Data Regeneration Based on Multiple Degradation Processes for Remaining Useful Life Estimation

Remaining useful life prediction based on deep learning for critical components demands sufficient and varied degradation samples. However, the field acquisition or laboratory preparation is generally cumbersome or the samples obtained are stereotyped. The paper proposes a data regeneration method based on multiple degradation processes to deal with the dilemma, which consists of three parts: state identification, regeneration rules from run to failure and state databases. In the first part, a global gain index and a local gain index are proposed to identify the different states of components. In the second part, an identical transformation method, a probability distribution of degradation states and data regeneration criteria are proposed to serve regeneration process of samples from run to failure. In the third part, an augmentation framework based on conditional generative adversarial networks is proposed to enrich the samples of the state database, which makes state samples more diverse. The practicability of regenerated samples obtained by the proposed method was verified by two experiments. In each experiment, initial samples, regenerated samples and hybrid samples were established respectively. Experiments with different training samples based on the same network were carried out to verify the effectiveness of the regenerated samples.