Fault diagnosis of head sheaves based on vibration measurement and data mining method

Abstract

Head sheaves are critical components in a mine hoisting system. It is inconvenient for workers to climb up to the high platform for overhaul and maintenance, and there is an urgent need for condition monitoring and fault diagnosis of head sheaves. In this article, Fault Tree Analysis is employed to investigate the faults of head sheaves, and headframe inclination, bearing faults, and head sheave swing are the three focal faults discussed. A test rig is built to simulate these three faults and collect vibration signals at bearing blocks. Based on vibration signals, some characteristic parameters are calculated, and together with the fault labels, a sample set is established. Before the selection of an excellent data mining method, these features are screened according to their significance, and then, gain–percentile chart, response–percentile chart, and prediction accuracy are used as the criteria to make a comparison between data mining algorithms. The result shows the boosted tree algorithm outperforms others and presents excellent performance on the evaluation of head sheave faults. Finally, this method is verified on a data set of 20 samples, and each case is identified correctly, which illustrates its high applicability.