使用多模式时间序列和集体变压器网络电机的故障诊断
Bo Xu1, Huipeng Li2,3, Ruchun Ding1
1School of Physics and Electronic Information, Huanggang Normal University, Huanggang, 438000, China.
Scientific reports
|March 6, 2025
概括
本研究介绍了一种先进的方法,用于使用多式联机数据融合和变压器网络来诊断感应电机故障. 该方法实现了高诊断精度,提高了工业设备的可靠性并降低了维护成本.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 机械工程 机械工程
背景情况:
- 感应电机对于工业运行至关重要,需要有效的故障诊断来防止停机时间和优化维护.
- 当前的诊断方法经常与各种故障类型和复杂的操作环境作斗争.
- 尽量减少停机时间和维护成本对于工业生产的经济效率至关重要.
研究的目的:
- 为各种感应电机故障开发一种新的,高度准确的诊断方法.
- 整合多模式数据和先进的深度学习技术,以加强故障识别.
- 通过精确的故障诊断,提高工业设备的可靠性和效率.
主要方法:
- 收集的多模式数据包括三相电流,振动,设备声音和环境声音.
- 开发了一个集体变压器网络,利用自注意机制来实现动态功能集成.
- 采用混乱的鱼优化算法 (WOA) 来实现网络的超参数优化.
主要成果:
- 拟议的方法在一组多模式的电机测量数据集上实现了99.10%的诊断精度.
- 与单模数据分析和现有最先进的方法相比,表现出卓越的性能.
- 验证了多模数据融合和变压器网络在电机故障诊断中的有效性.
结论:
- 这种新的诊断方法为感应电机故障识别提供了出色的性能和可靠性.
- 多模式数据融合显著提高了诊断准确性,解决了单一来源数据的局限性.
- 该方法为确保连续工业生产和优化维护策略提供了强大的解决方案.
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