Experimental Study of Lubrication States Identification for Journal Bearings with Acoustic Emission Technique and Support Vector Machine Method
In order to online identify the lubrication state of sliding bearing with an efficient,fast and accurate way,an experimental system and a relevant acoustic emission measurement platform were constructed.The lubrication states of sliding bearings were tested and analyzed by the acoustic emission technique and the genetic algorithm-support vector machine method.The acoustic emission signals were firstly pre-processed.The effective feature parameters were extracted and selected from time domain,frequency domain,information entropy,etc.Then,the extracted effective feature parameters were combined into a feature vector as the input of support vector machine.The multi-lubrication states were identified by the support vector machine classifier,and furtherly optimized by the combination of the penalty factor and the kernel function parameter through the genetic algorithm.The optimal lubrication states identification was obtained,and the overall accuracy rate reached 93.3%.
gearbox in wind turbinejournal bearinglubrication stateacoustic emissionsupport vector machine
万方数据
维普
