感应机器的故障检测和监测使用数据驱动模型漂移检测
Abdiel Ricaldi-Morales1, Camilo Ramírez1, Jorge F Silva1
1Department of Electrical Engineering, University of Chile, Av. Tupper 2007, Santiago 8370451, Chile.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究引入了一种新方法,用于早期检测感应电机中的定子短路故障 (SSCF),使用剩余信息值 (RIV) 原则. 它通过与现有的变速驱动器 (VSD) 集成,使可靠,非侵入性预测性维护成为可能.
科学领域:
- 电气工程 电气工程
- 机器学习 机器学习
- 预测性维护是指预测性维护.
背景情况:
- 静止器短路故障 (SSCF) 是诱导电机故障的主要原因之一.
- 由于标记数据稀缺和工业环境中的传感器安装不切实际,早期检测是很困难的.
- 像光谱分析和余能等传统方法的可靠性有局限性.
研究的目的:
- 提出一个新的,数据驱动的框架,用于SSCF的早期故障检测和诊断.
- 通过使用剩余信息价值 (RIV) 原则来克服现有方法的局限性.
- 开发一种与变速驱动器 (VSD) 无集成的非侵入性解决方案.
主要方法:
- 该框架将故障检测重新定义为电压输入和电流残留之间的独立性的统计测试.
- 一个健康的名义模型 (多层感知器) 使用VDS自动调试程序中的数据进行训练.
- 剩余信息价值 (RIV) 原则用于可靠的故障识别.
主要成果:
- 与传统基线相比,拟议的方法实现了优越的诊断性能.
- 它显示出更高的统计分离性和更低的错误报警率.
- 该系统在大约61ms内检测到1%的初始故障,并准确地识别出故障阶段.
结论:
- 基于RIV的战略为预测性维护提供了一个强大的,非侵入性的,行业准备好的解决方案.
- 它有效地平衡了高速检测与增强的统计可靠性.
- 该框架消除了手动数据收集或复杂物理参数识别的需要.
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