## 2 Health Condition Monitoring
### Data Processing  
#### 2.2.1 Data Preprocessing  
When the ship is in operation, ship managers and engineers need to identify and analyze a large quantity of real-time information on board. However, data obtained from the MSAE, ship navigation, and environment contain errors caused by measurement uncertainty, manual recording problem, transmission, storage process error, and so forth. Abnormal data can easily lead to misjudgment of MSAE health conditions, leading to wrong decision-making and catastrophic accidents. Therefore, it is necessary to preprocess the ship's data. There are generally three preprocessing methods, as shown in Figure 2, data imputation, outlier detection, and redundant data deletion. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Communication Technology** & **Communication Features** & **Scope of Application** \\ \hline Universal mobile communication network. & Low communication cost and large network bandwidth; small coverage and low network security. & Suitable for inland rivers and offshore ships. \\ \hline Automatic identification system (AIS) & Can directly realize the speed measurement navigation function of ship positioning and monitoring and carry out information broadcasting; high service cost, seriously affected by electromagnetic environment interference, communication distance is limited. & Suitable for inland rivers and offshore ships. \\ \hline Maritime satellite & Can provide data link for ocean-going ships; high cost, low communication bandwidth, maximum speed is only 492 Kbps. & Suitable for global navigation ships. \\ \hline Spread spectrum communication technology & Strong concealment and good anti-interference performance; the system uses a wide frequency band and has a limited data transmission rate and distance. & Suitable for inland rivers and offshore ships. \\ \hline GPS & Accurate positioning and strong anti-interference ability. & Only applicable to ship positioning, unable toachieve communication. \\ \hline BDS & All-weather, all-day, high-precision positioning; short message communication has low cost and high & Suitable for global navigation ships. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Remote data transmission technologies. (1) Data imputation  
Affected by various uncertainties, datasets from MSAE tend to contain a large amount of missing data [19; 20]. The lack of data will lead to the deviation of diagnosis and prediction results, which will affect the decision-making of condition-based maintenance (CBM). Therefore, the imputation method is vital for increasing the quality of the original dataset and improving the decision-making for intelligent O&M. [PERSON] et al. developed a new mixed imputation method to improve data quality [21]. The mixed-method is applied in the primary engine data set from a chemical vessel, and the results show that the average errors are smaller than the original algorithm. By correcting the sensor data from the HCM of MSAE, this mixed imputation algorithm tremendously improves the quality of the original data set. It relieved the conflict that the increasingly popular data-driven methods need complete data set for feature learning in the MSAE. Other imputation methods were applied to wind turbines [22] and cluster monitoring [23]. The effects of different missing data imputation methods were compared using cargo ships' sensor data [24];  
(2) Outliers detection  
Outliers in the data greatly impact the judgment of health conditions, so they need to be filtered out in advance during analysis. [PERSON] et al. presented a data gap analysis method that enables real-time detection of exception data in ships and marine data [25]. The method first detected original data after a series of data pretreatment and then sequentially detected the abnormal data in a relative error interval based on the predicted data obtained from the ship's performance element learning model. [PERSON] et al. employed an abnormal detection scheme that can be used directly in process monitoring or process control.