Research On Performance Degradation Trend Prediction Of Rolling Bearings Based On Integrated Soft Competition ART

As the most commonly used parts of the rotating machinery,the performance of the bearing directly affects the accuracy and reliability of the equipment.Therefore,the prediction for bearing performance degradation trend is of great significance.Two key issues in the prediction of bearing performance degradation are the establishment of prediction model and the selection of bearing performance degradation.This paper aimed to put forward the prediction method of bearing performance degradation based on integrated soft ART-RBF model.The establishment of prediction model is a key problem to be solved.The radial basis function(RBF)neural network can be used to establish a prediction model due to its characteristics such as fast training speed and approach to any nonlinear function.However,there are still some problems: 1)sample learning and classification are performed by the hard competition mechanism,which usually causes misclassification and reduces the prediction accuracy;2)training RBF network requires many samples.Processing so many samples will generate a large number of nodes,leading to a challenge to the network complexity.In this paper,the soft adaptive resonance theory was introduced into the RBF neural network,and the soft ART-RBF neural network prediction model was established.The model can adaptively control the generation of the hidden layer nodes by setting the vigilance parameters,and it can simplify the network and improve the computing efficiency when processing different number of training samples.The adoption of soft competition mechanism can effectively reduce the misclassification and improve the accuracy of pattern recognition and trend prediction.The results of verification of time series and analysis of model parameters showed that this prediction model can solve those problems above-mentioned to a certain extent.The other key problem to be solved is how to select the index which can describe the bearing performance degradation.In this paper,the confidence value(CV)was used as a comprehensive index,and the performance degradation was judged by comparing the change of the CV value between a target interval and the normal stateinterval.This index was based on the minimum quantization error obtained from the self-organizing map(SOM)network.Using the data obtained from the accelerated fatigue test of bearings,some common characteristic parameters were extracted,and the relative value of 4 characteristic parameters were selected to construct the comprehensive index according to the sensitivity and consistency analysis.Compared with the single feature,the comprehensive index based on the multiple feature parameters has obvious advantages in terms of bearing degradation sensitivity.In order to improve the prediction accuracy and stability,the prediction values of the 4 relative characteristic parameters to construct CV prediction samples were obtained based on integration method.In summary,this paper presents a prediction method of bearing performance degradation trend based on integrated soft ART model.The experiments showed that this method can be used to evaluate and predict the bearing performance degradation trend,and has some reference value for the evaluation and prediction of performance degradation research and the practical engineering application.