Feature extraction using dominant frequency bands and time-frequency image analysis for chatter detection in milling

Chen, Yun; Li, Huaizhong; Hou, Liang; Bu, Xiangjian

doi:10.1016/j.precisioneng.2018.12.004

Cited by 41 publications

(17 citation statements)

References 26 publications

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“…The acquired signals are often processed in the time, frequency and time-frequency domains (Benkedjouh et al, 2013;Tao et al, 2019). As chatter is a nonlinear and nonstationary process, time-frequency methods, such as short-time frequency transform (STFT) (Chen et al, 2019), empirical mode decomposition (EMD) (Fu et al, 2016), variational mode decomposition (VMD) (Liu et al, 2018) and wavelet-based analysis (Zhang et al, 2016), are popular to disclose chatter characteristics hidden in the time-domain signals. The STFT is based on a fixed window size of Fourier transform theory, and a proper balance between time and frequency resolution cannot be easily found (Albertelli et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Chatter detection for milling using novel p-leader multifractal features

Chen

Hou

et al. 2020

J Intell Manuf

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Chatter in machining results in poor workpiece surface quality and short tool life. A reliable chatter detection method is needed to monitor this self-excited vibration before its complete development. This paper applies multifractal features extracted from a novel p-leader multifractal formalism for chatter detection in milling processes. This novel formalism is able to discover a complex singular behavior rising on unstable chatter signals, and improves chatter detection performance. The multifractal features are selected using a multivariate filter, and assessed in terms of their dynamic monitoring abilities and classification accuracies under wide cutting conditions. The results show that the multifractal features cannot only detect chatter with high accuracy and short computation time, but are also efficient to distinguish stable tests with large amplitudes from unstable tests. A small p is recommended to highlight dynamic variations and obtain high classification accuracies. For further verification, the

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Section: Introductionmentioning

confidence: 99%

Chatter detection for milling using novel p-leader multifractal features

Chen

Hou

et al. 2020

J Intell Manuf

Self Cite

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“…The dominant frequency is the frequency that carries the maximum energy among all frequencies found in an acoustic spectrum [17]. It is expressed in Hertz (Hz).…”

Section: Acoustic Featuresmentioning

confidence: 99%

Audio Feature Engineering for Occupancy and Activity Estimation in Smart Buildings

et al. 2021

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The occupancy and activity estimation are fields that have been severally researched in the past few years. However, the different techniques used include a mixture of atmospheric features such as humidity and temperature, many devices such as cameras and audio sensors, or they are limited to speech recognition. In this work is proposed that the occupancy and activity can be estimated only from the audio information using an automatic approach of audio feature engineering to extract, analyze and select descriptors/variables. This scheme of extraction of audio descriptors is used to determine the occupation and activity in specific smart environments, such that our approach can differentiate between academic, administrative or commercial environments. Our approach from the audio feature engineering is compared to previous similar works on occupancy estimation and/or activity estimation in smart buildings (most of them including other features, such as atmospherics and visuals). In general, the results obtained are very encouraging compared to previous studies.

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“…The feature extraction and ranking techniques proposed by the authors [32] are briefly summarized in this section. In order to identify the dominant frequency bands with high energy, a constructed signal y(t k ) , that is, a sum of signals selected from the stable and unstable machining conditions is processed using an average fast Fourier transform (FFT).…”

Section: Feature Extraction and Rankingmentioning

confidence: 99%

“…In the authors' previous work [32], the time-frequency analysis is an ideal tool for processing the non-stationary signals generated in machining operations, and its image features from the STFT show better performance in discriminating the stable and unstable tests than the time-domain features in macro-milling. This paper is a continuous effort to extend timefrequency images features for intelligent chatter detection in micro-milling.…”

Section: Introductionmentioning

confidence: 99%

Intelligent chatter detection using image features and support vector machine

Chen

Jing

et al. 2019

Int J Adv Manuf Technol

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Chatter is a self-excited vibration that affects the part quality and tool life in the machining process. This paper introduces an intelligent chatter detection method based on image features and the support vector machine. In order to reduce the background noise and highlight chatter characteristics, the average FFT is applied to identify the dominant frequency bands that divide the time-frequency image of the short-time Fourier transform into several sub-images. The non-stationary properties of the machining conditions are quantified using sub-images features. The area under the receiver operating characteristics curve ranks the extracted image features according to their separability capabilities. The support vector machine is designed to automatically classify the machining conditions and select the best feature subset based on the ranked features. The proposed method is verified by using dry micro-milling tests of steel 1040 and high classification accuracies for both the stable and unstable tests are obtained. In addition, the proposed method is compared with two additional methods using either image features from the continuous wavelet transform or time-domain features. The results present a better classification performance than the two additional methods, indicating the efficiency of the proposed method for chatter detection.

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Feature extraction using dominant frequency bands and time-frequency image analysis for chatter detection in milling

Cited by 41 publications

References 26 publications

Chatter detection for milling using novel p-leader multifractal features

Chatter detection for milling using novel p-leader multifractal features

Audio Feature Engineering for Occupancy and Activity Estimation in Smart Buildings

Intelligent chatter detection using image features and support vector machine

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