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S-transform and ANFIS for detecting and classifying the vibration signals of induction motor

机译:S变换和ANFIS用于检测和分类感应电动机的振动信号

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摘要

In this paper, a hybrid approach is proposed for detecting and classifying the vibration signal of induction motor. The proposed hybrid technique is the combination of S-transformation algorithm and adaptive neuro fuzzy inference system (ANFIS) method. Here, the proposed hybrid method contains two processes, such as, fault detection and classification process. Initially, the pre-processing is applied in the electric motor vibration signal. In the fault detection process, significant features from vibration signals are extracted through the S-transformation algorithm. Consequently, the ANFIS classification technique is employed to classify the signal into the faulty or the normal. The proposed hybrid technique is implemented in MATLAB working platform. The performance of the proposed hybrid technique is evaluated with five types of faulty vibration signals. The performance of the proposed hybrid method is compared with the existing method such as S-transform-RBFNN and S-transform-FFBNN. Analyze these methods with the help of statistical measures such as, accuracy, sensitivity and specificity value.
机译:本文提出了一种混合方法来检测和分类感应电动机的振动信号。提出的混合技术是S变换算法和自适应神经模糊推理系统(ANFIS)方法的结合。在此,提出的混合方法包含两个过程,例如故障检测和分类过程。最初,预处理被应用到电动机振动信号中。在故障检测过程中,通过S变换算法从振动信号中提取重要特征。因此,使用ANFIS分类技术将信号分类为故障或正常。所提出的混合技术是在MATLAB工作平台上实现的。提出的混合技术的性能通过五种类型的故障振动信号进行评估。将所提出的混合方法的性能与现有方法(例如S-transform-RBFNN和S-transform-FFBNN)进行比较。借助统计方法(如准确性,敏感性和特异性值)分析这些方法。

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