2results about How to "Improve troubleshooting performance" patented technology

This invention discloses a multimodal gearbox fault diagnosis method based on deep transfer learning, addressing the problem of poor gearbox fault diagnosis capability under unlabeled sample conditions, belonging to the field of gearbox fault diagnosis technology. The method includes: collecting raw vibration signals under different operating conditions, defining them as source and target domains; fusing multimodal information in the time and frequency domains using a data-level fusion method and dividing the collected signals into samples; constructing a deep multimodal adversarial transfer network model, extracting fault information features from the source and target domains through iterative adversarial training to adapt to the joint probability distribution of the source and target domains, and utilizing the abundant fault label information in the source domain to ensure accurate fault category discrimination; finally, obtaining a trained transfer diagnosis model for the target domain. This method is applicable to gearbox fault diagnosis under different operating conditions, i.e., transfer diagnosis between different operating conditions or different fault types, exhibiting high accuracy and good generalization performance.

The application discloses a kind of complex equipment fault diagnosis methods based on polynomial improved graph convolution network.It includes the following steps: (1) the vibration signal of complex equipment operation is collected;(2) using cross-correlation coefficient, construct multi-objective fitness function, combine particle swarm optimization algorithm to optimize the selection of variational mode decomposition parameters;(3) according to the parameter obtained by optimization, the vibration signal is decomposed by variational mode, obtains several modal components and reconstructs to obtain denoising signal;(4) the signal after reconstruction is divided to construct graph;(5) the graph structure is input into the graph convolution model improved by Hermite polynomial to complete the diagnosis of fault.The application is used for the fault diagnosis of complex equipment, uses modal decomposition to denoise signal and improves the graph convolution model, improves the accuracy of diagnosis, and is suitable for the technical field of complex equipment fault diagnosis.