Scholarlink

<p>Among the fault types in five-phase permanent magnet synchronous motor (PMSM) drives, open-phase faults (OPFs), open-switch faults (OSFs), and inter-turn faults (ITFs) are the most common. In addition, they all require prompt detection. These faults typically cause a power imbalance across phases. Accurate diagnosis is crucial for applying an effective fault-tolerant control strategy. Thus, this study proposes a method to construct a health indicator by analyzing residual currents using maximum and minimum functions. The fundamental frequency of the health indicator is extracted and compared against a threshold to detect fault occurrence. A probabilistic neural network (PNN) is then used to identify the fault types and the faulty phase. The effectiveness and reliability of the method are validated through both simulation and experimental results.</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

Fault diagnosis for five-phase PMSM drive using residual current fusion and probabilistic neural network

  • 期刊论文

  • 发表日期：

    2025年6月30日

查看全文
  • Quan Wang,
  • Qiang Tan,
  • Cong Guo,
  • Bing Tian

摘要

Among the fault types in five-phase permanent magnet synchronous motor (PMSM) drives, open-phase faults (OPFs), open-switch faults (OSFs), and inter-turn faults (ITFs) are the most common. In addition, they all require prompt detection. These faults typically cause a power imbalance across phases. Accurate diagnosis is crucial for applying an effective fault-tolerant control strategy. Thus, this study proposes a method to construct a health indicator by analyzing residual currents using maximum and minimum functions. The fundamental frequency of the health indicator is extracted and compared against a threshold to detect fault occurrence. A probabilistic neural network (PNN) is then used to identify the fault types and the faulty phase. The effectiveness and reliability of the method are validated through both simulation and experimental results.