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首页> 外文会议>International Conference on Manufacturing Science and Technology >Intelligent Diagnostic of Induction Machine for Faults Detection and Classification Using Wavelet and Fuzzy inference
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Intelligent Diagnostic of Induction Machine for Faults Detection and Classification Using Wavelet and Fuzzy inference

机译：使用小波和模糊推理的故障检测和分类智能诊断

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An intelligent diagnostic method based on 3-D plot continuous wavelet transform (3-D plot CWT) and fuzzy inference system is presented to investigate the detectability and classification of rotor broken bars faults in induction machine (IM) and to overcome the limitation of classical Fourier Transform (FT). This approach is successfully used with Motor Current Signature Analysis (MCSA) and suitable developed model of IM in healthy and faulty mode using Matlab environment. As first step we performed new results using 3-D plot CWT to extract the discriminating features. The features extracted from the wavelet transformed signal are the second most predominant frequency, the time range at which it occurs and the corresponding wavelet coefficients. Then as second and last step a fuzzy Inference system is designed and implemented using Matlab software with these three features extracted from the wavelet transformed signal as inputs and generates an output that classifies the fault and no fault conditions. It is observed that the results are satisfactory.
机译：提出了一种基于3-D曲线连续小波变换（3-D绘图CWT）和模糊推理系统的智能诊断方法，研究了在感应机（IM）中转子断路器故障的可检测性和分类，并克服了经典的限制傅里叶变换（FT）。这种方法与电机电流签名分析（MCSA）和使用Matlab环境的健康和故障模式的合适开发模型合适地使用。作为第一步，我们使用3-D绘图CWT进行了新的结果以提取辨别特征。从小波变换信号中提取的特征是第二最主要频率，其发生的时间范围和相应的小波系数。然后，作为第二和最后一步，使用MATLAB软件设计和实现模糊推理系统，这三个特征从小波变换信号提取为输入，并生成分类故障和没有故障条件的输出。观察结果是令人满意的。

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