An improved algorithm with feature selection and neural network classification is proposed in this paper to investigate the fault diagnosis problem of rotating machinery. The feature vectors are constructed by extracting the time- and frequency-domain characteristics of the overall machine under multiple operating conditions. To strengthen the fault diagnostic ability, an improved sensitive feature selection algorithm is proposed by improving the distance evaluation (DE) method and reconstructing a low-dimensional sensitive feature sample with selectively chosen parameters from multidimensional feature vectors. The recursive least square backpropagation (RLS-BP) neural network algorithm is used for fault diagnosis by classifying the feature vectors of normal signal and faulty signals. The effectiveness of the proposed method is verified via hardware experiments using wind turbine drivetrain diagnostics simulator (WTDDS) by comparing with conventional feature vector construction methods and neural network algorithm.

中文翻译：

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一种改进的基于敏感特征和RLS-BP神经网络的旋转机械故障诊断方法

为了研究旋转机械的故障诊断问题，本文提出了一种具有特征选择和神经网络分类的改进算法。特征向量是通过提取整个机器在多种操作条件下的时域和频域特征来构建的。为了增强故障诊断能力，提出了一种改进的敏感特征选择算法，该算法通过改进距离评估（DE）方法并从多维特征向量中选择性选择参数重建低维敏感特征样本。递归最小二乘反向传播（RLS-BP）神经网络算法通过对正常信号和故障信号的特征向量进行分类，用于故障诊断。