A generalized degradation tendency tracking strategy for gearbox remaining useful life prediction

Gear is an important component of mechanical equipment. Its health state will affect the operation of the whole equipment. Therefore, the remaining useful life (RUL) prediction of gearbox is very important. However, most of the current deep learning-based RUL prediction methods inevitably focus too much on the fluctuation charac-teristics of degradation data, while not capturing trend characteristics well. In order to obtain accurate and reliable prediction results, a generalized degradation tendency tracking strategy (GDTTS) for gearbox RUL prediction is proposed. Firstly, a health indicator (HI) of gearbox degradation process is constructed based on improved HI fusion method. Then, a tradeoff loss function (TLF) is proposed to guide the feature learning of the model. The proposed TLF enables the model to grasp the tendency characteristics of the data itself and robust to singularities which can cause the entire prediction model to deteriorate. Finally, a new end-of-life determination criterion is established. The prediction results on the actual gearbox datasets show that the proposed strategy is a generalized strategy for gearbox RUL prediction which can apparently improve the prediction performance of the life prediction models.

Automated summary

Gear is crucial for mechanical equipment, and its health directly influences the overall operation of the equipment. Therefore, accurately predicting the remaining useful life (RUL) of gearboxes is of great significance. However, current deep learning-based RUL prediction methods often overlook trend characteristics and focus on the fluctuation patterns of degradation data. To address this issue, a generalized degradation tendency tracking strategy (GDTTS) is proposed to improve the prediction performance by capturing both trend and fluctuation characteristics. Experimental results on real gearbox datasets demonstrate the effectiveness of the proposed strategy.