## 2 Related Work  
### Warships Scheduled Maintenance Cost Prediction  
The category of warships maintenance is divided into condition-based preventive maintenance, temporary emergency maintenance and scheduled maintenance. The warships scheduled maintenance can also be divided into different grades according to their scope and depth, such as third-grade maintenance, second-grade, and first-grade maintenance. Depending on the mission of the warships, the structure of warships' scheduled maintenance during their whole life cycle varies. The structural diagram of a general warship's scheduled maintenance is shown in Figure 1. During the service period, the warship will undergo several rounds of scheduled maintenance, and the times of scheduled maintenance occur differently for different grades, such as the highest grade of first-grade maintenance is often performed only once in the middle of its commissioning. The higher the maintenance grade, the less time occurs, and the greater the scope and depth of the grade maintenance. The timing of the different scheduled maintenance is relatively fixed, and their corresponding repair times are also determined. The WSM is a massive and complicated project that involves numerous different systems of the warship, such as the piping system, power system, etc. Moreover, the inter-coupling and complex relationships between maintenance tasks of different systems lead to less quantifiable maintenance information among them. This also makes it difficult to reconstruct the structure of the WSM work; traditional cost forecasting methods based on engineering decomposition have difficulty in achieving satisfactory results. Furthermore, its corresponding data collection also incurs significant labour and time costs. Consequently, the primary ideology of WSM cost prediction is to establish the regression model by selecting relevant quantifiable feature indicators from the basic information and the corresponding maintenance scenarios of warships, with cost indicators as the output target. The regression model is employed to investigate statistical dependences between input features and costs, and then to estimate WSM costs based on the dependences. As the number of samples increases, these parametric models become more flexible and adaptive, accurate, and reliable, and they can also achieve significant reductions in time in terms of data collection. Given the small sample size of WSM data, various grey prediction models were introduced to perform cost forecasting such as GM(0, N) [6], GM(1, 1) [4] and grey correlation analysis [2] to construct the time-series model for short-term forecasting of WMS maintenance costs. The GM(0, N) warship maintenance cost prediction model based on the priority accumulation method of similar information is proposed in [6], to solve the warship maintenance cost prediction problem in the small sample case. In [5], the grey orthogonal method is proposed to analyze the influencing factors of the naval vessel maintenance cost, and further determine the major and minor influencing factors of maintenance cost. The combined prediction model for warship maintenance cost measurement based on grey correlation ranking was developed in [3]. As an emerging field in artificial intelligence, with its historical experience-based solution mechanism, case-based reasoning (CBR) is well suited to the problem of WSM cost prediction, which has difficulty determining an explicit model and lacks abundant experience, therefore, various CBR-based WSM cost prediction methods have been proposed [1; 8; 9; 10; 11; 12]. The literature [8] proposes a similarity retrieval technique and case adaptation technique for WMS cost cases based on CBR so that it can better adapt to the characteristics of WMS cases. Through an active learning strategy, a corresponding maintenance cost case library is formed, which in turn ensures the prediction accuracy of CBR technology on WMS costs.