基于PCA-ISSA-PNNN的永久磁铁同步电机脱磁故障诊断
Yinquan Yu1,2, Yang Li3,4, Dequan Zeng1,2
1Institute of Precision Machining and Intelligent Equipment Manufacturing, Key Laboratory of Conveyance and Equipment of Ministry of Education, East China Jiaotong University, Nanchang, 330013, China.
这项研究引入了一种用于诊断永久磁同步电机 (PMSM) 脱磁故障的新方法. 拟议的技术结合了主要组件分析 (PCA),改进的搜索算法 (ISSA) 和概率神经网络 (PNN) 进行准确的故障检测.
科学领域:
- 电气工程 电气工程
- 机器学习 机器学习
- 错误诊断 错误诊断 错误诊断 是一个
背景情况:
- 永久磁铁同步电机 (PMSM) 容易发生脱磁故障,影响其性能和可靠性.
- 准确和高效的故障诊断对于保持PMSM的运行完整性至关重要.
研究的目的:
- 为PMSM提出一种新的去磁化故障诊断方法.
- 通过结合PCA,ISSA和PNN来提高故障诊断的准确性和效率.
主要方法:
- 使用主要组件分析 (PCA) 来从相流中提取关键特征.
- 改进的搜索算法 (ISSA) 用于优化概率神经网络 (PNN) 的平滑系数.
- 一个结合PCA-ISSA-PNN模型被开发并使用实验数据进行验证.
主要成果:
- 拟议的PCA-ISSA-PNN模型实现了95.83%的故障诊断准确度.
- 与传统PNN,PCA-PNN,PCA-GA-PNN和PCA-DA-PNN相比,该方法显示出明显改善的故障诊断指数.
- 与PCA-GTO-PNN,PCA-AHA-PNN和PCA-SSA-PNN相比,观察到更高的优化性能.
结论:
- 该PCA-ISSA-PNN方法提供了一个高度准确和高效的解决方案,用于诊断PMSM的脱磁故障.
- 集成PCA和ISSA优化的PNN为先进的电机故障检测提供了一个强大的框架.
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