An AVMD-DBN-ELM Model for Bearing Fault Diagnosis

Abstract

Rotating machinery often works under complex and variable working conditions; the vibration signals that are widely used for the health monitoring of rotating machinery show extremely complicated dynamic frequency characteristics. It is unlikely that a few certain frequency components are used as the representative fault signatures for all working conditions. Aiming at a general solution, this paper proposes an intelligent bearing fault diagnosis method that integrates adaptive variational mode decomposition (AVMD), mode sorting based deep belief network (DBN) and extreme learning machine (ELM). It can adaptively decompose non-stationery vibration signals into temporary frequency components and sort out a set of effective frequency components for online fault diagnosis. For online implementation, a similarity matching method is proposed, which can match the online-obtained frequency-domain fault signatures with the historical fault signatures, and the parameters of AVMD-DBN-ELM model are set to be the same as the most similar case. The proposed method can decompose vibration signals into different modes adaptively and retain effective modes, and it can learn from the idea of an attention mechanism and fuse the results according to the weight of MIV. It also can improve the timeliness of the fault diagnosis. For comprehensive verification of the proposed method, the bearing dataset from the University of Ottawa is used, and some recent methods are repeated for comparative analysis. The results can prove that our proposed method has higher reliability, higher accuracy and higher efficiency.

A Bearing Fault Diagnosis Method Based on Improved Transfer Component Analysis and Deep Belief Network

“…W R denotes a m × n weight matrix connecting the neurons between the visible and hidden layers. The joint probability distribution P(v, h; θ) and the energy function E(v, h; θ) between the hidden and visible layers of the RBM are shown in Equations ( 8) and (9). The purpose of the greedy layer-by-layer training is to maximize the joint probability distribution.…”

“…Convolutional neural networks and fuzzy neural networks are prone to computational complexity and long training times. References [8][9][10][11] introduced deep belief networks (DBNs) into bearing fault diagnoses, further improving the accuracy of fault recognition. Unsupervised domain adaptation (UDA) [12][13][14][15] has been applied to fault diagnoses, improving the fault diagnosis of variable working conditions.…”

Improved SE-ResNet Acoustic–Vibration Fusion for Rolling Bearing Composite Fault Diagnosis

“…This approach effectively diagnoses faults in rolling bearings. As another example, Lei et al [17] introduced an intelligent bearing fault diagnosis method that combines adaptive variational mode decomposition (AVMD), a deep belief network (DBN) based on mode ordering, and an extreme learning machine (ELM). This method can adaptively decompose unsteady vibration signals into temporary frequency components, organize a set of effective frequency components, and facilitate online fault diagnosis.…”