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Abstract
Aiming at the problems of poor efficiency of the intelligent fault diagnosis method of the main reducer and the poor effectiveness of multichannel data fusion, this paper proposes a multichannel data fusion method based on deep belief networks and random forest fusion for fault diagnosis. Multiple deep belief networks (MDBNs) are constructed to obtain deep representative features from multiple modalities of multichannel data. Random forest can fuse deep representative features achieved from MDBNs to construct the model of multiple deep belief networks fusion (MDBNF). The proposed method is applied to fault diagnosis of the main reducer and evaluation of the performance. Multiple deep belief network model fusions (MD BN F) are constructed to improve the multichannel data fusion effect. Single sensory data, multichannel data, and two intelligent models based on support vector machine and deep belief networks are used as comparison in the experiments. The results indicate that the classification accuracy of the test set collected by sensor 1 and sensor 2 is 88.35% and 88.73%, respectively. The comparison results show that the method has good convergence. The data fusion of the proposed diagnostic model can effectively improve the correlation between the collected vibration signals and the failure mode, thereby improving the diagnostic performance by nearly 8%, representing improved diagnostic accuracy.
Highlights
As the crucial part of the rear axle, the condition of the main reducer has a direct impact on the level of vibration, safety and comfort, and any fault of the main reducer may lead to production downtime, economic loss and human injury [1,2]
Aiming to solve the challengers of fault diagnosis based on multichannel data, this paper proposes a multichannel data fusion method based on multiple deep belief networks (MDBNs) and random forest fusion for fault diagnosis
In order to effectively fuse deep representative features in the form of Equation (8) extracted from multichannel vibration signals by using MDBNs, we propose to use random forest to fuse these features and achieve the final result
Summary
As the crucial part of the rear axle, the condition of the main reducer has a direct impact on the level of vibration, safety and comfort, and any fault of the main reducer may lead to production downtime, economic loss and human injury [1,2]. Fault diagnosis based on vibration signals is the most-used way of machinery condition monitoring and fault diagnosis [3]. Utilized bi-spectrum analysis based on modulating signal to acquire features of gear vibration signal [4]. Marnatha et al employed vibration signals and statistical parameters to detect local fault of helical gear tooth [5]. Yang et al employed ensemble empirical mode decomposition to extract features of gear vibration signals [6]. Jena et al used active noise cancellation and adaptive wavelet transform in gear fault diagnosis [7]
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