## 2 Health Condition Monitoring
### Data Processing
#### 2.2.3 Data Fusion  
Due to the complex characteristics of ship machinery, a single set of data cannot fully express the overall operating condition. For example, the ship's navigation status is affected by fuel conditions, equipment working status, navigation area, weather conditions, and ship fouling [45]. Data fusion is the use of a certain algorithm to fuse multiple pieces of information from a single sensor or information provided by different types of sensors into a new and more expressive evaluation criterion. According to the characteristics of ship data, the fusion method can be roughly divided into three categories, namely data-level, feature-level, and decision-level, as shown in Figure 3.  
The data-level fusion operation object is usually the measured data. [PERSON] et al. applied support vector machines (SVM) and various evaluation functions to analyze the data and realize the ship's propulsion system's fault detection and health management [46]. [PERSON] et al. used the BP neural network method to realize the fault diagnosis of the turbocharger [47]. The feature-level fusion was oriented to feature fusion after the selection of monitoring objects. [PERSON] et al. applied the wavelet neural network to analyze the characteristic data extracted by Fourier and other methods and integrated the characteristic data of various equipment states, which further improves the accuracy of the actual fault state diagnosis of the ship [48]. For different types of ship monitoring data such as temperature, pressure, [PERSON] et al. apply the normalization method to reduce the amplitude difference between the data, then combined PCA and BP neural networks to identify diesel engine failure modes [49]. The decision-level fusion is the fusion of data-oriented diagnosis and analysis results. [PERSON] et al. proposed ER rules to integrate different fault diagnosis models of ship main engines, further established the primary engine wear fault diagnosis model, and improved the robustness and fault tolerance [50]. To ensure the navigation safety onboard, ship propulsion system needs to complete the state monitoring of the main engine, gearbox, shafting, and propeller. The main engine and gearbox are prone to failure and FD is necessary. The life of the main engine directly affects the ship's health management and needs failure prognostics. The decision fusion of the monitoring, diagnosis, and prediction results of the whole system is helpful to obtain specific O&M decisions. The data of the ship mechanical system come from different kinds of sensors with huge intensity differences, such as marine diesel engine exhaust gas temperature (More than 300 \({}^{\circ}\)C) and lubricating oil pressure (0.3 MPa). Before data fusion, we need to normalize the data by scaling or transforming features to the same range to ensure that each data will have an equal contribution. The data are rescaled from different ranges to a predetermined range, and the original data distribution can be retained or not. Many normalization methods have been utilized in the research areas such as maritime accident data normalization [51], ship mechanical equipment data normalization [52]. Various types of data have different amounts of information, so the weight problem needs to be considered in the normalization process. The variable sorting for normalization was utilized to solve this problem in recent years in which considered the random consensus strategy to estimate the weights before implementing normalizations [53].\" A novel Feature Wise Normalization approach was employed for the effective normalization of data which a unified solution is presented to address the shortcomings of traditional data normalization [54]. The general solution was formed based on a normalization unit established by various methods. This method could also be applied to marine field. The current feature processing methods are mainly aimed at static data, and there are few applications for dynamic data processing.