A Novel Intelligent Fault Diagnosis Method Based on Variational Mode Decomposition and Ensemble Deep Belief Network

Abstract

The deep belief network is widely used in fault diagnosis and health management of rotating machinery. However, on the one hand, deep belief networks only tend to focus on the global information of bearing vibration, ignoring local information. On the other hand, the single deep belief network has limited learning ability and cannot diagnose the health of rotating machinery more accurately and stably. As a non-recursive variational signal decomposition method, variational mode decomposition can easily obtain local information of signals. And the ensemble deep belief network composed of multiple deep belief networks also improves the accuracy and stability of the health status diagnosis of rotating machinery. This paper combines the advantages of ensemble deep belief network and variational mode decomposition to propose a novel diagnostic method for rolling bearings. Firstly, the variational mode decomposition is used to decompose the vibration data of the rolling bearing into intrinsic mode functions with local information. Then, using the deep belief network based on cross-entropy to learn the intrinsic mode functions of the rolling bearing data and reconstruct the vibration data. Finally, In the decision-making layer, the improved combination strategy is used to process the health status information of the bearings obtained by multiple deep belief networks to obtain a more accurate and stable diagnosis result. This method is used to diagnose experimental bearing vibration data. The results show that the method can simultaneously focus on and learn the global and local information of bearing vibration data and overcome the limitations of individual deep learning models. Experiments show that it is more effective than the existing intelligent diagnosis methods.