## 1 Introduction  
Due to the complex structure of the aviation piston engine, its control system must be a complex system with multi-loop, multi-variable, and non-linear. Currently, adopting Full-Authority Digital Electronic Control (FADEC) [1] has become an inevitable trend in the development of aero-engine control system technology. Under the poor working conditions of the engine, the sensors, the execution structure and the internal components of the engine control system will inevitably fail during operation. If the faults cannot be diagnosed and eliminated in time, it will cause huge economic losses while endangering personal safety. Therefore, there is an urgent need to improve the reliability and safety of the aviation piston engine control system. At present, the main measure taken to improve system reliability and safety is to improve the reliability of engine components [2]. However, the reliability of components will inevitably decrease as time goes by. Therefore, if the system faults can be detected, separated and corrected by automatic fault detection and diagnosis technology as early as possible, it can effectively improve the safety and stability of the system and avoid unnecessary losses [3; 4]. In this aspect, researchers have done a lot of work. For example, [PERSON] et al. proposed some fault-tolerant tracking-control schemes based on the adaptive-control technique [5; 6]. The efficiency of the approaches are verified by experiment. [PERSON] et al. proposed a new design approach for distributed state estimation and fault detection and isolation (FDI) filters, which can detect the fault signal effectively [7]. With the research and development of intelligent algorithms such as neural networks and support vector machines, intelligent diagnostic techniques have emerged since the early 1980s and are gradually applied in various fields. The techniques aroused the attention of engine researchers. In 1999, [PERSON] et al. applied support vector machine to the actual detection of engine knock [8]. In 2002, researchers in Nanjing University of Aeronautics and Astronautics used probabilistic neural networks to qualitatively diagnose several prototype faults of aero engines. The results showed that though the noise of the measured parameters was large, the probabilistic neural network still had high diagnostic accuracy and could minimize the losses from misdiagnosis [9]. In 2011, [PERSON] et al. applied the neural network based on Hebb learning rules to the fault diagnosis of power systems, and the effectiveness of the method has been verified by various testing systems [10]. In 2016, a researcher used the support vector machine to establish the main fuel flow estimation model of the engine, and the effectiveness of the fault diagnosis system designed by them was verified by semi-physical simulation experiments. It promoted the application of artificial intelligence algorithm in engine fault diagnosis [11]. In 2018, [PERSON] studied the feature vector which was based on IMF band energy characteristics and used the support vector machine to realize the identification of different degrees of the engine pistons' failure [12]. The result promotes the application of support vector machine in the fault diagnosis of piston engines. At present, the use of intelligent diagnostic technology to diagnose engine faults has become a major trend. Improving the accuracy of fault diagnosis results and the applicability of diagnostic techniques has become the primary goal of researchers. With the purpose to improve the accuracy of diagnosis results and improve the safety and reliability of aero engines, this paper proposes a fault diagnosis and optimization method for engine control system based on probabilistic neural network and support vector machine. The experiments' results show that the engine fault diagnosis method based on probabilistic neural network and support vector machine can effectively diagnose the common faults of the control system.