As a transmission component, gears take on a great significance for the Electromechanical system of aviation equipment and has long aroused the widespread attention of researchers. Fault diagnosis and remaining useful life (RUL) prediction during the gear operation is critical to prognostics and health management (PHM) of gear transmission systems. In this paper, the focus is placed on gear PHM methods. This paper attempts to review the existing methods and summarize them into four types (including physical model-based, knowledge modelbased, data-driven model-based, as well as hybrid model-based methods). Based on a wide variety of methods, the principle and the applica‐ tion situation are indicated. In particular, the data-driven model-based methods consist of stochastic algorithms, statistical algorithms, as well as the artificial intelligence (AI) method. The fault diagnosis, performance degradation and RUL prediction of various methods are primarily summarized. Furthermore, the advantages and disadvantages of various methods are assessed, and the prospect of the Digital Twin (DT) is forecasted to boost the applications of PHM.

Model-based fault diagnosis serves as an efficient and powerful technique in addressing fault detection and isolation (FDI) issues for control systems. However, the standard methods and their modifications still encounter some difficulties in algorithm design and application for complex higher-order systems. To avoid these difficulties, a novel fault diagnosis framework based on multiple performance indicators of closed-loop control system is proposed. Under this framework, a so-called performance residual vector is constructed to measure the differences between the real system and the nominal model in terms of system stability, accuracy, and rapidity (SAR) respectively. The criteria for quantification, normalization of the SAR residuals and the explicit mappings between the thresholds and the required performance are given. FDI can be easily achieved simultaneously by monitoring the normalized residual vector length and direction in the SAR performance residual space. A case study on electro-hydraulic servo control system of turbofan engine is adopted to demonstrate the effectiveness of the proposed method.