## 1 Introduction  
Rolling bearings, an important part of rotating machinery, are complex systems of many components [1]. The components interacting with each other easily lead to composite faults, meaning that multiple parts are failing [2]. Composite fault signals contain the causal relationships among components, and such relationships show themselves on the feature scale of the signals [3]. In this case, causal relationships exist also in the features extracted from composite fault signals, which makes it difficult to identify fault types. As a key step for fault diagnosis, the feature selection aims to select the most effective feature subset from the complex fault signals [4]. Due to the large amount of redundant information in the composite fault signal, selecting the optimal feature subset is essential for the stability and reliability of mechanical systems [5]. Thus, effective feature selection can not only reduce the space dimension of features but also improve the accuracy of fault diagnosis. Feature selection has been widely used in the fields of fault diagnosis, remote sensing classification of multiple crops and predictive maintenance of automobiles [6]. In the field of feature selection, the most classical method is univariate feature selection [7]. Scholars draw upon Pearson product-moment correlation coefficients (PCCs), Fisher score, Laplacian score and Gini index to calculate the distance between features and type labels [8; 9; 10; 11]. [PERSON] et al., proposed a hybrid metric feature selection method to solve the problem of sample misclassification, which is caused by ignoring the distribution of data [12]. Based on the decision tree algorithm and the enhanced Gini index, [PERSON] et al. proposed an intelligent DDoS attack detection model to select features [13]. This model reduces the data dimensionality and avoids the overfitting problem. The growth of artificial intelligence hasmade machine learning techniques like Random Forest, Simple Bayes and Support Vector Machines quite popular for feature selection [14]. These methods focus on selecting the features sensitive to type patterns but ignore the hidden interactions among features. Other scholars find that approaches introducing frameworks of causal relationship can add to the interpretability of the results, such as those based on the Markov Blanket and information theory [15; 16; 17]. But these approaches only consider the causality between features and type patterns and neglect the causal structural information among composite fault features. A key issue here is whether effective features can be selected from the structural information of the causal relationships between the features. Given the latest progress in this field, we focus on the criteria of feature selections. In contrast to the vast majority of studies, we constructed the feature selection mechanism by exploiting the potential correlations between features. That is, we propose a complex network model-based feature selection method for composite faults in rolling bearings. Complex network is a mathematical approach to express relational data [18]. It can directly express the various correlations within the rolling bearing system. For example, [PERSON] et al. used the complex network model to express fault samples and their relationships [19]. Fault samples are categorized based on the similarity measure of the complex network. [PERSON] et al. established a network model of fault data based on the components of the fault signals and their relationships [20]. Such an application can visualize the correlation relationship between the components in the bearing system and provide important support for the identification and treatment of composite faults. Therefore, we use complex networks model to express the causal relationship among composite fault features in the rolling bearing system and draw upon the characteristics of complex networks to select composite fault features.