空气压缩机的预测。研究结果表明，将转移学习作为朝着更准确和可靠的预测方法发展的有前途的研究领域。各种深度学习模型用于预测空气压缩机的剩余使用寿命（RUL）。在这里，通过建立在涡扇发动机上训练的单独的预测模型来应用转移学习。已经发现，探索的几种转移学习体系结构能够改善空气压缩机的预测。研究结果表明，将转移学习作为朝着更准确和可靠的预测方法发展的有前途的研究领域。各种深度学习模型用于预测空气压缩机的剩余使用寿命（RUL）。在这里，通过建立在涡扇发动机上训练的单独的预测模型来应用转移学习。已经发现，探索的几种转移学习体系结构能够改善空气压缩机的预测。研究结果表明，将转移学习作为朝着更准确和可靠的预测方法发展的有前途的研究领域。