The accurate localization of the rolling element failure is very important to ensure the reliability of rotating machinery. This paper proposes an efficient and anti-noise fault diagnosis model for rolling elements. The proposed model is composed of feature extraction, feature selection and fault classification. Feature extraction is composed of signal processing and signal noise reduction. Signal processing is carried out by local mean decomposition (LMD), and signal noise reduction is performed by product function (PF) selection and wavelet packet decomposition (WPD). Through the steps of signal noise reduction, high-frequency noise can be effectively removed, and the fault information hidden under the noise can be extracted. To further improve the effectiveness of the diagnostic model, an improved binary particle swarm optimization (IBPSO) is proposed to find the most important features from the feature space. In IBPSO, cycling time-varying inertia weight is introduced to balance exploitation and exploration and improve the capability to escape from local solutions, and crossover and mutation operations are also introduced to improve exploration and exploitation capabilities, respectively. The main contributions of this research are briefly described as follows: (1) The feature extraction process applied in this research can effectively remove noise and establish a high-accuracy feature set. (2) The proposed feature selection algorithm has higher accuracy than the other state-of-the-art feature selection algorithms. (3) In a strong noise environment, the proposed rolling element fault diagnosis model is compared with the state-of-the-art fault diagnosis model in terms of classification accuracy. Experimental results show that the model can maintain high classification accuracy in a strong noise environment. Therefore, it can be proved that the fault diagnosis model proposed in this paper can be effectively applied to the fault diagnosis of rotating machinery.

中文翻译：

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使用改进的二元粒子群优化算法进行特征选择任务的滚动元件故障定位

滚动体故障的准确定位对于保证旋转机械的可靠性非常重要。本文提出了一种高效、抗噪声的滚动体故障诊断模型。所提出的模型由特征提取、特征选择和故障分类组成。特征提取由信号处理和信号降噪组成。信号处理采用局部均值分解（LMD），信号降噪采用乘积函数（PF）选择和小波包分解（WPD）。通过信号降噪的步骤，可以有效去除高频噪声，提取隐藏在噪声下的故障信息。为了进一步提高诊断模型的有效性，提出了一种改进的二元粒子群优化（IBPSO）来从特征空间中找到最重要的特征。在 IBPSO 中，引入了循环时变惯性权重来平衡开发和探索，提高逃避局部解的能力，还引入了交叉和变异操作，分别提高了探索和开发能力。本研究的主要贡献简述如下： (1) 本研究采用的特征提取过程可以有效去除噪声，建立高精度的特征集。(2) 所提出的特征选择算法比其他最先进的特征选择算法具有更高的精度。(3)在强噪声环境中，在分类精度方面，将所提出的滚动元件故障诊断模型与最先进的故障诊断模型进行了比较。实验结果表明，该模型在强噪声环境下仍能保持较高的分类准确率。因此，可以证明本文提出的故障诊断模型可以有效地应用于旋转机械的故障诊断。