## 2 Literature Review  
### Fault Diagnosis of Aeroengines  
As one of the most complicated structures of aircraft, aeroengines work in extremely harsh environments of high temperature, high pressure, and strong vibration for a long time, leading to fatigue, creep, fracture, and many other component failures. Therefore,fault diagnosis of aeroengines is necessary to ensure safe and efficient operation of the engine. There are three widely used methods for aeroengine fault diagnosis: model-based, knowledge-based, and statistical learning methods.  
Model-based fault diagnosis needs to analyze the entire structure of mechanical equipment and establish corresponding mathematical or simulation models. [PERSON] et al. (2018) established a simulation model for lubricating oil systems [24]. Using this model, an online fault diagnosis system was built using the parameter trend analysis method. [PERSON] and [PERSON] (2006) developed a fault diagnosis and prediction system based on discrete event system modeling and tested the system using actual flight data of startup component failures [25]. They believed that such qualitative modeling methods can be applied to the hierarchical diagnosis of complex large-scale systems because they do not require a detailed model of the system. [PERSON] et al. (2018) established a nonlinear model for the fault diagnosis of a fuel regulator using a particle swarm optimization algorithm combined with a back-propagation neural network for higher diagnosis precision [26]. The nonlinear modeling method is closer to the real operating state of an aeroengine with higher modeling accuracy. This type of method, combined with some conventional fault classification algorithms, can better serve the fault diagnosis of aeroengines.  
Knowledge-based fault diagnosis can effectively utilize expert knowledge and experience to make judgments without relying on analytical mathematical models. An expert system is the most classic knowledge-based fault-diagnosis method. [PERSON] et al. (2021) proposed an expert system for aeroengine gas-path fault diagnosis to manage the flight data of various types of engines [27]. [PERSON], [PERSON], and [PERSON] (2022) built the first tentative case base in the field of aeroengine fault diagnosis, and developed a CBR system with a highly accurate novel similarity measure for fault diagnosis of aeroengines, where three local similarity measures associated with different attributes were integrated [28].  
Statistical learning methods for the fault diagnosis of aeroengines mainly include neural networks, support vector machines, and Bayesian networks. [PERSON] et al. (2020) used neural network methods such as convolutional neural networks and back-propagation neural networks for aeroengine gas-path fault diagnosis instead of traditional thermodynamic methods [29]. Neural networks can not only obtain higher accuracy of fault diagnosis but also have better adaptability to aeroengine data. [PERSON] and [PERSON] (2004) proposed a Bayesian belief network for gas turbine performance fault diagnosis, which can be built from mathematical models rather than hard-to-obtain flight data from malfunctioning operations [30].