模糊推理数值尖端神经P系统用于诱导电机故障诊断
Xiu Yin1, Xiyu Liu1, Minghe Sun2
1Academy of Management Science, Business School, Shandong Normal University, Jinan 250014, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
模糊推理数值尖端神经P系统有效地使用间隔值模糊数字来诊断感应电机故障. 这种方法处理不确定的故障数据,比现有的诊断方法有优势.
科学领域:
- 计算智能是一种计算智能.
- 人工智能的人工智能
- 工程 工程师 工程师 工程师
背景情况:
- 数字尖端神经P系统 (NSN P系统) 是由生物神经元启发的计算模型.
- 目前用于感应电机故障诊断的现有方法在不完整和不确定的数据中扎.
研究的目的:
- 引入模糊推理数值尖端神经P系统 (FRNSN P系统) 以改进诱导电机故障诊断.
- 模拟模糊的生产规则,并执行模糊的推理来识别电机故障.
主要方法:
- 通过将区间值的三角模糊数字集成到NSN P系统中,开发了FRNSN P系统.
- 设计了一个FRNSN P推理算法来处理不确定的故障信息.
- 利用相对偏好关系来评估故障严重程度.
主要成果:
- FRNSN P系统成功地模拟了电机故障的模糊生产规则.
- FRNSN P推理算法有效地诊断了单个和多个感应电机故障.
- 拟议的方法在现有故障诊断技术上显示了优势.
结论:
- FRNSN P系统为感应电机故障诊断提供了一个强大的框架.
- 使用间隔值的三角模糊数字可以提高处理不确定的故障数据的性能.
- 开发的算法为及时的电机维护和维修提供了一个有希望的替代方案.
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