Pattern recognition of rigid hoisting guides based on vibration characteristics

A test rig is built to simulate the typical fault patterns of rigid hoisting guides and to collect vibration and inclination signals. In this work, we use these signals to perform data mining for fault-pattern recognition. Parameters are initially defined by analyzing collected signals. Then, the importance of each parameter is calculated using the boosting-tree method. Some valuable parameters are retained. To establish a data-mining algorithm that works remarkably for the fault recognition of rigid hoisting guides, six different algorithms including the boosting tree, K-nearest neighbor, MARSpline, neural network, random forest, and support vector machine are compared. Results show that the best performance is that of the boosting-tree algorithm, whose mechanism is then presented in detail.