A new time–space attention mechanism driven multi-feature fusion method for tool wear monitoring

Feng, Tao; Guo, Liang; Gao, Hongli; Chen, Tao; Yu, Yaoxiang; Li, Changgen

doi:10.1007/s00170-022-09032-3

Cited by 26 publications

(11 citation statements)

References 41 publications

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“…To verify the performance of the proposed model, four typical models are selected for comparison, including CNN, 24 deep bidirectional LSTM (DBiLSTM), 19 convolutional neural network with stacked bidirectional and unidirectional LSTM network (CNN-SBULSTM) 33 and a hybrid model with time–space attention mechanism (CNN-LSTM with attention). 34 The first model uses a single CNN to perform both feature extraction and regression tasks. The DBiLSTM performs regression prediction through two layers of bidirectional LSTM.…”

Section: Model Validationmentioning

confidence: 99%

Tool remaining useful life prediction considering wear state based on hybrid attention network

Wu,

Li,

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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Accurate prediction of the remaining useful life for the cutting tool is a key part of the predictive maintenance of computer numerical control machines. However, the wide variety of tools makes the process of modeling different tool wear regularities redundant and cumbersome. In addition, it is difficult to deal with the input characteristics of multi-sensor monitoring signals in a targeted manner. To solve the above problems, a hybrid predictive model with squeeze-and-excitation (SE) module is proposed. Combined with adaptive feature extraction based on convolutional neural network and observation based on bidirectional gated recurrent unit, accurate multivariate regression prediction is achieved. The SE module enhances the focus on crucial features. Finally, through the design of the tool wear experiment and the combination of the public dataset, the accuracy and generalization ability of the proposed model are verified under different tool types and different working conditions.

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Section: Model Validationmentioning

confidence: 99%

Tool remaining useful life prediction considering wear state based on hybrid attention network

Wu,

Li,

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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show abstract

“…To describe the correlation between the features more directly, the absolute value of R is adopted to construct the RS features of the time and frequency domains. Let ft indicate the feature series sampled at time t and f0 indicate the time series of the initial observation time; then, the RS features can be calculated according to formula (2).…”

Section: Similarity Feature Construction Based On Pearson's Correlati...mentioning

confidence: 99%

“…With the increasing complexity of sampling data, supervised data-driven methods have been extensively researched [1][2][3]. The core idea is to train neural network models using HI and RUL labels, which can then be used to predict the RUL of bearings [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction for rolling bearings based on similarity feature fusion and convolutional neural network

Nie

Zhang

et al. 2022

J Braz. Soc. Mech. Sci. Eng.

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As a critical content of condition-based maintenance (CBM) for mechanical systems, remaining useful life (RUL) prediction of rolling bearing attracts extensive attention to this day. Through mining the bearing degradation rule from operating data, the deep learning method is often used to perform RUL prediction. However, due to the complexity of operating data, it is usually difficult to establish a satisfactory deep learning model for accurate RUL prediction. Thus, a novel convolutional neural network (CNN) prediction method based on similarity feature fusion is proposed. In this paper, the similarity features are extracted based on the correlation between statistical features and time series. After sensitive feature screening, eligible features are applied to develop a health indicator (HI), which can be used to define the bearing failure stages and reduces the complexity of the CNN model. Subsequently, a one-dimensional CNN is established to predict the RUL of bearing, and the HI is utilized to train the prediction model. The proposed approach is verified by FEMTO bearing datasets and IMS bearing datasets. And the experimental results reveal the superiority and effectiveness of the feature fusion-based CNN method in constructing HI and accurate RUL prediction.

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“…They have the potential to outperform traditional analytical and empirical models by capturing intricate nonlinearities in the machining process. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention networks, and so on, have skyrocketed in the mechanical field [ 22 , 23 , 24 ]. These models, primarily known for their remarkable achievements in areas such as computer vision and language translation, have found substantial relevance in the realm of mechanical production as well, underpinning the evolution of smart manufacturing.…”

Section: Introductionmentioning

confidence: 99%

A Novel Piecewise Cubic Hermite Interpolating Polynomial-Enhanced Convolutional Gated Recurrent Method under Multiple Sensor Feature Fusion for Tool Wear Prediction

He,

Yuan,

Lei

et al. 2024

Sensors

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The monitoring of the lifetime of cutting tools often faces problems such as life data loss, drift, and distortion. The prediction of the lifetime in this situation is greatly compromised with respect to the accuracy. The recent rise of deep learning, such as Gated Recurrent Unit Units (GRUs), Hidden Markov Models (HMMs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention networks, and Transformers, has dramatically improved the data problems in tool lifetime prediction, substantially enhancing the accuracy of tool wear prediction. In this paper, we introduce a novel approach known as PCHIP-Enhanced ConvGRU (PECG), which leverages multiple—feature fusion for tool wear prediction. When compared to traditional models such as CNNs, the CNN Block, and GRUs, our method consistently outperformed them across all key performance metrics, with a primary focus on the accuracy. PECG addresses the challenge of missing tool wear measurement data in relation to sensor data. By employing PCHIP interpolation to fill in the gaps in the wear values, we have developed a model that combines the strengths of both CNNs and GRUs with data augmentation. The experimental results demonstrate that our proposed method achieved an exceptional relative accuracy of 0.8522, while also exhibiting a Pearson’s Correlation Coefficient (PCC) exceeding 0.95. This innovative approach not only predicts tool wear with remarkable precision, but also offers enhanced stability.

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A new time–space attention mechanism driven multi-feature fusion method for tool wear monitoring

Cited by 26 publications

References 41 publications

Tool remaining useful life prediction considering wear state based on hybrid attention network

Tool remaining useful life prediction considering wear state based on hybrid attention network

Remaining useful life prediction for rolling bearings based on similarity feature fusion and convolutional neural network

A Novel Piecewise Cubic Hermite Interpolating Polynomial-Enhanced Convolutional Gated Recurrent Method under Multiple Sensor Feature Fusion for Tool Wear Prediction

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