测量不确定性对故障诊断系统的影响:对感应电机电气故障的案例研究
Simone Mari1, Giovanni Bucci1, Fabrizio Ciancetta1
1Dipartimento di Ingegneria Industriale e dell'Informazione e di Economia, Università dell'Aquila, 67100 L'Aquila, Italy.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
本研究介绍了使用人工神经网络 (ANN) 和振动分析对异步电机的可靠电气故障诊断系统. 纳入测量不确定性可以提高诊断准确度,减少停机时间和维护成本.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 分类系统对于预测性维护和故障诊断至关重要.
- 机器学习模型中的高错误率可能会导致重大风险,例如虚假报警导致不必要的停机时间.
- 当前的系统往往错误地分类故障,而不是表明无法分类.
研究的目的:
- 为异步电机开发更可靠的电气故障诊断系统.
- 通过纳入测量不确定性来减少错误分类.
- 提高运动功能障碍诊断的准确性和预防性.
主要方法:
- 使用人工神经网络 (ANN) 模型训练了振动测量.
- 通过ML模型实现不确定性传播,以建立置信区间.
- 分析振动数据以检测和定位电机故障.
主要成果:
- 振动分析有效地检测和定位异步运动故障.
- 拟议的系统通过整合测量不确定性来证明可靠性增加.
- 信心带有助于区分可分类和不可分类的输入,最大限度地减少错误分类.
结论:
- 开发的系统提高了异步电机电气故障诊断的可靠性.
- 纳入测量不确定性导致更准确和预防性维护决策.
- 这种方法有助于减少运营停机时间和相关的维护成本.
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