Tang Yuyao, Yang Yapeng, Zhao Xiaoyu, Lv Qi, He Jiapeng, Zhang Zhiqiang
China Institute for Radiation Protection, Taiyuan 030006, China.
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
Sensors (Basel). 2025 Aug 20;25(16):5175. doi: 10.3390/s25165175.
In this paper, we propose an ensemble approach for the intelligent fault diagnosis of machinery, which consists of six feature selection methods and classifiers. In the proposed approach, six filters, based on distinct metrics, are utilized. Each filter is combined with an improved sparse representation classifier (ISRC) to form a base model, in which the ISRC is an improved version of a sparse representation classifier and has the advantages of high classification accuracy and being less time consuming than the unimproved version. For each base model, the filter selects a feature subset that is used to train and test the ISRC, where the two hyper-parameters involved in the filter and ISRC are optimized by the binary particle swarm optimization algorithm. The outputs of six base models are aggregated through the cumulative reconstruction residual (CRR), where the CRR is devised to replace the commonly used voting strategy. The effectiveness of the proposed method is verified on six mechanical datasets involving information about bearings and gears. In particular, we conduct a detailed comparison between CRR and voting and carry out an intensive exploration into the question of why CRR is superior to voting in the ensemble model.
在本文中，我们提出了一种用于机械智能故障诊断的集成方法，该方法由六种特征选择方法和分类器组成。在所提出的方法中，使用了基于不同度量的六种滤波器。每个滤波器与一个改进的稀疏表示分类器（ISRC）相结合，形成一个基础模型，其中ISRC是稀疏表示分类器的改进版本，具有分类准确率高且比未改进版本耗时少的优点。对于每个基础模型，滤波器选择一个特征子集，用于训练和测试ISRC，其中滤波器和ISRC中涉及的两个超参数通过二进制粒子群优化算法进行优化。六个基础模型的输出通过累积重构残差（CRR）进行聚合，其中CRR旨在取代常用的投票策略。在所提出的方法的有效性在六个涉及轴承和齿轮信息的机械数据集上得到了验证。特别是，我们对CRR和投票进行了详细比较，并深入探讨了在集成模型中CRR优于投票的原因。
