The reliability testing of electrical automation control equipment adopts two main methods:Laboratory testing and on-site testing.Laboratory testing provides a large amount of accurate and repeatable experimental data,which helps identify product reliability issues,but differs from the actual environment.On site testing generates highly valuable data,but requires accurate equipment matching and high professional skills of operators.Therefore,this study proposes a combination of laboratory testing,on-site testing,and deep learning methods for the reliability testing of electrical automation control equipment.It fully utilizes the large amount of experimental data from laboratory testing,reliability data from on-site testing,and the predictive ability of neural networks to achieve online evaluation of equipment operation data training models.
维普
万方数据