标题： 基于LSTM和随机森林的设备故障预测模型 显示英文标题

标题： Device Fault Prediction Model based on LSTM and Random Forest

摘要： 电力设备的质量构成了大型电网安全的物质基础。 确保进入电网的设备质量是物资管理的核心任务。 目前，来料检验包括抽样计划的生成、抽样、封样、样品传递和测试。 由于缺乏全面的控制系统和有效的控制措施，无法事后追溯业务操作的执行过程。 这种无法追溯的情况妨碍了测试问题的调查和风险控制，因为它缺乏有效的数据支持。 此外，基于抽样操作标准的测试过程中关键参数的原始记录信息，尚未被有效利用。 为了解决这些问题，我们基于物联网（IoT）对典型物料检验过程中的关键监控技术进行了研究，并分析了检验结果中的关键参数。 为了完成上述任务，本文研究了基于关键检验参数的材料设备质量预测中长短期记忆（LSTM）算法的应用。 总之，本文旨在为公司内部的各种业务流程，包括物资采购、工程建设和设备运行，提供专业可靠的质量数据支持。 显示英文摘要

摘要： The quality of power grid equipment forms the material foundation for the safety of the large power grid. Ensuring the quality of equipment entering the grid is a core task in material management. Currently, the inspection of incoming materials involves the generation of sampling plans, sampling, sealing, sample delivery, and testing. Due to the lack of a comprehensive control system and effective control measures, it is not possible to trace the execution process of business operations afterward. This inability to trace hampers the investigation of testing issues and risk control, as it lacks effective data support. Additionally, a significant amount of original record information for key parameters in the testing process, which is based on sampling operation standards, has not been effectively utilized. To address these issues, we conduct researches on key monitoring technologies in the typical material inspection process based on the Internet of Things (IoT) and analyze the key parameters in inspection results. For purpose of complete the above tasks, this paper investigates the use of Long Short-Term Memory (LSTM) algorithms for quality prediction in material equipment based on key inspection parameters. In summary, this paper aims to provide professional and reliable quality data support for various business processes within the company, including material procurement, engineering construction, and equipment operation.