Slope displacement prediction based on morphological filtering

2013 ◽  
Vol 20 (6) ◽  
pp. 1724-1730 ◽  
Author(s):  
Qi-yue Li ◽  
Jie Xu ◽  
Wei-hua Wang ◽  
Zuo-peng Fan
2021 ◽  
pp. 73-92
Author(s):  
Wengang Zhang ◽  
Yanmei Zhang ◽  
Xin Gu ◽  
Chongzhi Wu ◽  
Liang Han

Measurement ◽  
2019 ◽  
Vol 134 ◽  
pp. 634-648 ◽  
Author(s):  
Chengyin Liu ◽  
Zhaoshuo Jiang ◽  
Xishuang Han ◽  
Wanxi Zhou

2012 ◽  
Vol 246-247 ◽  
pp. 370-376
Author(s):  
Wei Yu ◽  
Jing Lu Cai ◽  
Feng Ping An

Slope displacement time series prediction model,a combination of Local mean decomposition(LMD) and BP neural network is presented.By selecting train samples on the basis of monitoring data on slope displacement and conducting an adaptive decomposing, several production function is obtained.After that, BP neural network is used to forecast the PF and finally adding it all up and the result is the predicton of slope displacement. BP neural network is used to optimize the parameters so as to improve the forecast accuracy.The model is put into application on the slope displacement forecasting of the permanent lock slope.The case study shows that the prediction result is of high accuracy, scientifically valid and has potential value in the field of slope displacement time series prediction.


2021 ◽  
Vol 1085 (1) ◽  
pp. 012038
Author(s):  
Nuja M Unnikrishnan ◽  
Mini Tom ◽  
V Bino Sebastian ◽  
K V Thomas

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 52
Author(s):  
Tongtong Liu ◽  
Lingli Cui ◽  
Chao Zhang

The turn domain resampling (TDR) method is proposed in the paper on the basis of the existing angle domain resampling for solving the problem of non-fixed fault frequency under variable working conditions. TDR can select the appropriate sampling order according to the influence of frequency conversion, which avoided the error caused by the spline interpolation method. It can provide accurate parameters for the subsequent calculation of the equivalent frequency order. Variable multi-scale morphological filtering (VMSMF) method is proposed for the purpose of further reducing the interference of noise in resampling signal to feature extraction. VMSMF adaptively selects structural elements according to the parameter change of impact signal to make its scale more targeted. It only needs to calculate once using the optimal structural unit for a particular impact, and the filtering accuracy and operating efficiency have been greatly improved. The main steps of this article are as follows. First, the TDR is used to resample the original signal as to get the resampling signal which is still submerged by the strong noise. In the second step, VMSMF is used to filter the resampling signal to obtain the signal with less noise interference. Finally, the fault characteristics of the filtering signal was extracted and compared with the possible fault frequency calculated by the sampling parameters provided by resampling, so as to determine the fault type of the planetary gearbox. By analyzing the simulation signal and the experimental signal respectively, this method can find out the corresponding fault characteristics effectively.


Measurement ◽  
2021 ◽  
Vol 176 ◽  
pp. 109163
Author(s):  
Bingyan Chen ◽  
Dongli Song ◽  
Weihua Zhang ◽  
Yao Cheng ◽  
Zhiwei Wang

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