A self-data-driven method for remaining useful life prediction of wind turbines considering continuously varying speeds

2022 ◽  
Vol 165 ◽  
pp. 108315
Author(s):  
Naipeng Li ◽  
Pengcheng Xu ◽  
Yaguo Lei ◽  
Xiao Cai ◽  
Detong Kong
Keyword(s):  
Wind Turbines ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Useful Life

Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems

2020 ◽  
pp. 1-1
Author(s):  
Naipeng Li ◽  
Yaguo Lei ◽  
Nagi Gebraeel ◽  
Zhijian Wang ◽  
Xiao Cai ◽  
...  
Keyword(s):  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Sensor Data ◽  
Useful Life

scholarly journals Remaining Useful Life Prediction of the Concrete Piston Based on Probability Statistics and Data Driven

2021 ◽  
Vol 11 (18) ◽  
pp. 8482
Author(s):  
Jie Li ◽  
Yuejin Tan ◽  
Bingfeng Ge ◽  
Hua Zhao ◽  
Xin Lu
Keyword(s):  
Inventory Management ◽  
Regression Models ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Distribution Fitting ◽  
Useful Life ◽  
Concrete Pump Truck ◽  
Concrete Pump ◽  
Actual Life

This paper proposes a method on predicting the remaining useful life (RUL) of a concrete piston of a concrete pump truck based on probability statistics and data-driven approaches. Firstly, the average useful life of the concrete piston is determined by probability distribution fitting using actual life data. Secondly, according to condition monitoring data of the concrete pump truck, a concept of life coefficient of the concrete piston is proposed to represent the influence of the loading condition on the actual useful life of individual concrete pistons, and different regression models are established to predict the RUL of the concrete pistons. Finally, according to the prediction result of the concrete piston at different life stages, a replacement warning point is established to provide support for the inventory management and replacement plan of the concrete piston.

scholarly journals Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

2020 ◽  
Vol 10 (3) ◽  
pp. 1062 ◽  
Author(s):  
Tarek Berghout ◽  
Leïla-Hayet Mouss ◽  
Ouahab Kadri ◽  
Lotfi Saïdi ◽  
Mohamed Benbouzid
Keyword(s):  
Extreme Learning Machine ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Selection Strategy ◽  
Mapping Technique ◽  
Model Parameters ◽  
Aircraft Engines ◽  
Useful Life ◽  
Learning Machine

The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.

A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network

Measurement ◽  
2020 ◽  
Vol 154 ◽  
pp. 107461 ◽  
Author(s):  
Qinglong An ◽  
Zhengrui Tao ◽  
Xingwei Xu ◽  
Mohamed El Mansori ◽  
Ming Chen
Keyword(s):  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Milling Tool ◽  
Useful Life ◽  
Lstm Network
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