Intelligent fault diagnosis of machinery based on hybrid deep learning with multi temporal correlation feature fusion

With the advent of intelligent manufacturing era, higher requirements are put forward for the fault diagnosis technology of machinery. The existing data-driven approaches either rely on specialized empirical knowledge for feature analysis, or adopt single deep neural network topology structure for automatic feature extraction with compromise of certain information loss especially the time-series information's sacrifice, which both eventually affect the diagnosis accuracy. To address the issue, this paper proposes a novel multi-temporal correlation feature fusion net (MTCFF-Net) for intelligent fault diagnosis, which can capture and retain time-series fault feature information from different dimensions. MTCFF-Net contains four sub-networks, which are long and short-term memory (LSTM) sub-network, Gramian angular summation field (GASF)-GhostNet sub-network and Markov transition field (MTF)-GhostNet sub-network and feature fusion sub-network. Features of different dimensional are extracted through parallel LSTM sub-network, GASF-GhostNet sub-network and MTF-GhostNet sub-network, and then fused by feature fusion sub-network for accurate fault diagnosis. Two fault diagnosis experimental studies on bearings are implemented to validate the effectiveness and generalization of the proposed MTCFF-Net. Experimental results demonstrate that the proposed model is superior to other comparative approaches.