Persistent Homology Deep Meta-Transfer Learning for Intelligent Fault Diagnosis of Storage Stacking Machinery Under High-Speed and Heavy-Load Conditions

Because of the challenges in gathering fault data from storage stacking machinery operating under high-speed and heavy-load conditions, the diagnostic accuracy of data driven-fault diagnosis methods is often compromised by a lack of sufficient fault information. To mitigate this issue, we propose an intelligent fault diagnosis strategy using a novel persistent homology deep meta-transfer learning (PHDMTL) algorithm. This method aims to develop a more extensive feature representation, enhancing fault identification accuracy with limited data availability. Initially, a barcode chart of vibration data is generated through the persistent homology mechanism, from which the corresponding fault topology features are derived based on the chart. Subsequently, the fault features identified are fed into a specially designed deep meta-transfer model for multitask learning to derive a pretrained model. This pretraining model is further refined both externally and internally to produce an optimal initialization model adept at rapidly adapting to few-shot tasks. Experimental results affirm the viability and efficacy of the proposed method in scenarios requiring few-shot fault diagnosis. Notably, when compared with some current and advanced diagnostic methods, the proposed PHDMTL method demonstrates superior diagnostic accuracy for fault diagnosis in storage stacking machinery operating under high-speed and heavy-load conditions.