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增强的双卷积神经网络模型使用可解释的人工智能对工业4.0的故障优先级进行解释.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 预测性维护是指预测性维护.

背景情况:

  • 人工智能 (AI) 越来越多地被整合到企业安全中,以预测资产状态和减少维修时间.
  • 目前的系统需要改进自然语言交互,以便运营商能够有效地使用预测性维护功能.
  • 将物理修复剂与计算机化管理系统集成,需要先进的人机交互模型.

研究的目的:

  • 开发一种用于与人工智能驱动的预测维护系统进行自然语言交互的新技术.
  • 使用模糊逻辑增强故障优先级,提高口语理解.
  • 为优化预测模型性能和准确性提出一个算法 (DSADRRFP).

主要方法:

  • 利用双重神经网络卷积模型进行设备数据分析.
  • 基于潜在的损害或费用,实现了基于潜在的损害或费用的故障优先级的模糊逻辑技术.
  • 开发了一种以对话为导向的设计,用于持续学习和语言和交互模型的改进.
  • 提出了数据集 (DS) 与亚当 (AD) 优化器,回归 (RR) 和特征映射 (FP) 算法 (DSADRRFP).

主要成果:

  • 拟议的DSADRRFP算法旨在通过利用组件好处来提高预测模型的性能和精度.
  • 模糊逻辑有效地根据其影响对故障进行排名,有助于确定优先级.
  • 对自然语言交互方法的成功而言,口语理解的持续改进至关重要.

结论:

  • 与物理修复剂集成的AI系统为企业安全和预测性维护提供了显著的好处.
  • 人工智能系统需要精炼才能有效地提取操作系统数据,并与用户自然互动.
  • 持续更新和使用公共培训套件对于保持AI模型的准确性和相关性至关重要.