Excavation equipment classification based on improved MFCC features and ELM

An efficient algorithm for earthmoving device recognition is essential for underground high voltage cable protection in the mainland of China. Utilizing acoustic signals generated either by engine or the clash during operations, an intelligent classification system for four representative excavation equipments (namely, electric hammers, hydraulic hammers, cutting machines, and excavators) is developed in this paper. A benchmark acoustic wave database collecting from a real construction site is first established. Then, an improved feature extraction approach based on the Mel-Frequency Cepstrual Coefficients (MFCC) which can efficiently describe the dynamics of acoustics wave is developed. The recent fast and effective extreme learning machine is employed as the classifier in the proposed classification system. Experiments on real collected signals and field testings using our developed software platform are provided to demonstrate the efficiency of the proposed classification system.