## 3 Fault Diagnosis
### FD Methods
#### 3.1.2 Knowledge-Based Method  
Knowledge-based FD methods are considered approaches that could utilize the expert-level diagnosis knowledge to solve the identification of fault state instead of manual judgment, as shown in Figure 5. Those methods are based on the qualitative model of prior knowledge, and then the FD process is realized by running a mature search algorithm. The core of the knowledge-based FD system is an export control system composed of an expert knowledge base, a dynamic database, various inference engines, the user interface, and an explanation system. More and more attention has been paid to expert system-based FD technology in dealing with the complex process.  
Rule-based reasoning, fuzzy logic-based reasoning, neural network-based reasoning, and case-based reasoning these four different classifications are used to distinguish knowledge-based diagnosis models according to different inference engines [80].  
(1) The rule-based reasoning is employed to complete diagnosis knowledge and then uses the predetermined rules to realize decision-making. The rule-based reasoning was achieved better diagnosis performance for FD of marine diesel engines [81], centrifugal pumps [82], hydraulic systems [83], and bearings [84]. A rule-based reasoning model for ship power systems is established based on the classification society's operational  
Figure 5: Knowledge-based FD models.  
mode requirements and regulations. The main engines, diesel-electric engines, and energy storage systems were considered [85]. Rule-based reasoning could build a nonlinear mapping relationship between feature and health condition, but with the increase of MSAE complexity, the number of rules required increases greatly so that the reasoning efficiency will decrease significantly;  
(2) Fuzzy set theory is introduced into inference engine to realize fuzzy logic-based reasoning. Through this method, imprecise non-numerical information description can be realized. The fuzzy logic-based reasoning method was first applied to power systems [86]. The application of Fuzzy logic-based reasoning for FD on board mainly includes scooter engines [87], bearings [88; 89], gear systems [90]. The performance of the method is affected by the difficulty of obtaining a fuzzy dataset, which will reduce the fault identification accuracy;  
(3) The neural network-based reasoning makes full use of the neural networks' learning, association, and memory ability to realize the intelligent reasoning of diagnostic knowledge. The application of the method mainly includes internal combustion engines [91], bearing [92]. Method implementation depends on sufficient fault data to realize model training, which is difficult to obtain in MSAE. In addition, the neural network adopts the black box principle, and the accurate mapping between some physical processes and diagnostic knowledge is not easy to realize;  
(4) Case-based reasoning uses similar existing cases to solve the technical problem. At present, this method is widely used in ship collision avoidance and ship design, and the application of MSAE fault diagnosis has not been found.  
The knowledge-based fault diagnosis model uses the knowledge of experts to construct an inference engine to realize fault pattern recognition. The model is highly dependent on expert knowledge, and the accuracy of expert knowledge directly affects the diagnosis results. Moreover, the model has no self-learning ability and can only solve the existing problems.