# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 1 Introduction  
As one of the most significant parts of rotating machines, rolling bearing has a great influence on operating status of mechanical equipment. According to the statistics of mechanical faults, more than 40% of the faults are caused by rolling bearings [1; 2]. Therefore, it is important and meaningful for researchers to do research on fault diagnosis of rolling bearings. In bearing fault diagnosis, an essential role to improve the diagnosis accuracy of bearings is played by feature selection, which aims at selecting a subset from the original set of features according to discrimination capability [3]. Traditional feature selection methods can be divided into three types: filters, wrappers and embedded methods [4]. Filter methods evaluate the quality of features through some feature evaluation criteria and select the top high-ranked features. Based on the advantages of high generality and low computational cost, filter-based feature selection methods are suitable for high-dimensionaldatasets [5; 6]. Although filter-based methods are computationally fast, they usually do not take feature relevance into considerations. Wrapper methods utilize the performance of machine learning model to judge the quality of the feature subset. They usually repeat the two steps, including searching for an optimal feature subset and evaluating the selected features, until a stopping measure is met [5; 6]. The computational speed of wrapper-based methods is slow and the computational complexity is high, because of search strategies which involve sequential selection algorithms like sequential forward selection (SFS), sequential backward selection (SBS) and heuristic search algorithms like genetic algorithms (GAs), particle swarm optimization (PSO), gravitational search algorithm (GSA), and ant colony optimization (ACO). The complementary strengths of both filter and wrapper are exploited by embedded methods. Embedded methods define the feature that has the best ability to differentiate among classes in each stage and take feature selection as part of the training process. Thus, embedded methods are more effective than filter approaches since they involve interaction with the learning algorithm, and they are superior to wrapper methods as they do not need to evaluate the feature subsets iteratively [5]. However, embedded methods have the shortcomings of low computational speed and high computational complexity as well. As mentioned above, feature selection in bearing fault diagnosis is helpful to improve the accuracy of diagnosis and reduce the dimensions of features. [PERSON] [7] combined filter and wrapper methods to select features in bearing fault diagnosis. [PERSON] [8; 9] proposed a supervised Laplacian score (SLS)-based feature selection method. [PERSON] [10] applied an improved wrapper-based feature selection method for bearing fault diagnosis. [PERSON] [11] selected features through an evolutionary Monte Carlo method, which is a wrapper-based method. [PERSON] [12] proposed a hybrid feature selection method which employed a genetic algorithm (GA)-based filter analysis to select optimal features. [PERSON] [13] used the real-valued gravitational search algorithm (RGSA) to optimize the input weights and bias of extreme learning machine (ELM), and the binary GSA (BGSA) was used to select important features from a compound feature set. [PERSON] [14] combined the K-means method with standard deviation to select the most sensitive characteristics. [PERSON] [15] and [PERSON] [16] utilized distance evaluation technique (DET) to select sensitive features. [PERSON] [17] presented a feature selection method involved in feature weight, monotonicity, correlation, and robustness. However, the above methods have some problems that lack considerations for relevance; for example, the computational speed is slow, and the computational complexity is high. Some researchers recently took signal relevance into consideration. [PERSON] [18] selected intrinsic mode functions (IMFs) as features through correlation.