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首页> 外文期刊>IEEE transactions on industrial informatics >Vibration Analysis Based Interturn Fault Diagnosis in Induction Machines
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Vibration Analysis Based Interturn Fault Diagnosis in Induction Machines

机译：基于振动分析的感应电机匝间故障诊断

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摘要

A vibration analysis based interturn fault diagnosis of induction machines is proposed in this paper, using a neural-network-based scheme, constituting of two parts. The first part finds out the optimum network size of the probabilistic neural network (PNN) using the Orthogonal Least Squares Regression algorithm. This judges the size of the PNN, with an effort to reduce the computation. The feature extraction to model the PNN is made meaningful using dual tree complex wavelet transform (DTCWT), which is nearly shift invariant analytical wavelet transform, giving a true representation of the input space. In the second part, preprocessing using principal component analysis is suggested as an effective way to further reduce the dimension of the feature set and size of the PNN without compromising the performance. The sensitivity, specificity, and accuracy show that the vibration signatures capture the fault more effectively (especially by the axial and radial ones), under varying supply-frequency and load conditions. A comparison with traditional discrete wavelet transform proves the applicability of the proposed scheme. A comparative evaluation with feedforward neural network and naïve Bayes scheme brings out the advantage of the proposed optimized DTCWT-PNN based technique over other machine learning approaches.
机译：提出了一种基于振动分析的感应电机匝间故障诊断方法，采用基于神经网络的方案，由两部分组成。第一部分使用正交最小二乘回归算法找出概率神经网络（PNN）的最佳网络大小。这会判断PNN的大小，以减少计算量。使用双树复数小波变换（DTCWT）来进行PNN建模的特征提取变得有意义，该变换几乎是平移不变的解析小波变换，可以真实表示输入空间。在第二部分中，建议使用主成分分析进行预处理是在不影响性能的前提下进一步减小特征集的尺寸和PNN大小的有效方法。灵敏度，特异性和准确性表明，在变化的供电频率和负载条件下，振动信号可以更有效地捕获故障（尤其是轴向和径向振动）。与传统离散小波变换的比较证明了该方案的适用性。通过前馈神经网络和朴素贝叶斯方案进行的比较评估，证明了所提出的基于DTCWT-PNN的优化技术优于其他机器学习方法的优势。

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