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.

Modeling of the dynamic hysteresis in DEAP actuator using an empirical mode decomposition based long-short term memory network

2021 ◽  
pp. 1045389X2098699
Author(s):  
Zhaoguo Jiang ◽  
Yuan Li ◽  
Qinglin Wang
Keyword(s):  
Empirical Mode Decomposition ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Hysteresis Phenomenon ◽  
Short Term ◽  
Term Memory ◽  
Mode Decomposition ◽  
Hysteresis Nonlinearity ◽  
Memory Network ◽  
Long Short Term Memory

As a smart material-based actuator, the dielectric electro-active polymer (DEAP) actuator is widely considered to be a potential driving mechanism for many applications, especially in intelligent bio-inspired robotics. However, the DEAP actuator demonstrates rate-dependent and asymmetrical hysteresis phenomenon which leads to great tracking inaccuracy and even oscillatory response, severely limiting its further development. Feedforward Neural Network (FNN) model has already become a widely used method to describe this kind of strong hysteresis nonlinearity in recent years. However, the FNN has no ability to remember the historical state of long period of time which is also a very important factor to restrict hysteresis phenomenon. In this paper, a novel hybrid model, Long-Short Term Memory (LSTM) network combined with Empirical Mode Decomposition (EMD), is proposed to model the dynamic hysteresis nonlinearity in DEAP actuator. At first, the original control signal sequence is preprocessed into a series of sub-sequence by the EMD method and is reshaped by one-sided dead-zone operator. Then the input space of LSTM is conducted using the original control signal, the sub-sequence, and reshaped signal. Finally, the input space and the displacement signal are applied to train the long-short term memory network. In order to verify the performance of the proposed model, the traditional artificial back propagation neural network (BPNN) model, rate-dependent Prandtl-Ishlinskii (RPI) model, and nonlinear electromechanical (NEM) model are compared from prediction accuracy. The results demonstrate that: (1) the proposed model has a higher prediction accuracy than the traditional artificial BPNN, RPI, and NEM model; and (2) the prediction accuracy of LSTM network is significantly improved by using EMD. Therefore, the long-short term memory network combined with empirical mode decomposition is a competitive method compared to the existing state-of-the-art approach.

scholarly journals Dynamic Displacement Forecasting of Dashuitian Landslide in China Using Variational Mode Decomposition and Stack Long Short-Term Memory Network

2019 ◽  
Vol 9 (15) ◽  
pp. 2951 ◽  
Author(s):  
Yin Xing ◽  
Jianping Yue ◽  
Chuang Chen ◽  
Kanglin Cong ◽  
Shaolin Zhu ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Forecast Accuracy ◽  
Short Term ◽  
Dynamic Displacement ◽  
Term Memory ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Lstm Network

In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. VMD is used to decompose landslide displacement into different displacement subsequences, and the SLSTM network is used to forecast each displacement subsequence. Then, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all displacement subsequences. On the other hand, the SLSTM networks are updated by adding the forecast values into the training set, realizing the dynamic displacement forecasting. The proposed model was verified on the Dashuitian landslide in China. The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)–LSTM network, the proposed model has higher forecast accuracy.

A hybrid method coupling empirical mode decomposition and a long short-term memory network to predict missing measured signal data of SHM systems

2020 ◽  
pp. 147592172093281
Author(s):  
Linchao Li ◽  
Haijun Zhou ◽  
Hanlin Liu ◽  
Chaodong Zhang ◽  
Junhui Liu
Keyword(s):  
Missing Data ◽  
Empirical Mode Decomposition ◽  
Hybrid Method ◽  
Short Term Memory ◽  
Measured Signal ◽  
Short Term ◽  
Term Memory ◽  
Mode Decomposition ◽  
Memory Network ◽  
Long Short Term Memory

Missing data, especially a block of missing data, inevitably occur in structural health monitoring systems. Because of their severe negative effects, many methods that use measured data to infer missing data have been proposed in previous research to solve the problem. However, capturing complex correlations from raw measured signal data remains a challenge. In this study, empirical mode decomposition is combined with a long short-term memory deep learning network for the recovery of the measured signal data. The proposed hybrid method converts the missing data imputation task as a time series prediction task, which is then solved by a “divide and conquer” strategy. The core concept of this strategy is the prediction of the subsequences of the raw measured signal data, which are decomposed by empirical mode decomposition rather than directly predicted, as the decomposition can assist in the modeling of the irregular periodic changes of the measured signal data. In addition, the long short-term memory network in the hybrid model can remember more long-range correlations of subsequences than can the traditional artificial neural network. Three widely used prediction models, namely, the autoregressive integrated moving average, support vector regression, and artificial neural network models, are also implemented as benchmark models. Raw acceleration data collected from a cable-stayed bridge are used to evaluate the performance of the proposed method for missing measured signal data imputation. The recovery results of the measured signal data demonstrate that the proposed hybrid method exhibits excellent performance from two perspectives. First, the decomposition by empirical mode decomposition can improve the accuracy of the core long short-term memory prediction model. Second, the long short-term memory model outperforms other benchmark models because it can fit more microscopic changes of measured values. The experiments conducted in this study also suggest that the change patterns of raw measured signal data are complex, and it is therefore important to extract the features before modeling.

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