Intelligent industrial production has recently emerged as an important trend for application of the Industrial Internet of Things (IIoT) in edge computing. This study applied remote edge devices and edge servers, preprocessing the signal sensor, through covert data to cloud storage, and loaded the data to propose several deep learning methods to assess the status of aircraft engines in operation, and to classify stages of operational degradation so as to predict the functional remaining lifespan of components. The predicted results are transmitted to a cloud-based server for monitoring and maintenance.

中文翻译：

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基于状态评估的深度学习中基于边缘评估的剩余使用寿命预测

智能工业生产最近已成为工业物联网（IIoT）在边缘计算中应用的重要趋势。这项研究应用了远程边缘设备和边缘服务器，对信号传感器进行了预处理，将数据通过秘密数据存储到云存储中，并加载了数据，以提出几种深度学习方法来评估飞机发动机的运行状态，并对运行退化的各个阶段进行分类。以预测组件的功能剩余寿命。预测结果将传输到基于云的服务器以进行监视和维护。