Tool condition monitoring in milling process using multifractal detrended fluctuation analysis and support vector machine

2020 ◽  
Vol 110 (5-6) ◽  
pp. 1445-1456
Author(s):  
Jingchao Guo ◽  
Anhai Li ◽  
Rufeng Zhang
Keyword(s):  
Support Vector Machine ◽  
Condition Monitoring ◽  
Detrended Fluctuation Analysis ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Tool Condition ◽  
Detrended Fluctuation

scholarly journals Multifractal Analysis for Soft Fault Feature Extraction of Nonlinear Analog Circuits

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Xinmiao Lu ◽  
Hong Zhao ◽  
Haijun Lin ◽  
Qiong Wu
Keyword(s):  
Support Vector Machine ◽  
Analog Circuits ◽  
Detrended Fluctuation Analysis ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Soft Fault ◽  
Nonlinear Analog Circuits ◽  
Detrended Fluctuation ◽  
Nonlinear Analog

Aiming at the nonstationarity and nonlinearity of soft fault signals of nonlinear analog circuits, the use of multifractal detrended fluctuation analysis can effectively reveal the dynamic behavior hidden in multiscale nonstationary signals. This paper adopts a new method that uses multifractal detrended fluctuation analysis to calculate the multifractal singularity spectrum of soft fault signals of nonlinear analog circuits. Moreover, this method endows the parameters of the spectrum with definite physical meanings including width, maximum singular index, minimum singular index, and corresponding singularity index of the extreme point. Therefore, this method can be applied to characterize the internal dynamic mechanism of the soft fault signals of nonlinear analog circuits, making it suitable for the feature extraction of fault circuits. All multifractal feature parameters can be organized into a feature set, which will be then input to a support vector machine, and fault detection for the nonlinear analog circuit can be conducted via the support vector machine.

Tool condition monitoring system based on support vector machine and differential evolution optimization

2016 ◽  
Vol 231 (5) ◽  
pp. 805-813 ◽  
Author(s):  
Guo F Wang ◽  
Qing L Xie ◽  
Yan C Zhang
Keyword(s):  
Support Vector Machine ◽  
Tool Wear ◽  
Differential Evolution ◽  
Condition Monitoring ◽  
Support Vector Machine Classifier ◽  
Tool Condition Monitoring ◽  
Support Vector ◽  
Grid Search ◽  
Tool Condition ◽  
Condition Monitoring System

A tool condition monitoring system based on support vector machine and differential evolution is proposed in this article. In this system, support vector machine is used to realize the mapping between the extracted features and the tool wear states. At the same time, two important parameters of the support vector machine which are called penalty parameter C and kernel parameter [Formula: see text] are optimized simultaneously based on differential evolution algorithm. In order to verify the effectiveness of the proposed system, a multi-tooth milling experiment of titanium alloy was carried out. Cutting force signals related to different tool wear states were collected, and several time domain and frequency domain features were extracted to depict the dynamic characteristics of the milling process. Based on the extracted features, the differential evolution-support vector machine classifier is constructed to realize the tool wear classification. Moreover, to make a comparison, empirical selection method and four kinds of grid search algorithms are also used to select the support vector machine parameters. At the same time, cross validation is utilized to improve the robustness of the classifier evaluation. The results of analysis and comparisons show that the classification accuracy of differential evolution-support vector machine is higher than empirical selection-support vector machine. Moreover, the time consumption of differential evolution-support vector machine classifier is 5 to 12 times less than that of grid search-support vector machine.

Classification of Epileptic EEG Based on Detrended Fluctuation Analysis and Support Vector Machine

2011 ◽  
Vol 27 (2) ◽  
pp. 175-182 ◽  
Author(s):  
Dongmei CAI ◽  
Weidong ZHOU ◽  
Shufang LI ◽  
Jiwen WANG ◽  
Guijuan JIA ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Detrended Fluctuation Analysis ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Detrended Fluctuation

scholarly journals Multifractal Detrended Fluctuation Analysis of Insole Pressure Sensor Data to Diagnose Vestibular System Disorders

2021 ◽  
Author(s):  
Batuhan Günaydın ◽  
Serhat İkizoğlu
Keyword(s):  
Vestibular System ◽  
Pressure Sensor ◽  
Detrended Fluctuation Analysis ◽  
Human Life ◽  
Sensor Data ◽  
Support Vector ◽  
Substantial Part ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation

Abstract The vestibular system (VS) is a sensory system that has a vital function in human life by serving to maintain balance. In this study, multifractal detrended fluctuation analysis (MFDFA) is applied to insole pressure sensor data collected from subjects in order to extract features to identify diseases related to VS dysfunction. We use the multifractal spectrum width as the feature to distinguish between healthy and diseased people. It is observed that multifractal behavior is more dominant and thus the spectrum is wider for healthy subjects, where we explain the reason as the long-range correlations of the small and large fluctuations of the time series for this group. We directly process the instantaneous pressure values to extract features in contrast to studies in the literature where gait analysis is based on investigation of gait dynamics (stride time, stance time, etc.) requiring long gait cycles. Thus, as the main innovation of this work, we detrend the data to give meaningful information even for a relatively short-duration gait cycle. Extracted feature set was input to fundamental classification algorithms where the Support-Vector-Machine (SVM) performed best with an accuracy of 98.2% for the binary classification as healthy or suffering. This study is a substantial part of a big project where we finally aim to identify the specific VS disease that causes balance disorder and also determine the stage of the disease, if any. Within this scope, the achieved performance gives high motivation to work more deeply on the issue.

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