In the Industrial Internet of Things, the massive data generated by the SCADA of wind turbines is not suitable for being directly sent to the cloud for processing due to real-time requirements. This paper designs and build a set of Micro-Wind Turbine equipment condition monitoring system based on Edge Intelligence.Three unsupervised anomaly detection algorithms, including OC-SVM, IForest and HBOS, are analyzed and compared with each other. The experimental results show that OC-SVM have the best real-time anomaly detection effect. The F1 scores in the rotation anomaly test set and vibration anomaly test set are 0.997 and 0.969，respectively. This paper can provide some reference value for the landing verification of edge side training and reasoning scheme.