Power transmission line is one of the most important infrastructures of power system, and its safety monitoring is of great significance. The conventional way of fault monitoring of power transmission lines by only setting threshold value on single temperature data of strain clamp turned out to multiple misjudgements and delayed alarms, causing the increment of operation risk of power transmission line. In this paper, various types of time-varying sensors data such as strain clamp temperature data, environmental data, and cable ampacity data are accounted. Also, an unsupervised machine learning algorithm - K-means clustering algorithm was introduced to build a discriminant model in detecting the defects of power transmission line. Experimental results proved that the proposed method is able to avoid delayed alarms as well as misjudgement incurred from conventional method. As a result, the operation safety of power transmission line and inspection efficiency can be improved. The inspection cost would be reduced as well.