## 2 Health Condition Monitoring
### Data Processing
#### 2.2.2 Feature Processing  
Feature processing is a program to find out the useful features for classification and recognition from many features. This process also could compress the dimension of feature space, that is, to obtain a group of \"few but fine\" classification features with low classification error probability. At present, the feature processing methods in the application field of ships mainly include feature selection and feature extraction. The multi-dimensional data collected from ship complex equipment or systems need to be processed with these methods to obtain main operation characteristics, as shown in the blue block diagram in Figure 3.  
(1) Feature selection  
Feature selection is the process of selecting the relevant features of the data set from the original feature space according to a particular evaluation criterion. The selected feature data are a subset of the original data set, and it is an inclusive relationship [30]. A suitable method must be selected to analyze the original data to adapt to different algorithms' analysis and processing needs. [PERSON] et al. and [PERSON] et al. employed the Pearson correlation analysis method to filter and analyze the data to obtain the optimal subset of equipment failure characteristics and reduced the impact of extreme data on  
Figure 3: Feature selection and feature fusion structure. the analysis results [31; 32]. [PERSON] et al. applied the Max-Relevance and Min-Redundancy method to process and analyze the data characteristics according to the correlation and redundancy [33]. To reduce the negative impact of irrelevant and redundant features between marine equipment status data on the analysis process, [PERSON] et al. used K-means Clustering to select clustering features of ship equipment fault history data, which provides an effective tool for equipment failure analysis [34]. K-means clustering is a simple feature extraction method, which use a \"central object\" (gravitational center) obtained by the mean of each cluster center object. To improve the convergence speed of K-means in large-scale ship data, [PERSON] et al. and [PERSON] et al. proposed objective function and feature weighting to optimize it and apply the Fuzzy C-means clustering algorithm to process ship data [35; 36]. Gaussian mixture model (GMM) algorithm, frequency sequence subtraction (FSS), and other methods were also applied to the selecting of features [9; 37]. These feature selection methods can obtain the feature data set, remove the redundant data while retaining the characteristics of original MSAE data, and simplify the complexity of problem analysis;  
Feature extraction is to transform the original feature space in a certain way through a particular method to obtain new features. The different feature extraction methods of the transformation result can be roughly divided into two categories: dimensionality reduction and decomposition extraction [38]. [PERSON] and [PERSON] used linear discriminant analysis to extract fault features and reduce the dimensionality of the main fault features to improve the accuracy and efficiency of fault recognition [39]. [PERSON] et al. presented principal component analysis (PCA) to analyze various oil parameters to reduce dimensionality, effectively realizing the state detection of marine steam turbines [40]. [PERSON] et al. adapted ensemble empirical mode decomposition partly to decompose and extract the statistical ship propulsion shafting features of intrinsic mode functions and obtain a suitable fault feature vector set for corresponding analysis and diagnosis [41]. This is the type of data decomposition method. To improve the data analysis ability of the feature extraction method, some researchers applied the kernel function to optimize the extraction method and obtained comprehensive algorithms such as kernel independent component analysis (KICA) and kernel principal component analysis (KPCA) [42; 43].