Intelligent fault diagnosis of rotating components in the absence of fault data: A transfer-based approach

This paper focuses on the intelligent fault diagnosis (IFD) of rotating components in the absence of fault data. Specifically, an Order Spectrum Transfer based Fault Diagnosis (OSTFD) method is proposed to establish IFD models for the target component by exploiting the monitoring data of other related machines. Considering the variable operating conditions, Bandwidth Fourier Decomposition method and Hilbert Order Transform algorithm are introduced in OSTFD to extract the envelope order spectrum (EOS) that is insensitive to unsteady speed and load for pattern recognition. Then, based on the fault mechanism, a novel Order Spectrum Transfer algorithm is proposed to transform the fault characteristics (EOS) of the target data to the source domain, in which the classifier based on one-dimensional convolutional neural network is trained. Experimental results based on four benchmark datasets demonstrate the effectiveness and superiority of the proposed OSTFD in actual applications lacking complete samples.

Automated summary

This paper presents a fault diagnosis method based on Order Spectrum Transfer, which establishes intelligent fault diagnosis models for rotating components by utilizing monitoring data from other related machines. Experimental results demonstrate the effectiveness and superiority of the proposed method in real applications lacking complete samples.