Vibration source classification and propagation distance estimation system based on spectrogram and KELM

Abstract

Earth surface vibration signals source classification and propagation distance estimation attract increasing attention in recent years due to the wide applications in many areas. In this study, the authors develop a hybrid classification and propagation distance estimation algorithm for general earth surface vibration sources. The spectrogram (SPEC) feature characterising the energy distribution of vibrations is first developed for signal representation in this study. The kernel-based extreme learning machine (KELM) algorithm is then adopted for the vibration source classification and propagation distance estimation. Comparing with the conventional approaches, the proposed KELM + SPEC algorithm is not only effective in characterising the time- and frequency-domain features of vibrations, but also superior in accuracy and efficiency. To test the effectiveness of the proposed KELM + SPEC algorithm, experiments on real collected vibration signals are presented, where simulations on both periodic and aperiodic vibrations are carried out in the study. Comparisons to various existing vibration signal extraction and classification algorithms are provided to show the advantages of the proposed KELM + SPEC algorithm.

Keywords

• signal representation
• vibrations
• learning (artificial intelligence)
• feature extraction
• signal classification
• propagation distance estimation algorithm
• general earth surface vibration sources
• kernel-based extreme learning machine algorithm
• vibration source classification
• kelm + spec algorithm
• periodic vibrations
• aperiodic vibrations
• propagation distance estimation system
• hybrid classification
• signal representation