基于ANN的感应电机故障诊断在定位器间旋转短路和不平衡的供应电压下
Lazar Amat Ellah Noussaiba1, Ferdjouni Abdelaziz1
1Automatics Department, Saad Dahleb University, Soumaa, Blida 09000, Algeria.
ISA transactions
|December 23, 2023
概括
本研究引入了一种人工智能方法,用于检测感应电机中的定子间转换短路 (SITSC) 和不平衡供应电压 (USV). 该系统准确地识别这些故障,防止发动机损坏和错误报警.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 工业自动化 工业自动化
背景情况:
- 感应电机 (IM) 是易受操作压力的关键工业部件.
- 管理系统的故障可能源于诸如定位器间转换短路 (SITSC) 和不平衡供应电压 (USV) 等问题.
- 早期检测故障对于防止灾难性电机故障和最大限度地减少停机时间至关重要.
研究的目的:
- 开发一个高效的诊断系统来检测和区分SITSC和USV故障在IMs.
- 为了估计SITSC故障的百分比.
- 通过将其与SITSC区分开来,避免USV引起的错误报警.
主要方法:
- 使用人工智能 (AI) 工具,特别是人工神经网络 (ANN).
- 实现多层感知神经网络 (MLP-NN) 作为核心故障分类器.
- 通过IM和实验室实验的模拟数学模型创建数据集,用于培训,测试和验证.
主要成果:
- 拟议的诊断系统在检测SITSC和USV方面表现出高灵敏度.
- 该MLP-NN有效地分类了SITSC和USV故障.
- 该系统在现实世界和模拟条件下证明了其良好运行.
结论:
- 由人工智能驱动的诊断系统准确地识别了感应电机中的SITSC和USV.
- 该方法有效地区分不同类型的故障,提高诊断可靠性.
- 这种方法提供了一种可靠的解决方案,可以防止电机损坏并确保运行连续性.
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