国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

In order to effectively monitor and evaluate the working state of bearings, an evaluation scheme based on convolutional sparse combination algorithm was proposed. Based on the convolutional neural network framework, the sparse representation criterion of bearing performance was established and the attenuation degree of bearing performance was predicted. The bearing attenuation autocorrelation function is used to predict the density conditions related to the bearing spectrum, and on the basis of analyzing the numerical parameters of other models, the application stability of the evaluation method is verified. Degradation index is selected as the experimental object, and through the analysis of related index parameter values, it can be seen that the evaluation results of the proposed algorithm have strong interpretability, which can better maintain the attenuation mechanism of bearing performance, and the influence coefficient value is controlled between [-1,1]. In comparison with the prediction performance of the traditional algorithm, the deviation values of the algorithm in two states are 0.02 and 0.01 respectively, which is superior to the traditional bearing performance evaluation algorithm, and also has certain advantages in the evaluation and prediction efficiency.