Research of Remaining Useful Life Prediction of Gear based on Exponential Smoothing and Improved Incremental SVR - Liu Zhenxiang; Chen Xiaohui (Jixie chuandong)
The remaining life prediction problem of gear is studied and a new set of gear life prediction program is put forward based on double exponential smoothing（ DES） and improved incremental support vector machine for regression（ SVR）. The new program utilizes principal component analysis（ PCA） to filtrate fusion index set,and then using DES to process fusion index set as the input of improved incremental SVR. Meanwhile,the improved incremental SVR prediction model is constructed based on incremental Latin hyper- cube sampling（ LHD） and Direct search algorithm. Compared with the standard SVR prediction model based on original characteristic values,the interference of signal randomness and mutation of the new model sre overcome,furthermore,the latest evolution trend information of the gear status is obtained,the optimization effect and prediction performance is obviously improved. The validation analysis of the new model is carried out by using the full life- cycle test data,the results show that the improved incremental SVR prediction model obtained a more reliable,more stable predictions,it has certain practical value in engineering.