Power signal separation in milling process based on wavelet transform and independent component analysis

Shao, Hua; Shi, Xing; Li, Lin

doi:10.1016/j.ijmachtools.2011.05.006

Cited by 46 publications

(24 citation statements)

References 46 publications

(53 reference statements)

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“…However, as tools for the analysis of non-stationary signals become more widely available, more recent works do analyze non-stationary signals, e.g. Refs [14,15,26,28]. …”

Section: Discussion Of Related Workmentioning

confidence: 99%

“…Kalvoda and Hwang [14] applied the Hilbert-Huang transform (HHT) to the measured signal of monitor tool wear in the frequency domain and compared the outcome of the results with the widely applied Fourier transform. Shao et al [15] applied a modified single-channel blind sources separation (BSS) technique based on the wavelet transform and independent component analysis to separate the source signals related to a milling cutter and a spindle for the application of tool breakage monitoring. Chen et al [16] measure the tool vibrations, apply the wavelet transform and use a logistical correlation study of the wavelet energy is made to identify feature frequency bands that indicate tool wear.…”

Section: Tool Condition Monitoringmentioning

confidence: 99%

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Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Bakker

Gibson

Wilson

et al. 2015

Mechanical Systems and Signal Processing

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Due to its inherent advantages, linear friction welding is a solid-state joining process of increasing importance to the aerospace, automotive, medical and power generation equipment industries. Tangential oscillations and forge stroke during the burn-off phase of the joining process introduce essential dynamic forces, which can also be detrimental to the welding process. Since burn-off is a critical phase in the manufacturing stage, process monitoring is fundamental for quality and stability control purposes. This study aims to improve workholding stability through the analysis of fixture cassette deformations. Methods and procedures for process monitoring are developed and implemented in a fail-or-pass assessment system for fixture cassette deformations during the burn-off phase. Additionally, the denoised signals are compared to results from previous production runs. The observed deformations as a consequence of the forces acting on the fixture cassette are measured directly during the welding process. Data on the linear friction-welding machine are acquired and de-noised using empirical mode decomposition, before the burn-off phase is extracted. This approach enables a direct, objective comparison of the signal features with trends from previous successful welds. The capacity of the whole process monitoring system is validated and demonstrated through the analysis of a large number of signals obtained from welding experiments.

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“…However, as tools for the analysis of non-stationary signals become more widely available, more recent works do analyze non-stationary signals, e.g. Refs [14,15,26,28]. …”

Section: Discussion Of Related Workmentioning

confidence: 99%

Section: Tool Condition Monitoringmentioning

confidence: 99%

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Bakker

Gibson

Wilson

et al. 2015

Mechanical Systems and Signal Processing

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show abstract

“…Here, the experimental signals are selected as sinusoidal signal 1 Figure 1". Use EEMD to decompose the mixed signal, obtain a series of IMF components, the decomposition results is shown in " Figure 2".…”

Section: Experimental Simulation Signalmentioning

confidence: 99%

“…In this case, some scholars decompose the signal with wavelet, the resulting signal component is subjected to ICA processing, finally get the source signals [1]; Some scholars put forward the method of space-time, the method is to delay the mixed signal collected multi-channel signals, and then use the independent component analysis algorithm of multiple mixed signal separation, thus realize the rotating machinery fault diagnosis [2].…”

mentioning

confidence: 99%

Application of Single Channel Blind Separation Algorithm Based on EEMD-PCA-RobustICA in Bearing Fault Diagnosis

Xu¹,

Yan²

2017

IJCNS

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Aiming at the problem that ICA can only be confined to the condition that the number of observed signals is larger than the number of source signals; a single channel blind source separation method combining EEMD, PCA and RobustICA is proposed. Through the eemd decomposition of the single-channel mechanical vibration observation signal the multidimensional IMF components are obtained, and the principal component analysis (PCA) is performed on the matrix of these IMF components. The number of principal components is determined and a new matrix is generated to satisfy the overdetermined blind source separation conditions, the new matrix input RobustICA, to achieve the separation of the source signal. Finally, the isolated signals are respectively analyzed by the envelope spectrum, the fault frequency is extracted, and the fault type is judged according to the prior knowledge. The experiment was carried out by using the simulation signal and the mechanical signal. The results show that the algorithm is effective and can accurately diagnose the location of mechanical fault.

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“…The monitoring index extracted from wavelet coefficients of highest energy packets could reliably detect the condition of the tool. (Shao et al, 2011) utilized a modified blind sources separation (BSS) technique to separate source signals in milling process. A single-channel BSS method based on wavelet transform and independent component analysis (ICA) was developed, and source signals related to a milling cutter and spindle were separated from a single-channel power signal.…”

Section: Tool Wearmentioning

confidence: 99%

Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications

Λούτας¹,

Kostopoulos²

2012

Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

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Power signal separation in milling process based on wavelet transform and independent component analysis

Cited by 46 publications

References 46 publications

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Application of Single Channel Blind Separation Algorithm Based on EEMD-PCA-RobustICA in Bearing Fault Diagnosis

Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications

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