Degradation trend prediction of rotating machineries based on improved WD-GM-ANFIS

Tao Shu; Yichi Zhang; Xincheng Song; Rixian Ding

doi:10.1117/12.2640277

23 May 2022 Degradation trend prediction of rotating machineries based on improved WD-GM-ANFIS

Tao Shu, Yichi Zhang, Xincheng Song, Rixian Ding

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 122542O (2022) https://doi.org/10.1117/12.2640277
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

Rotating machineries account for 80 percent of the total large mechanical equipment. To optimize their work efficiency, research of the simulation on improving the accuracy of life prediction which is about key components in rotating machineries was conducted. Firstly, Confidential Value (CV) can quantify working condition. And then make the training set of the original CV data be in Wavelet Decomposition (WD). Next, the data transformation and accumulative integral can improve GM (1,1) by the optimization of the smooth ratio and background value; improved GM (1,1) is in combination with Adaptive Neuro-Fuzzy Inference System (ANFIS) for new prediction model to predict the CV. Ultimately, improved WD-GM-ANFIS is compared with GM(1,1), ANFIS and LSTM network in three performance indicators, meanwhile, contrast improved WD-GM-ANFIS with Full Convolutional Layer Neural Networks (FCLNN) algorithm and Unscented Particle Filter (UPF) algorithm in failure time prediction. Three indicators' averages of the new model are all better than the other three single models; failure time prediction of the new model is more precise than the two improved algorithms.

Citation Download Citation

Tao Shu, Yichi Zhang, Xincheng Song, and Rixian Ding "Degradation trend prediction of rotating machineries based on improved WD-GM-ANFIS", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 122542O (23 May 2022); https://doi.org/10.1117/12.2640277

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KEYWORDS

Data modeling

Performance modeling

Wavelets

Instrument modeling

Evolutionary algorithms

Mathematical modeling

Neural networks

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