Feature Analysis Based on Acoustic Emission Signal Processing for Tubular Steel Tower Condition Monitoring

In this study, we propose and analyze a machine learning method based on the genetic algorithm (GA) and supporting vector machine (SVM) for the effective classification of faults detected by an acoustic emission test on the welding parts of tubular steel towers. A band-pass filter, an envelope analysis (EA), and an intensified EA (IEA) are employed to generate feature vectors for the machine learning method based on the GA. After signal processing, the signals are applied to GA-based machine learning to derive the representative features of the received signal, and the SVM classifies the fault signals and normal signals from the detected signals. Consequently, it is confirmed that the received signal processed by EA and IEA can classify faults with an accuracy of 93 % or more. Hence, the proposed fault test and classification method is expected to be useful in the development of a system for constant monitoring and early detection of welding faults inside a tubular steel tower.