Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

2021 ◽  
Vol 60 ◽  
pp. 608-619
Author(s):  
Xianli Liu ◽  
Shaoyang Liu ◽  
Xuebing Li ◽  
Bowen Zhang ◽  
Caixu Yue ◽  
...  
Keyword(s):  
Tool Wear ◽  
Short Term Memory ◽  
Tool Wear Monitoring ◽  
Short Term ◽  
Term Memory ◽  
Wear Monitoring ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions

2021 ◽  
Vol 23 (4) ◽  
pp. 612-618 ◽  
Author(s):  
Guoxiao Zheng ◽  
Weifang Sun ◽  
Hao Zhang ◽  
Yuqing Zhou ◽  
Chen Gao
Keyword(s):  
Tool Wear ◽  
Short Term Memory ◽  
Milling Process ◽  
Cutting Conditions ◽  
Short Term ◽  
Term Memory ◽  
Mode Decomposition ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Wear Condition

Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

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