在BJT放大器电路中基于AI的故障检测的理论研究和实践验证
Kyrillos K Selim1, Ahmed Alaa2, Ola Hassan2
1Department of Electronics Technology, Faculty of Technology and Education, Helwan University, Cairo, 11795, Egypt.
Scientific reports
|December 3, 2025
概括
这项研究引入了人工智能驱动的模拟电路故障检测方法,达到98.7%的成功率. 该方法提高了灾难性和参数性故障的诊断精度,提高了电子系统的可靠性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 电路设计 电路设计
背景情况:
- 模拟电路对于电子系统至关重要,但容易发生灾难性和参数性故障.
- 传统的故障诊断方法对于复杂的电路和微妙的故障是不够的.
- 人工智能 (AI) 提供了用于故障检测的数据分析和模式识别的增强功能.
研究的目的:
- 开发和验证基于人工智能的系统,用于准确的模拟电路故障诊断.
- 调查机器学习在检测各种故障模式方面的有效性.
- 将分析模型与机器学习结果相关联,以获得更深入的物理洞察力.
主要方法:
- 研究了一种常见的发射器电路直流配置,其中有15个正常和故障模式.
- 使用模拟和实验性读数创建了一个混合数据集.
- 监督机器学习,特别是随机森林分类器,用于故障检测.
主要成果:
- 人工智能系统在故障检测方面取得了98.7%的成功率.
- 随机森林分类器有效地识别了各种故障条件,包括电阻变化,内部电阻和温度波动.
- 一个链接的分析模型提供了对断层模式和特征重要性物理学的见解.
结论:
- 人工智能,特别是随机森林,显著提高了模拟电路故障检测的准确性和速度.
- 开发的系统在识别不同类型的故障方面表现出强的性能.
- 将分析建模与机器学习相结合,为电路故障分析提供了一个全面的方法.
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