在感应电机中使用无重量神经网络检测和识别故障.
1Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar, Pakistan.
The Review of scientific instruments
|February 13, 2024
概括
一个新的实值无重量神经网络分类器增强了感应电机的故障检测. 这种先进的方法提高了单相和三相电机故障识别的准确性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 感应电机的故障检测对于工业可靠性至关重要.
- 现有的无重量神经网络变体在处理实值数据和多类问题方面存在局限性.
- 准确的故障识别确保了运营效率,并防止了灾难性的故障.
研究的目的:
- 引入一个新的多类,多参数实值无重量神经网络 (WNN) 分类器.
- 增强WNN在故障诊断方面的识别和概括能力.
- 应用开发的分类器用于单相和三相感应电机的故障检测和识别.
主要方法:
- 开发一个改进的输入到地址映射策略,用于区分单位.
- 实施有效的内存扩展方案,使用基于相似度量的会员值.
- 使用WNN分类器来分析感应电机的实值输入特征.
主要成果:
- 拟议的WNN分类器在故障识别方面取得了很高的准确性.
- 在单相感应电机故障识别中达到99.6%的准确性.
- 实现了89.25%的准确性,用于三相感应电机故障识别.
结论:
- 新的实值WNN分类器为多类故障检测提供了卓越的性能.
- 该方法显示了与原始WNN变体相比的显著改进.
- 该分类器有效地识别了单相和三相感应电机中的多重故障.
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