Performance Degradation Assessment Based on Transfer Learning for Bearing

Bearing design, production, and complicated operating condition can lead to the scattered life cycle degradation distribution, which will bring a challenge of generalization for performance degradation assessment models. And it is costly and time-consuming to collect a large amount of labeled data for supervised diagnosis, especially when the task comes from a new operating condition. Thus in this chapter, a novel bearing degradation assessment model is proposed based on transfer learning and deep hierarchical features extraction. The research on degradation assessment is transformed to the classification task of degradation pattern, which divides the degradation process into the normal, slight fault, fault development, and damage patterns. The hierarchical network with random weight parameters is introduced to extract the local sub-band characteristics of the spectrum, in which the multiple alternately convolution and pooling layers without supervised fine-tuning are employed. The Joint Geometrical and Statistical Alignment method is then utilized to obtain projected sharing feature space, and thus the knowledge of bearing degradation process is transferred to accomplish degradation pattern assessment under different operating conditions. Results of the experiments on bearing fault severity and degradation process show that the proposed method reduces the feature distribution divergence between the degradation processes and accomplishes bearing performance degradation assessment in different operating condition.