Boring chatter identification by multi-sensor feature fusion and manifold learning

Pan, Jinqiu; Liu, Zhibing; Wang, Xibin; Chen, Che; Pan, Xiaoyu

doi:10.1007/s00170-020-05611-4

Cited by 18 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then chatter detection is performed by a trained multi-class SVM. The authors of the document [36] proposed an approach for identifying chatter in the boring process. It consists of merging the characteristics of multiple sensors to obtain the processing signals.…”

Section: Machine Learningmentioning

confidence: 99%

Review of AI-based methods for chatter detection in machining based on bibliometric analysis

Kounta

Arnaud

Kamsu-Foguem

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

HIGHLIGHTS• Bibliometric analysis on chatter detection techniques in machining processes.• Effectiveness of AI methods combined with transformation and decomposition techniques.• Research areas mainly cover manufacturing, mechanics, and automation control systems.• Application of signal processing techniques in chatter detection with their advantages.• Challenges of deep learning models to solve problems of performance and explainability.

show abstract

Section: Machine Learningmentioning

confidence: 99%

Review of AI-based methods for chatter detection in machining based on bibliometric analysis

Kounta

Arnaud

Kamsu-Foguem

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…On the one hand, multi-sensor signals are the quantitative superposition of single-sensor signals. Many scholars still perform feature extraction in the same way as single-sensor signals, and then solve the problem of excessive feature dimensionality by feature fusion methods such as local linear embedding (LLE) [31] and local preserving projection [32], and finally classify them by vector classifiers. For example, Shen and Xu [33] proposed an optimized weighted kernel principal component analysis (PCA) feature fusion method applied to multi-sensor bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor gearbox fault diagnosis using generalized minimum entropy deconvolution and main frequency center extraction

Liu,

Wang,

2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Accurate diagnosis of gearbox faults is vital to maintain the stability and dependability of rotating machinery. In practical engineering scenarios, the installation of multiple sensors is often necessary to monitor numerous variables and gather extensive information. To effectively utilize the fault information from multiple sensors and improve fault diagnosis performance, this paper presents a gearbox fault diagnosis method using generalized minimum entropy deconvolution (GMED) and main frequency center extraction (MFCE). Firstly, the introduced GMED enhances the fault signals from multiple sensors through the maximization of standardized moments, offering advantages such as stability, wide applicability, and minimal parameter tuning requirements. Subsequently, the proposed MFCE is employed to extract features. MFCE selects main spectral lines and derives a novel feature metric named main frequency center. This not only further reduces the influence of interfering components, but also requires the extraction of only 2 n features (where n is the number of sensors) to achieve a high fault recognition rate, while remaining compatible with multiple classical classifiers. Finally, the effectiveness and robustness of the proposed method are demonstrated through experimental results on two gearbox fault datasets.

show abstract

“…Kuljanic et al [9]compared the sensibility of chatter onset of several sensors, and found that three or four sensors are the most promising solution for reliable and robust chatter identification. Pan et al [10]studied the boring chatter identification with multi-sensors by multi-feature parameters and manifold learning, and found that multi-features extracted from different kinds of sensors can improve the recognition rate. However, some sensors are not applicable in practical application of cutting process.…”

Section: Introductionmentioning

confidence: 99%

Chatter detection in milling process based on the combination of wavelet packet transform and PSO-SVM

Zheng

Chen

Jiao

2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Chatter has become the mainly limiting factor in the development of rapid and stable machining of machine tools, which seriously impacts on surface quality and dimensional accuracy of the finished workpiece. In this paper, a novel method of chatter recognition was proposed based on the combination of wavelet packet transform (WPT) and PSO-SVM in milling. The collected vibration signal was pre-processed by wavelet packet transform (WPT), and the wavelet packets with rich chatter information were selected and reconstructed. The selected wavelet packets can reduce the redundant noise and useless information. a combination of 10 time-domain and 4 frequency-domain feature parameters were obtained through calculating the reconstructed vibration signals. Compared to three methods of k-fold cross validation (k-CV), genetic algorithm (GA) and particle swarm optimization (PSO) to optimize the input parameters of SVM, the experiment results were shown that the PSO algorithm has is characterized by high accuracy. The proposed approach can recognize the stable, chatter and transition states more accurately than the other traditional approaches.

show abstract

Boring chatter identification by multi-sensor feature fusion and manifold learning

Cited by 18 publications

References 27 publications

Review of AI-based methods for chatter detection in machining based on bibliometric analysis

Review of AI-based methods for chatter detection in machining based on bibliometric analysis

Multi-sensor gearbox fault diagnosis using generalized minimum entropy deconvolution and main frequency center extraction

Chatter detection in milling process based on the combination of wavelet packet transform and PSO-SVM

Contact Info

Product

Resources

About