Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84935
Title: Machine health condition prediction via online dynamic fuzzy neural networks
Authors: Pan, Yongping
Er, Meng Joo
Li, Xiang
Yu, Haoyong
Gouriveau, Rafael
Keywords: Fuzzy neural network
Machine health condition
Issue Date: 2014
Source: Pan, Y., Er, M. J., Li, X., Yu, H., & Gouriveau, R. (2014). Machine health condition prediction via online dynamic fuzzy neural networks. Engineering Applications of Artificial Intelligence, 35, 105-113.
Series/Report no.: Engineering Applications of Artificial Intelligence
Abstract: Machine health condition (MHC) prediction is useful for preventing unexpected failures and minimizing overall maintenance costs in condition-based maintenance. The neural network (NN)-based data-driven method has been considered to be promising for MHC prediction due to the adaptability, nonlinearity and universal approximation capability of NNs. This paper presents an online MHC prediction approach using online dynamic fuzzy NNs (OD-FNNs) with structure and parameters learning. To meet the requirement of real-time application, the original OD-FNN is simplified based on an extreme learning machine technique as follows: (1) initial fuzzy rules are randomly generated without the knowledge of training data; (2) fuzzy rules are added and pruned uniformly by fired strength-based criteria; (3) antecedent parameters are fixed after generation so that only consequent parameters are updated online. The modified OD-FNN is particularly suitable for MHC prediction since: (1) fuzzy rules can evolve as new training datum arrives, which enables us to cope with non-stationary processes in MHC; (2) learning mechanisms applied are simple and efficient for real-time implementation. The validity and superiority of the proposed MHC prediction approach has been evaluated by real-world monitoring data from the accelerated bearing life.
URI: https://hdl.handle.net/10356/84935
http://hdl.handle.net/10220/40893
ISSN: 0952-1976
DOI: 10.1016/j.engappai.2014.05.015
Rights: © 2014 Elsevier.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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