The existing data-driven methods in the early detection of rolling bearing degradation have problems of low sensitivity and high false alarms. To address these issues, a dynamically adjustment grey incidence analysis ( DAGIA) method for transient mechanical equipment health monitoring is proposed. First, the Hilbert transform is applied to demodulate the vibration data of the rolling bearing to obtain the envelope signal. To weaken the influence of the value of the resolution coefficient to highlight the degree of discrimination of the correlation value, the feature-to-noise energy ratio (FNER) method is introduced into the traditional grey incidence analysis (TGIA) to dynamically adjust the resolution coefficient, which can characterize the strength of bearing faults. Then, the first set of data is extracted at the initial stage of bearing operation as reference data. The dynamic grey incidence analysis is calculated between the remaining data and the reference data and the bearing performance degradation index is established. Finally, according to the normal samples and combined with Chebyshev′s inequality, the control line is set to identify the starting position of the early degradation of the rolling bearing. The IMS and XJTU-SY databases are used to complete the early degradation recognition of rolling bearings. The results show that the proposed method can accurately recognize the starting position of early degradation and the false alarm is close to 0. It has both sensitivity and robustness, which is beneficial for equipment maintenance personnel to better grasp the operating status of rolling bearings.