College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumchi 830017, China.
Sensors (Basel). 2023 May 16;23(10):4792. doi: 10.3390/s23104792.
Gearboxes are utilized in practically all complicated machinery equipment because they have great transmission accuracy and load capacities, so their failure frequently results in significant financial losses. The classification of high-dimensional data remains a difficult topic despite the fact that numerous data-driven intelligent diagnosis approaches have been suggested and employed for compound fault diagnosis in recent years with successful outcomes. In order to achieve the best diagnostic performance as the ultimate objective, a feature selection and fault decoupling framework is proposed in this paper. That is based on multi-label K-nearest neighbors (ML-kNN) as classifiers and can automatically determine the optimal subset from the original high-dimensional feature set. The proposed feature selection method is a hybrid framework that can be divided into three stages. The Fisher score, information gain, and Pearson's correlation coefficient are three filter models that are used in the first stage to pre-rank candidate features. In the second stage, a weighting scheme based on the weighted average method is proposed to fuse the pre-ranking results obtained in the first stage and optimize the weights using a genetic algorithm to re-rank the features. The optimal subset is automatically and iteratively found in the third stage using three heuristic strategies, including binary search, sequential forward search, and sequential backward search. The method takes into account the consideration of feature irrelevance, redundancy and inter-feature interaction in the selection process, and the selected optimal subsets have better diagnostic performance. In two gearbox compound fault datasets, ML-kNN performs exceptionally well using the optimal subset with subset accuracy of 96.22% and 100%. The experimental findings demonstrate the effectiveness of the proposed method in predicting various labels for compound fault samples to identify and decouple compound faults. The proposed method performs better in terms of classification accuracy and optimal subset dimensionality when compared to other existing methods.
变速箱几乎在所有复杂的机械设备中都有应用，因为它们具有很高的传动精度和承载能力，所以它们的故障经常导致重大的经济损失。尽管近年来已经提出并采用了许多基于数据驱动的智能诊断方法来进行复合故障诊断，并取得了成功的结果，但高维数据的分类仍然是一个难题。为了达到最佳的诊断性能作为最终目标，本文提出了一种特征选择和故障解耦框架。该框架基于多标签 K-最近邻（ML-kNN）作为分类器，可以自动从原始高维特征集中确定最佳子集。所提出的特征选择方法是一个混合框架，可以分为三个阶段。Fisher 得分、信息增益和 Pearson 相关系数是三个过滤器模型，用于在第一阶段对候选特征进行预排序。在第二阶段，提出了一种基于加权平均方法的加权方案，用于融合第一阶段得到的预排序结果，并使用遗传算法优化权重，重新对特征进行排序。在第三阶段，使用三种启发式策略（包括二分搜索、序贯前向搜索和序贯后向搜索）自动和迭代地找到最优子集。该方法在选择过程中考虑了特征无关性、冗余性和特征间相互作用的考虑因素，选择的最优子集具有更好的诊断性能。在两个齿轮箱复合故障数据集上，使用最优子集，ML-kNN 的性能非常出色，子集准确率分别为 96.22%和 100%。实验结果表明，该方法在预测复合故障样本的各种标签以识别和解耦复合故障方面是有效的。与其他现有方法相比，该方法在分类准确性和最优子集维度方面表现更好。
