In order to solve the problem of high error rate of grid equipment fault prediction due to the existence of communication blind zones and poor smoothness of collected data in existing monitoring methods, a digital twin-based full-life cycle condition monitoring method for grid equipment is proposed. This paper divides the whole life cycle stage of power transmission and distribution equipment, and summarizes the characteristics of life cycle stage determination; based on this, we improve RFID technology to build a multi-antenna RFID communication framework; based on the construction of the communication framework, we combine the characteristics of different life cycle stages of the equipment, and adaptively adjust the collection interval of the operation data to obtain the operation data of power transmission and distribution equipment; we effectively combine the integration of the moving average autoregressive model and the support vector machine algorithm to predict the faults in advance, and we use the digital twin to predict the faults in advance, and then we use the digital twin to predict the faults in advance. The moving average autoregressive model and support vector machine algorithm are effectively combined to predict the faults of power transmission and distribution equipment in advance, thus realizing the intelligent monitoring of the whole life cycle of power transmission and distribution equipment. The experimental results show that compared with the comparison method, the error rate of fault prediction of transmission and distribution equipment obtained by the proposed method in this paper is lower, the lowest numerical value is 5%, which indicates that the fault prediction results of the proposed method are more accurate.