[PERSON] [31] conducted experiments on OH-58C helicopter main-rotor transmission to demonstrate capability of vibration separation on planetary gear fault detection. [PERSON] [32; 33] applied vibration separation on fault detection of sun gear in planetary gearbox. He [34] proposed a method for separating coupling modulation from signal of gearbox based on match pursuit and correlation filtering. [PERSON] [35] developed a windowing and mapping strategy of extract signals of the planet gear of interest. [PERSON] [36] dedicated a tutorial on vibration separation technique-based tooth fault detection of planetary gear set. Neural network-based methods require large volume of data to guarantee a well-trained model for industrial application. Overfitting and underfitting are also underlying risks. Noise level contained in the data, model structure, and number of parameters should be delicately considered during model design and test. There is also a lack of interpretation on physical meaning of model structure and parameters. Additionally, high-performance computing platform is necessary to train a model within an acceptable waiting time. Vibration separation (VS) does not require huge amount of data or high-performance computing devices. It can separate out dynamic responses of individual gear components from overall vibrations collected from the gearbox casing, but its result covers only one cycle of the gear component. As a result, if a gear is damaged, the fault-related impulse will appear only once in the resultant signal. This feature of VS has limited its application on fault diagnosis of planetary gearboxes. Moreover, fault-related impulse is transient signal. It will not only decay quickly but also be easily overwhelmed by regular vibration signals and noises. Thus, another challenge for VS-based methods is detection of fault-induced impulse among regular vibrations and noises. Among ring gear, planet gear, and sun gear, planet gear is the most unique gear component in planetary gearbox. Movement of planet gear is more complicated compared with ring gear and sun gear. It rotates around sun gear while rotating around its own central axis. Thus, signal transfer paths from the meshing area (including ring-planet and sun-planet meshing) to the sensor change periodically, which result in modulation effect in monitoring signal of planetary gearbox. Planetary movement brings in difficulties on fault diagnosis of planet gear since modulation effect should be carefully considered when developing a fault diagnosis method. Focusing on fault diagnosis of planet gear and limitation of current VS-based methods, continuous vibration separation (CVS) is proposed in this paper; a fault diagnosis method of planet gear using CVS and minimum entropy deconvolution (MED) is proposed as well. CVS maintains signal periodicity when applying extracting and mapping strategy, while MED helps to enhance fault-induced impulsive signal components. In order to guide the development and validate the effectiveness of the method, a dynamic model focusing on dynamic response characteristics is established to simulate planet gear fault. The remainder of this paper is organized as the following: Section 2 focuses on planetary movements-caused modulation effects and dynamics of planet gear under health and fault state. Section 3 presents fault diagnosis method of planet gear using CVS and MED after investigating planet gear meshing regularity. In Section 4, the proposed method is validated using both simulation signals and experimental signals. Simulations are conducted using the dynamic model stated in Section 2. At last, conclusions are drawn in Section 5 to summarize the whole paper.