在变速箱类型负载中用于故障严重性诊断的电扭矩:基于Poincaré图和统计特征的ML方法
Mariela Cerrada1, René-Vinicio Sánchez2, Luis-Renato Ortega3
1Research Faculty, Universidad Estatal de Milagro, 091050, Milagro, Ecuador. mcerradal@unemi.edu.ec.
Scientific reports
|November 17, 2025
概括
电力扭矩监测有效地检测到工业机器中的变速箱牙损伤,为振动分析提供了具有成本效益的替代方案. 这种方法在诊断故障方面实现了高精度,增强了基于条件的维护策略.
科学领域:
- 机械工程 机械工程
- 电气工程 电气工程
- 机器学习 机器学习
背景情况:
- 传统的状态监测依赖于机械振动信号,但在工业环境中面临成本和环境挑战.
- 加速度计性能下降和传感器故障是恶劣工业环境中常见的问题.
- 开发基于条件的维护的替代信号对于可靠的旋转机械诊断至关重要.
研究的目的:
- 为了研究电扭矩作为机械负载故障的敏感指标在三相感应电机.
- 评估电扭矩分析用于诊断变速箱牙断裂的有效性.
- 为了比较电扭矩的诊断性能与传统的振动监测.
主要方法:
- 对电扭矩信号的频率分析,以评估对故障严重性的敏感性.
- 机器学习分类器的应用,特别是随机森林和KNN,用于监督故障诊断.
- 使用统计和波因卡雷图 (PP) 的特征进行分类.
主要成果:
- 电扭矩对各种故障严重程度的变速箱牙损伤具有很高的敏感性.
- 使用统计特征的机器学习模型在故障诊断中实现了100%的准确性和精度.
- 波因卡雷图形特征导致了98.15%的准确性和98.40%的精度的最大性能.
结论:
- 电扭矩是一种可行的和敏感的信号,用于诊断感应电机中的变速箱牙断裂.
- 拟议的机器学习方法表明与振动分析具有相似或优越的性能.
- 电扭矩监测为基于工业条件的维护提供了一个有希望的,具有成本效益的替代方案.
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