Advances in vibration analysis and modeling of large rotating mechanical equipment in mining arena: A review

Abstract

Large-scale rotating mechanical equipment in the mining arena plays a pivotal role in mining production, where vibration issues directly influence production efficiency and safety. This Review aims to provide a comprehensive review of the latest advancements and methodologies related to the generation mechanisms, identification, and applications of vibrational characteristics in large-scale mining rotating mechanical equipment. Semi-autogenous mills, ball mills, and coal mills are selected as archetype equipment, and the Lagrangian motion equation is employed to unveil the generation mechanisms of vibrations and the embedded physical information in the signals of these machines. Initially, the research delves deeply into the acquisition, extraction, and identification of vibrational signal features, emphasizing that while mechanical vibration signals can reveal the internal operational state and fault information of machinery, there remains a need to enhance their capability to depict complex vibrational signals. Subsequently, this Review discusses in depth the studies focused on predicting the vibrational state of equipment by establishing accurate and reliable soft measurement models, pointing out that current models still have room for improvement in prediction accuracy and generalization capabilities. Conclusively, based on the elucidation of mechanical vibration mechanisms and the collation and outlook of the existing research study, the importance of on-site monitoring, deep learning, Internet of Things technology, and full lifecycle management is accentuated. To better support practical engineering applications, further exploration into the physical properties of vibrational signals and the mechanisms of mechanical vibrations is essential.