Modeling and analysis of tool wear prediction based on SVD and BiLSTM

Wu, Xiaoqiang; Li, Jia; Jin, Yongquan; Zheng, Shibao

doi:10.1007/s00170-019-04916-3

Cited by 50 publications

(17 citation statements)

References 15 publications

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“…Linear algorithms transform a high-dimensional feature space into a lower dimensional feature space with a linear combination of the original dimensions. Principal component analysis (PCA) [ 61 , 194 , 195 ], singular value decomposition (SVD) [ 196 ], linear discriminant analysis (LDA) [ 197 ], Fisher discriminant analysis (FDA) [ 198 ], Fisher discriminant ratio (FDR) [ 199 ], factor analysis (FA) [ 200 ], and independent component analysis (ICA) [ 201 ] are examples of linear feature transformation algorithms. On the other hand, nonlinear algorithms, such as kernel PCA (KPCA) [ 202 , 203 ], probabilistic kernel FA (PKFA) [ 200 ], kernel Fisher discriminant analysis FDA (KFDA) [ 204 ], and isometric mapping (ISOMAP) [ 205 ], nonlinearly transform a high-dimensional feature space into a lower space.…”

Section: Signal Processing Techniquesmentioning

confidence: 99%

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Mohamed

Hassan

M’Saoubi³

et al. 2022

Sensors

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In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the way for automated machining through monitoring the state of the cutting tool, including the occurrences of wear, cracks, chipping, and breakage, with the aim of improving the efficiency and economics of the machining process. This article reviews the state-of-the-art TCM system components, namely, means of sensing, data acquisition, signal conditioning and processing, and monitoring models, found in the recent open literature. Special attention is given to analyzing the advantages and limitations of current practices in developing wireless tool-embedded sensor nodes, which enable seamless implementation and Industrial Internet of Things (IIOT) readiness of TCM systems. Additionally, a comprehensive review of the selection of dimensionality reduction techniques is provided due to the lack of clear recommendations and shortcomings of various techniques developed in the literature. Recent attempts for TCM systems’ generalization and enhancement are discussed, along with recommendations for possible future research avenues to improve TCM systems accuracy, reliability, functionality, and integration.

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Section: Signal Processing Techniquesmentioning

confidence: 99%

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Mohamed

Hassan

M’Saoubi³

et al. 2022

Sensors

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“…It can be known from the research results that the decomposition of the waveform has a good effect on the prediction of time series. Wu et al [33] realized singular value decomposition, reconstructed the original cutting force signal of the tool, and then used BiLSTM to predict the feature subsignal, thereby effectively improving the prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Solar Irradiance Prediction Based on Multichannel LSTM Neural Networks Using Edge-Based IoT System

Jin

Chen

et al. 2022

Wireless Communications and Mobile Computing

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Most photovoltaic power generation methods use global level irradiance (GHI) as the main input and output. However, randomness, instability, and intermittency are the main factors that seriously degrade the solar irradiance prediction results. Traditional data-driven prediction models are difficult for accurate predictions. In this study, a multichannel deep learning model named multichannel, wavelet transform combining convolutional neural network and bidirectional long short-term memory (MC-WT-CBiLSTM) framework-based edge computing and IoT system is proposed to improve the GHI prediction accuracy. The solar irradiance data is decomposed by wavelet transform to reduce data complexity. Each decomposed component is inputted into the multichannel MC-CBiLSTM deep learning framework for forecasting and combined to produce the final results. The comparison with existing solar irradiance forecasting methods shows that the proposed MC-WT-CBiLSTM deep learning framework has obvious advantages in the prediction of various time horizons.

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“…Shao et al [24] constructed a convolutional deep belief network for fault diagnosis of rolling bearing,which used compressed sensing (CS) for reducing data amount. Wu et al [25] utilized bidirectional long short-term memory neural network (BiLSTM) to deal with singular value decomposition features to predict current tool wear value. Zhao et al [26] proposed a deep residual network with dynamically weighted wavelet coefficients for planetary gearbox fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%