Domain-adversarial Statistical Enhancement for Cross-domain Fault Diagnosis
Fault diagnosis is of great importance in ensuring the safe and stable operation of large-scale mechanical equipment.However,the obtained data often suffer from severe label shortages or lack of labels,and the data distribution varies significantly at different operating conditions.Traditional machine learning or fine-tuning methods have limitations in feature extraction,with a single pattern and fixed perspective,making it difficult to align features of the same class but different domains.To address these issues,this paper proposes a domain-adversarial statistical enhancement-based cross-domain fault diagnosis method called DASEM.This method utilizes direct transfer deep learning techniques to enhance the representation of global statistical charac-teristics within the framework of domain adversarial learning.It also integrates these characteristics with local structural patterns by constructing a dual-path feature extractor.The balance between domain labels and data structures is utilized to describe the manifestation of domain adversarial learning,and the fault diagnosis results are outputted based on class labels.Experimental re-sults on the bearing datasets from Western Reserve University and Jiangnan University demonstrate the effectiveness of DASEM,achieving an average accuracy of 94.90％and 93.15％,respectively,for various cross-domain tasks.
Fault diagnosisFeature distribution alignmentDomain adversarialGlobal statistical characteristics
NETL
NSTL
万方数据
