Arifin Md Shamsul, Wang Wilson, Uddin Mohammad Nasir
Department of Electrical and Computer Engineering, Lakehead University, GC Campus, Barrie, ON L4M 3X9, Canada.
Department of Mechanical and Mechatronics Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada.
Sensors (Basel). 2024 Aug 11;24(16):5186. doi: 10.3390/s24165186.
Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally.
感应电机（IMs）广泛应用于各个工业领域。尽早识别感应电机的缺陷对于防止电机性能下降、提高生产质量和安全性至关重要。这项工作将聚焦于感应电机断条（BRB）故障检测，因为断条故障会在感应电机中产生额外的发热、振动、噪声，甚至火花。本文提出一种改进的经验模态分解（EMD）技术，即 MEMD，用于基于电机电流特征分析的断条故障检测。我们的研究团队开发了一个基于智能传感器的数据采集（DAQ）系统，用于无线采集电流信号。MEMD 需经过几个处理步骤。首先，进行基于相关性的 EMD 分析以选择最具代表性的本征模态函数（IMF）。其次，提出一种自适应窗函数用于频谱运算和分析以检测断条故障。第三，提出一种新的参考函数以解析生成用于故障严重程度诊断的故障指标。所提 MEMD 技术的有效性通过实验得到验证。
