As a classic mechatronic product, motor is widely used in electric vehicles, industrial control, automation and aerospace. To ensure safe operation of system and avoid failure, it is necessary to assess the reliability of Motor. The common method of motor's reliability assessment is based on single axial performance degradation data. While in practice, the reliability information of the motor is contained in several axial degradation data of mechatronic products, and each axial of degradation information has cross complementarities. The reliability of the motor estimated from only one axial degradation data cannot be guaranteed in the evaluation accuracy. So we consider fusing multi-axial degradation data of motor to assess its reliability. In this work, first, extraction of multi-axial (X-, Y-, and Z-axial) feature parameters from multi-rotor Unmanned Aerial Vehicle brushless DC motor vibration signals are obtained by degradation test. Then, the Wiener process is used to describe each axial degradation process of the motor. Next, the Copula function is applied to construct a reliability model which fuses the multi-axial degeneration prediction information of motor to evaluate its reliability. Finally, the results of case study indicate that the reliability assessment result based on performance degradation data from multi-axial of motor is more comprehensive and effective than the result of using the single-axial degradation data.