The Application of Nonstandard Support Vector Machine in Tool Condition Monitoring System

Sun, J.; Gao, Hong; Rahman, M.; Wong, Yoke San

doi:10.1109/delta.2004.10017

Cited by 20 publications

(7 citation statements)

References 13 publications

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“…Finally, cross-validation for testing is performed to evaluate the predictive performance of the surface roughness via vibration signal datasets after training datasets. Using the afore-described methods for choosing the test dataset numbers, dataset numbers (3,13,23,33,43), (4,14,24,34,44), (5,15,25,35,45), (6,16,26,36,46), and (7,17,27,37,47) are chosen for testing the datasets to account for cross-validation in the testing process. The mean error results of the dataset cross-validation is provided to evaluate the predictive accuracy of the trained model.…”

Section: -D Cnnmentioning

confidence: 99%

“…The methods most commonly used for feature extraction methods are time-domain, frequency domain, and time-frequency domain methods. The extracted features are fed into the classifier, such as a support vector machine (SVM) [26][27][28] or a neural network (NN) [29,30]. N. N. Bhat [26] proposed the use of the SVM technique for classifying tool wear states of surface images.…”

Section: Introductionmentioning

confidence: 99%

“…J. Sun et al [27] used the SVM approach to identify tool flank wear. S. Cho [28] proposed the use of the SVM algorithm to identify tool breakage abnormalities.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis

et al. 2019

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The use of surface roughness (Ra) to indicate product quality in the milling process in an intelligent monitoring system applied in-process has been developing. From the considerations of convenient installation and cost-effectiveness, accelerator vibration signals combined with deep learning predictive models for predicting surface roughness is a potential tool. In this paper, three models, namely, Fast Fourier Transform-Deep Neural Networks (FFT-DNN), Fast Fourier Transform Long Short Term Memory Network (FFT-LSTM), and one-dimensional convolutional neural network (1-D CNN), are used to explore the training and prediction performances. Feature extraction plays an important role in the training and predicting results. FFT and the one-dimensional convolution filter, known as 1-D CNN, are employed to extract vibration signals’ raw data. The results show the following: (1) the LSTM model presents the temporal modeling ability to achieve a good performance at higher Ra value and (2) 1-D CNN, which is better at extracting features, exhibits highly accurate prediction performance at lower Ra ranges. Based on the results, vibration signals combined with a deep learning predictive model could be applied to predict the surface roughness in the milling process. Based on this experimental study, the use of prediction of the surface roughness via vibration signals using FFT-LSTM or 1-D CNN is recommended to develop an intelligent system.

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Section: -D Cnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis

et al. 2019

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“…In this research, SVM-BN was developed first all to classify the error into groups depending on the operating condition and then carry out a mapping of the temperature profile with the measured error. The other research was carried out by Sun et al (33,34) who classified tool wear using SVM based on manufacturing consideration. This research was aimed to propose a new performance evaluation function for tool condition monitoring (TCM).…”

Section: Machine Toolsmentioning

confidence: 99%

Support Vector Machine for Machine Fault Diagnosis and Prognosis

Yang

Widodo

2008

JSDD

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This paper presents a survey of fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and development of SVM in machine fault diagnosis. Numerous methods have been developed based on intelligent system. However, the use of SVM for machine fault diagnosis is still rare. In addition, this paper introduces the feasibility of SVM based on regression (SVR) for machine prognosis system. The proposed method is addressed to predict the upcoming state of machine based on previous condition. The viability of developed system is evaluated by using trending data of low methane compressor acquired from condition monitoring routine. The results show that SVR has potential and promise for reliable and robust forecasting tool in machine prognosis system.

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“…Dongfeng et al [28] presented a tool wear predictive model combining least square support vector machines (LS-SVM) and principal component analysis (PCA) technique. Finally, Sun et al [31] used the SVM in a turning process where the level of wear was classified in a binary class. Thus, the nonlinear feature reduction and nonlinear modeling aspects are not thoroughly addressed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Health assessment and life prediction of cutting tools based on support vector regression

Benkedjouh¹,

et al. 2013

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The integrity of machining tools is important to maintain a high level of surface quality. The wear of the tool can lead to poor surface quality of the workpiece and even to damage of the machine. Furthermore, in some applications such as aeronautics and precision engineering, it is preferable to change the tool earlier rather than to loose the workpiece because of its high price compared to the tool's one. Thus, to maintain a high quality of the manufactured pieces, it is necessary to assess and predict the level of wear of the cutting tool. This can be done by using condition monitoring and prognostics. The aim is then to estimate and predict the amount of wear and calculate the remaining useful life of the cutting tool. This paper presents a method for tool condition assessment and life prediction. The method is based on nonlinear feature reduction and support vector regression. The number of original features extracted from the monitoring signals is first reduced. These features are then used to learn nonlinear regression models to estimate and predict the level of wear. The method is applied on experimental data taken from a set of cuttings and simulation results are given. These results show that the proposed method is suitable for assessing the wear evolution of the cutting tools and predicting their remaining useful life. This information can then be used by the operators to take appropriate maintenance actions.

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The Application of Nonstandard Support Vector Machine in Tool Condition Monitoring System

Cited by 20 publications

References 13 publications

Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis

Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis

Support Vector Machine for Machine Fault Diagnosis and Prognosis

Health assessment and life prediction of cutting tools based on support vector regression

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