Scholarlink

<p>Induction motors (IMs) are prone to diverse faults during operation, and untimely detection can lead to catastrophic failures. To address this issue, this study develops a dual-module intelligent fault diagnosis system integrating the RELAX algorithm with 1D convolutional neural network (1DCNN) and random forest (RF). The RELAX algorithm first suppresses fundamental frequency interference to highlight intrinsic fault features. The RELAX-1DCNN framework realizes efficient fault detection with 99.75% average accuracy, while the RELAX-RF framework achieves precise fault classification with 99.19% accuracy. This integrated system provides a reliable solution for IM fault diagnosis, balancing high efficiency and classification precision.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Intelligent fault diagnosis and classification of induction motors based on Relax-1DCNN and Relax-RF

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月3日

查看全文
  • Xiang Wei,
  • Bo Wu,
  • Yu Wei

摘要

Induction motors (IMs) are prone to diverse faults during operation, and untimely detection can lead to catastrophic failures. To address this issue, this study develops a dual-module intelligent fault diagnosis system integrating the RELAX algorithm with 1D convolutional neural network (1DCNN) and random forest (RF). The RELAX algorithm first suppresses fundamental frequency interference to highlight intrinsic fault features. The RELAX-1DCNN framework realizes efficient fault detection with 99.75% average accuracy, while the RELAX-RF framework achieves precise fault classification with 99.19% accuracy. This integrated system provides a reliable solution for IM fault diagnosis, balancing high efficiency and classification precision.