Nonlinear time-series modeling of feed drive system based on motion states classification

Yakun Jiang (), Jihong Chen, Huicheng Zhou (), Jianzhong Yang and Guangda Xu
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Yakun Jiang: Huazhong University of Science and Technology
Jihong Chen: Huazhong University of Science and Technology
Huicheng Zhou: Huazhong University of Science and Technology
Jianzhong Yang: Huazhong University of Science and Technology
Guangda Xu: Huazhong University of Science and Technology

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 8, No 9, 1935-1948

Abstract: Abstract This paper proposed a novel modeling method using the running process data, i.e., the reference input positions and the actual output positions, based on the Naïve Bayes method and a nonlinear autoregressive long-short term memory network (i.e., the NAR-LSTM) to address the nonlinear time-series modeling problem for increasing the prediction accuracy of the model of CNC machine feed drive system. A Naïve Bayes based motion states classifier (i.e., the NB-MSC) is proposed to automatically classify the motion states with knowledge of dynamic characteristics of the feed drive system for constructing the submodels of different motion states (startup state, reverse state, etc.). In addition, a model based multi-objective optimization method is presented to extract samples for the training of the NB-MSC. Then by modifying the basic long-short term memory (LSTM) network, the NAR-LSTM network is proposed to construct those submodels. Compared to the existing modeling methods via dynamic analysis, the proposed method is better because it can achieve higher prediction accuracy on highly nonlinear motion states such as the reverse state. To validate the proposed methods, a set of experiments are conducted to prove the feasibility of the feed drive model as well as the advantages of the NB-MSC and the NAR-LSTM network in improving the modeling performance.

Keywords: Feed drive system; Motion states classification; NAR-LSTM network; Time-series modeling; Prediction (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-020-01546-5

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