Abstract
Mechanical equipment fault detection is critical in industrial applications. Based on vibration signal processing and analysis, the traditional fault diagnosis method relies on rich professional knowledge and artificial experience. Achieving accurate feature extraction and fault diagnosis is difficult using such an approach. To learn the characteristics of features from data automatically, a deep learning method is used. A qualitative and quantitative method for rolling bearing faults diagnosis based on an improved convolutional deep belief network (CDBN) is proposed in this study. First, the original vibration signal is converted to the frequency signal with the fast Fourier transform to improve shallow inputs. Second, the Adam optimizer is introduced to accelerate model training and convergence speed. Finally, the model structure is optimized. A multi-layer feature fusion learning structure is put forward wherein the characterization capabilities of each layer can be fully used to improve the generalization ability of the model. In the experimental verification, a laboratory self-made bearing vibration signal dataset was used. The dataset included healthy bearings, nine single faults of different types and sizes, and three different types of composite fault signals. The results of load 0 kN and 1 kN both indicate that the proposed model has better diagnostic accuracy, with an average of 98.15% and 96.15%, compared with the traditional stacked autoencoder, artificial neural network, deep belief network, and standard CDBN. With improved diagnostic accuracy, the proposed model realizes reliable and effective qualitative and quantitative diagnosis of bearing faults.
Highlights
With the explosive progress of modern science and industry, machinery and equipment in fields such as aerospace, rail, and wind power are becoming faster, more automated, and meticulous than before
Traditional vibration signal processing and analysis methods rely on certain professional skills; Existing shallow machine learning methods rely on the accuracy of manual feature extraction; Improper selection of parameters for standard deep learning models can result in failure to effectively converge, diagnostic accuracy is difficult to guarantee; Existing research on the quantitative diagnosis of bearing fault is relatively inadequate compared with that on qualitative diagnosis
Proposed by Lee in 2009, a convolutional deep belief network (CDBN) is a network model that consists of a Convolutional RBM (CRBM)
Summary
With the explosive progress of modern science and industry, machinery and equipment in fields such as aerospace, rail, and wind power are becoming faster, more automated, and meticulous than before. Proposed a novel deep learning method called improved convolutional neural networks and support vector machines with data fusion for intelligent fault diagnosis. Traditional vibration signal processing and analysis methods rely on certain professional skills; Existing shallow machine learning methods rely on the accuracy of manual feature extraction; Improper selection of parameters for standard deep learning models can result in failure to effectively converge, diagnostic accuracy is difficult to guarantee; Existing research on the quantitative diagnosis of bearing fault is relatively inadequate compared with that on qualitative diagnosis. It can be concluded that the general deep models have challenges: (1) a suitable signal preprocessing method is needed to enhance features; (2) necessary measures need to be taken during the model training process to make it more stable; (3) the models should determine the fault type, and the fault degree In response to these problems, a CDBN has the superiority of fast calculations and feature extraction.
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