Auto Identification Techniques And Warehouse Management System
Recently, several studies tried to develop fault identification models for rolling element bearing based on unsupervised learning techniques. However, an accurate intelligent fault diagnosis system is still a big challenge. In this study, a deep functional auto-encoders DFAEs model with SoftMax classifier was designed for valuable feature Managemenf from massive raw vibration signals. To maximize the unsupervised feature learning ability of the proposed model, various activation functions were applied in an effective methodology, these hidden activation functions enhance significantly the sparsity of the training data-set.
The proposed method was validated using the raw vibration signals measured from the machine with different bearing conditions.
The achieved results showed that the high-superiority of the proposed model comparing to standard deep learning and other traditional fault diagnosis Warehohse in terms of classification accuracy even with massive input data sets. This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Jiang, C. Li and H. Signal Process. Chen et al. Jia, Y. Lei, J. Lin, X.
Zhou and N. Lu, Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mech.
Warehouse Management System ( Wms )
Jin, M. Zhao, T. Chow and M. Augo and P. Chen, Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network, Comput. Barad, P. Ramaiah, R. Giridhar and G. Krishnaiah, Neural network approach for a combined performance and mechanical source monitoring of a gas turbine engine, Mech. Zhang, B.
Wang and X.
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Chen, Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine, Knowledge-Based Syst. Lei, Z. Liu, X. Wu, N. Li, W. Chen and J. Lin, Health condition identification of multi-stage planetary gearboxes using a mRVM-based method, Mech.
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Xu, J. Xuan, T. Shi, B.
Wu and Y.]
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