在模拟电路中使用集群智能进行故障诊断
Nadia Nedjah1, Jalber Dinelli Luna Galindo1, Luiza de Macedo Mourelle2
1Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rio de Janeiro 20550-900, Brazil.
这项研究介绍了对模拟电路故障诊断的群集智能,使用蝙蝠算法 (BA) 实现更高的准确性和速度,而不是粒子群集优化 (PSO). 该方法有效地识别出故障组件,而不需要广泛的培训数据.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 由于连续响应,模拟电路的传统故障模型可能无法捕捉所有潜在的故障.
- 准确的模拟电路故障诊断需要识别故障元件,它们的值,并考虑容忍约束.
研究的目的:
- 提出并实施使用集群智能对模拟电路的故障诊断方法.
- 为了比较粒子集群优化 (PSO) 和蝙蝠算法 (BA) 对此任务的有效性.
主要方法:
- 使用非线性电路方程来计算故障诊断的参数.
- 采用粒子群集优化 (PSO) 和蝙蝠算法 (BA) 作为群集智能技术.
- 使用Tow-Thomas Biquad过器和Butterworth过器的案例研究来评估性能.
主要成果:
- 蝙蝠算法 (BA) 与粒子优化 (PSO) (93.9%) 相比,实现了更高的平均精度 (95.5%).
- BA在执行时间方面表现出卓越的性能,无故障电路的性能降低了7.95%,故障电路的性能降低了8.12%.
- 提出的方法成功地确定或减少了潜在故障组件的数量.
结论:
- 群体智能,特别是蝙蝠算法,为模拟电路故障诊断提供了一种有效的方法.
- 蝙蝠算法为机器学习方法提供了可比的准确性,而不需要数据集或培训.
- 这种方法通过考虑连续电路响应和组件公差来增强故障识别.
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