Quantum mechanics

Algorithm

Gaussian

Linguistics

Philosophy

Transfer of learning

Statistics

Similarity (geometry)

Multi-source

Conditional probability distribution

Operating system

Weighting

Data mining

Parallel computing

Computer science

Acoustics

Fault (geology)

Seismology

Geology

Radiology

Transfer (computing)

Physics

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Autoencoder

Feature (linguistics)

Domain (mathematical analysis)

Mathematical analysis

Artificial intelligence

Medicine

Deep learning

Bearing (navigation)

Encoder

Knowledge-Based Systems

Transfer learning based on a single source domain to a target domain has received a lot of attention in the cross-domain fault diagnosis tasks of rolling bearing. However, the practical issues often contain multiple source domain data, and the information contained in the target domain is quite different from one source domain to another. Therefore, the transfer pattern from multiple source domains to the target domain undoubtedly has brighter application prospects. Based on these discussions, a multi-source domain transfer learning approach called conditional weighting transfer Wasserstein auto-encoder is developed to deal with the challenges of cross-domain fault diagnosis. Different from the traditional distribution alignment idea of directly aligning the source and target domains, the proposed framework adopts an indirect latent alignment idea to achieve better feature alignment, that is, indirectly aligning the feature distribution of source and target in the latent feature space with the help of Gaussian prior distribution. Furthermore, considering the variability of different source domains containing information about the target domain, an ingenious conditional weighting strategy is designed to quantify the similarity of different source domains to target domain, and further help the proposed model to minimize the discrepancy in conditional distribution. The cross-domain fault diagnosis tasks adequately verify that the proposed framework can sufficiently transfer knowledge from all source domains to the target domain, and has extensive application prospects.

A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains