Multi-sensor data fusion-enabled semi-supervised optimal temperature-guided PCL framework for machinery fault diagnosis

Abstract

Due to the extremely limited prior knowledge, machinery fault diagnosis under varying working conditions with limited annotation data is a very challenging task in practical industrial scenarios. To solve this issue, a multi-sensor data fusion-enabled semi-supervised optimal temperature-guided prototypical contrastive learning (OTPCL) framework is proposed for machinery fault diagnosis under varying working conditions with limited annotation data. First, a multi-sensor data adaptive weighting strategy (MSAWS) is built to reintegrate the multi-sensor data by adaptively learning the important information of different sensor data and assigning the corresponding weights. Then, an OTPCL method is proposed to reduce the domain shift between the source and target domains, where the vital parameter named the temperature coefficient are adaptively optimized for enhancing the ability of discriminant domain-invariant feature extraction. Finally, a semi-supervised learning (SSL) framework is constructed by combining the MSAWS, the OTPCL and a domain-shared classifier for machinery fault diagnosis. In two experimental analyses on machinery varying working conditions with limited annotation data, the superiority of the proposed MSAWS is validated by respectively using our fused multi-sensor data and different original single-sensor data for the SSL framework. The proposed method obviously outperforms the five advanced domain adaptation methods including CORAL, MMD, CMMD, JDA and PCL.