Vibration monitoring of high speed spindles using spectral analysis techniques

Vafaei, S.; Rahnejat, Homer; Aini, R.

doi:10.1016/s0890-6955(02)00049-4

Cited by 93 publications

(78 citation statements)

References 13 publications

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“…A number of researches have been carried out for demodulating the responses,isolating and identifying the specific causes of the spectral contributions.In the case of high speed rotors, some methods can be compromised by the fact that the impulse responses generated by the sharp impacts in the bearings may have a comparable length to the separation between the impacts [3]. The methods of envelope analysis such as Hilbert-Huang transform [10], of Wavelet decomposition [11] or of Auto Regressive Moving Average method applied in frequency domain [12] have conducted to good results concerning spindle applications.…”

Section: Fig 1 Examples Of Cutting Forcesmentioning

confidence: 99%

“…Indeed, manufacturing defects in form and finish of rolling elements and raceway surfaces induce other vibrations. The secondary effects of the surfaces waviness, off-sized balls and non-linearity of the ballrace contact stiffness were modeled as cyclo-stationaritiesand conducted to the analytical expressions of the secondary bearing-induced vibration frequencies [6,7,11,12]. Considering the raceway waviness for example, even if the defect is distributed on the whole groove circumference, cyclo-stationary approaches are still compatible due to the periodic nature of the defect.…”

Section: Fig 1 Examples Of Cutting Forcesmentioning

confidence: 99%

“…Considering the raceway waviness for example, even if the defect is distributed on the whole groove circumference, cyclo-stationary approaches are still compatible due to the periodic nature of the defect. Interesting results were obtained concerning high speed spindle issues [7,12].…”

Section: Fig 1 Examples Of Cutting Forcesmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring of distributed defects on HSM spindle bearings

Castelbajac

Ritou

Laporte³

et al. 2014

Applied Acoustics

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Section: Fig 1 Examples Of Cutting Forcesmentioning

confidence: 99%

Section: Fig 1 Examples Of Cutting Forcesmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring of distributed defects on HSM spindle bearings

Castelbajac

Ritou

Laporte³

et al. 2014

Applied Acoustics

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“…Rolling bearing vibration signals are often nonlinear and non-stationary when the rolling bearing works with faults [2][3][4][5]. Many nonlinear dynamics methods including approximate entropy (AppEn) [6,7], sample entropy (SampEn) [8,9] and MSE [10][11][12] have been widely applied to mechanical fault diagnosis due to their ability to effectively extract the fault feature information hidden in the vibration signals that cannot be well extracted by linear analysis methods.…”

Section: Introductionmentioning

confidence: 99%

A Refined Composite Multivariate Multiscale Fuzzy Entropy and Laplacian Score-Based Fault Diagnosis Method for Rolling Bearings

Zheng

Pan

et al. 2017

Entropy

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Abstract:The vibration signals of rolling bearings are often nonlinear and non-stationary. Multiscale entropy (MSE) has been widely applied to measure the complexity of nonlinear mechanical vibration signals, however, at present only the single channel vibration signals are used for fault diagnosis by many scholars. In this paper multiscale entropy in multivariate framework, i.e., multivariate multiscale entropy (MMSE) is introduced to machinery fault diagnosis to improve the efficiency of fault identification as much as possible through using multi-channel vibration information. MMSE evaluates the multivariate complexity of synchronous multi-channel data and is an effective method for measuring complexity and mutual nonlinear dynamic relationship, but its statistical stability is poor. Refined composite multivariate multiscale fuzzy entropy (RCMMFE) was developed to overcome the problems existing in MMSE and was compared with MSE, multiscale fuzzy entropy, MMSE and multivariate multiscale fuzzy entropy by analyzing simulation data. Finally, a new fault diagnosis method for rolling bearing was proposed based on RCMMFE for fault feature extraction, Laplacian score and particle swarm optimization support vector machine (PSO-SVM) for automatic fault mode identification. The proposed method was compared with the existing methods by analyzing experimental data analysis and the results indicate its effectiveness and superiority.

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“…Others are due to unbalances in assembly and yet others are due to the waviness of rolling surfaces (no surface is perfectly smooth). Then, there are initiators of failure, which are noted subsequently through vibration monitoring [6], such as the presence of off-sized balls or pits, dents, or cracks on rolling surfaces. These cause particular frequency to output from bearings, such as "ball spin frequency" or multiple of cage frequency.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings Based on EWT and KDEC

Ge¹,

Wang²,

Ren³

2017

Entropy

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This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT) and kernel density estimation classifier (KDEC), which can well diagnose fault type of the rolling element bearings. With the proposed fault diagnosis method, the vibration signal of rolling element bearing was firstly decomposed into a series of F modes by EWT, and the root mean square, kurtosis, and skewness of the F modes were computed and combined into the feature vector. According to the characteristics of kernel density estimation, a classifier based on kernel density estimation and mutual information was proposed. Then, the feature vectors were input into the KDEC for training and testing. The experimental results indicated that the proposed method can effectively identify three different operative conditions of rolling element bearings, and the accuracy rates was higher than support vector machine (SVM) classifier and back-propagation (BP) neural network classifier.

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Vibration monitoring of high speed spindles using spectral analysis techniques

Cited by 93 publications

References 13 publications

Monitoring of distributed defects on HSM spindle bearings

Monitoring of distributed defects on HSM spindle bearings

A Refined Composite Multivariate Multiscale Fuzzy Entropy and Laplacian Score-Based Fault Diagnosis Method for Rolling Bearings

Fault Diagnosis of Rolling Bearings Based on EWT and KDEC

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