Joint alignment network preserving structural information for multimode process fault diagnosis

Abstract

Production conditions are complex and varied for a number of reasons. Models for defect diagnosis may perform worse as a result of the distributional mismatch between test data and training data. In order to diagnose process faults, it is crucial to take into account the fact that data exhibits varied distribution characteristics under various conditions. In the case of multiple operating conditions, the cross‐domain problem caused by different data distributions can degrade the performance of deep learning‐based fault diagnosis models. To overcome this challenge, a joint alignment network preserving structural information (JANSI) method is proposed. To extract richer and fine‐grained domain‐invariant features, the structural information preservation is proposed, which combines domain labels, category labels, and data distribution structures. To increase intra‐class compactness and inter‐class separability, class centre alignment is proposed. The effectiveness of the method on the cross‐domain unsupervised fault diagnosis problem is verified through three case studies.

Issue Highlights

“…The proposed method is compared with existing successful unsupervised domain adaptation methods and achieves positive results on three datasets. [3] One-step hydrothermal synthesis of a green NiCo-LDHs-rGO composite for the treatment of lead ion in aqueous solutions Ata Makarem, Alireza Aldaghi, Mohammad Gheibi, Mohammad Eftekhari, Kourosh Behzadian…”