Composite structures are very popular in industries due to their lightweight and high strength. They degrade and cause huge economic losses or even serious disasters. Structure health monitoring has a great significance in the operation and maintenance of industrial assets. Current commercial software for structural health monitoring uses traditional and common methods which are only effective in constant loading conditions and require a large amount of historical data to train models. The intern has developed an unsupervised structural damage detection technique. We have done some preliminary tests and the results are promising. In this project, we will perform more validation tests to further evaluate our algorithm's effectiveness under varying loading conditions. Then, we will convert our algorithm to software with a user-friendly interface. It is a big step to foster the growth and commercialization of innovative technologies through the Lab2Market program.