Research On Performance Assessment And Prediction Method For Hoist Bearing Based On Gaussian Processes

The key equipment of mine hoisting is the hoist bearing, it is also the importantpart of the lifting system and the core components; meanwhile, it is the weak part ofthe system and easy to cause fault; thus its operating status directly affects theoperation of the whole system. Therefore, how to effectively assess the operationalstatus of hoist bearing, how to study and formulate equipment maintenance program,how to use the means of intelligent information processing to implement intelligentmaintenance of the equipments, which is an urgent problem to be solved to make surethe operation of the equipments is safe and reliable, so that a variety of unexpectedfatal accidents can be avoided or reduce. In the field of coal production, it is also animportant topic to make the process of production safely and the coal industryhealthily.The main research work is elaborated as following:(1) In order to improve the efficiency of assessment and prediction algorithmwhen dealing with the data degradation in the high-dimensional feature space, amethod, which is based on the spectral regression, is proposed to capture the featuresof the hoist bearing. Through multiple sets of experimental data of bearing, it isconfirmed that the method of spectral regression is more effective than PCA, FA orthe other common manifold learning techniques.(2) As the methods of supervised learning normally need a larger number ofcorrectly per-labeled training examples and the distribution of training examples isimbalance, a method of Semi-supervised Gaussian process is proposed in the field ofassessing the degradation of the hoist bearing. The effectiveness of such method isproven through several experiments in practice.(3) To improve the efficient of Gaussian process when dealing with equipmentperformance degradation data, a method of Hidden Markov Gaussian process isproposed in the field of assessing the degradation of the hoist bearing. The method iswell combined the kernel-based Gaussian process regression with the characteristicsof HMM in predicting the performance degradation data and can be used to build up aHMMGP model. The comparing tests are carried out by using the some sets ofexperimental data of full life of the bearings. HMMGP prediction model has higherprediction accuracy.The contents in this thesis include capturing the features of the hoist bearing, assessing and predicting the degradation of the bearing by using the Gaussianprocesses learning and the related improved methods. The proposed algorithm is usedin a real case to assess the degradation of the hoist bearing, the result of theapplication shows that the effectiveness of the new algorithm when it is used to assessthe degradation of the hoist bearing, and it has great influence on improving thereliability of mining equipment and process of the mining production.