IEEE Trans Neural Netw Learn Syst. 2014 May;25(5):990-1001. doi: 10.1109/TNNLS.2013.2285552.
Interturn fault diagnosis of induction machines has been discussed using various neural network-based techniques. The main challenge in such methods is the computational complexity due to the huge size of the network, and in pruning a large number of parameters. In this paper, a nearly shift insensitive complex wavelet-based probabilistic neural network (PNN) model, which has only a single parameter to be optimized, is proposed for interturn fault detection. The algorithm constitutes two parts and runs in an iterative way. In the first part, the PNN structure determination has been discussed, which finds out the optimum size of the network using an orthogonal least squares regression algorithm, thereby reducing its size. In the second part, a Bayesian classifier fusion has been recommended as an effective solution for deciding the machine condition. The testing accuracy, sensitivity, and specificity values are highest for the product rule-based fusion scheme, which is obtained under load, supply, and frequency variations. The point of overfitting of PNN is determined, which reduces the size, without compromising the performance. Moreover, a comparative evaluation with traditional discrete wavelet transform-based method is demonstrated for performance evaluation and to appreciate the obtained results.
基于各种神经网络技术的感应电机匝间故障诊断已经得到了讨论。在这些方法中，主要的挑战是由于网络的巨大规模和大量参数的修剪而导致的计算复杂性。本文提出了一种基于近似平移不变复小波的概率神经网络（PNN）模型，该模型仅需优化一个参数，用于匝间故障检测。该算法由两部分组成，并以迭代的方式运行。在第一部分中，讨论了 PNN 结构的确定，使用正交最小二乘回归算法找到了最佳的网络大小，从而减小了网络的大小。在第二部分中，推荐了贝叶斯分类器融合作为一种有效的决策机器状态的方法。在负载、电源和频率变化下，基于乘积规则的融合方案获得了最高的测试准确性、灵敏度和特异性值。确定了 PNN 的过拟合点，在不影响性能的情况下减小了网络的大小。此外，还与传统的基于离散小波变换的方法进行了比较评估，以进行性能评估和欣赏所得到的结果。
