There exist some problems in the mechanical equipment condition monitoring system based on cloud computing framework. The problems of the extension of data transmission, poor real-time performance of early warning and diagnosis etc. Usually occur in practical application. This paper presents a mechanical equipment condition monitoring system for edge computing, which has three-tier architecture: Equipment layer, edge layer and cloud layer. High real-time computing tasks are deployed in multiple edge computing nodes, and data feature extraction, dimensionality reduction, intelligent diagnosis, data saving and uploading are carried out in the edge layer. The proposed method is verified on the spindle test-bed of high-speed machine tool. The experimental results show that the condition monitoring system based on edge computing reduces the output delay by 29. 5% compared with the condition monitoring system based on cloud computing, saves 81. 3% cloud storage space, and significantly improves the real-time performance of the system under the condition of ensuring a high diagnosis rate.