Cross-recurrence plot quantification analysis of input and output signals for the detection of chatter in turning

Elias, Jacob; Namboothiri, V. N. N.

doi:10.1007/s11071-013-1124-0

Cited by 29 publications

(12 citation statements)

References 16 publications

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“…Additionally, Nair, Thampi and Sujith [80] showed that RQA measures in swirl-stabilized and buff-body-stabilized combustors could reliably quantify changes in dynamics as a function of Reynolds number and where a potential candidate for early-warning indication of impending combustion. Elias and Namboothiri [81] used cross-recurrence analysis to identify the transition from regular cutting to chatter cutting of metal plates irrespective of the choice of metal. Harris et al [82] showed that RQA measures were able to characterize the dynamics of bistable cross-shaped laminated plate energy harvesters and proposed recurrence plots as an efficient approach to distinguish different classes of dynamics such as single-well periodic dynamics, chaotic snapthrough, and continuous snap-through.…”

Section: Recurrence Plots In Engineering Researchmentioning

confidence: 99%

A Brief Introduction to Nonlinear Time Series Analysis and Recurrence Plots

Goswami

2019

Vibration

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Nonlinear time series analysis gained prominence from the late 1980s on, primarily because of its ability to characterize, analyze, and predict nontrivial features in data sets that stem from a wide range of fields such as finance, music, human physiology, cognitive science, astrophysics, climate, and engineering. More recently, recurrence plots, initially proposed as a visual tool for the analysis of complex systems, have proven to be a powerful framework to quantify and reveal nontrivial dynamical features in time series data. This tutorial review provides a brief introduction to the fundamentals of nonlinear time series analysis, before discussing in greater detail a few (out of the many existing) approaches of recurrence plot-based analysis of time series. In particular, it focusses on recurrence plot-based measures which characterize dynamical features such as determinism, synchronization, and regime changes. The concept of surrogate-based hypothesis testing, which is crucial to drawing any inference from data analyses, is also discussed. Finally, the presented recurrence plot approaches are applied to two climatic indices related to the equatorial and North Pacific regions, and their dynamical behavior and their interrelations are investigated.

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Section: Recurrence Plots In Engineering Researchmentioning

confidence: 99%

A Brief Introduction to Nonlinear Time Series Analysis and Recurrence Plots

Goswami

2019

Vibration

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“…However, these models often do not account for the effect of the changing dynamics or for highly complex cutting operations. This led to the emergence of in-situ methods for chatter detection based on instrumenting the cutting center with sensors and analyzing the resulting signals [6,7,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition

Yesilli

Khasawneh

Otto

2020

CIRP Journal of Manufacturing Science and Technology

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The increasing availability of sensor data at machine tools makes automatic chatter detection algorithms a trending topic in metal cutting. Two prominent and advanced methods for feature extraction via signal decomposition are Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition (EEMD). We apply these two methods to time series acquired from an acceleration sensor at the tool holder of a lathe. Different turning experiments with varying dynamic behavior of the machine tool structure were performed. We compare the performance of these two methods with Support Vector Machine (SVM) classifier combined with Recursive Feature Elimination (RFE). We also show that the common WPT-based approach of choosing wavelet packets with the highest energy ratios as representative features for chatter does not always result in packets that enclose the chatter frequency, thus reducing the classification accuracy. Further, we test the transfer learning capability of each of these methods by training the classifier on one of the cutting configurations and then testing it on the other cases. It is found that when training and testing on data from the same cutting configuration both methods yield high accuracies reaching in one of the cases as high as 94% and 91%, respectively, for WPT and EEMD. However, EEMD is shown to outperform WPT in transfer learning applications with accuracy of up to 84%. Therefore, for systems where the movement of the cutting center leads to significant variations in the stiffness of the machine-tool system, we recommend using EEMD over WPT for training a classifier. This is because EEMD retains higher accuracy rates in comparison to WPT when the input data stream deviates from the data that was used to train the classifier.

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“…Since the information from CRP is embedded in a visual representation of the data, cross recurrence quantification analysis (CRQA) has been developed. With CRQA, the number and duration of recurrences of a nonlinear, dynamic system can be presented by examining at its phase trajectory and quantify it [8].…”

Section: Introductionmentioning

confidence: 99%

Methodology Proposal of EMG Hand Movement Classification Based on Cross Recurrence Plots

Aceves-Fernández

Ramos-Arreguín

Gorrostieta-Hurtado

et al. 2019

Computational and Mathematical Methods in Medicine

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Dealing with electromyography (EMG) signals is often not simple. The nature of these signals is nonstationary, noisy, and high dimensional. These EMG characteristics make their predictability even more challenging. Cross recurrence plots (CRPs) have demonstrated in many works their capability of detecting very subtle patterns in signals often buried in a noisy environment. In this contribution, fifty subjects performed ten different hand movements with each hand with the aid of electrodes placed in each arm. Furthermore, the nonlinear features of each subject's signals using cross recurrence quantification analysis (CRQA) have been performed. Also, a novel methodology is proposed using CRQA as the mainstream technique to detect and classify each of the movements presented in this study. Additional tools were presented to determine to which extent this proposed methodology is able to avoid false classifications, thus demonstrating that this methodology is feasible to classify surface EMG (SEMG) signals with good accuracy, sensitivity, and specificity. Lastly, the results were compared with traditional machine learning methods, and the advantages of using the proposed methodology above such methods are highlighted.

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Cross-recurrence plot quantification analysis of input and output signals for the detection of chatter in turning

Cited by 29 publications

References 16 publications

A Brief Introduction to Nonlinear Time Series Analysis and Recurrence Plots

A Brief Introduction to Nonlinear Time Series Analysis and Recurrence Plots

On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition

Methodology Proposal of EMG Hand Movement Classification Based on Cross Recurrence Plots

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