High-resolution methods in vibratory analysis: application to ball bearing monitoring and production machine

Dron, Jean-Paul; Rasolofondraibe, Lanto; Bolaers, Fabrice; Pavan, A.

doi:10.1016/s0020-7683(00)00277-8

Cited by 19 publications

(13 citation statements)

References 7 publications

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“…After the development of time and frequency domain characteristics into a certain stage, the relevant statistical parameters are introduced into the vibration analysis. Dron et al [16] compared the performance of the different parametric autoregressive spectral analysis methods with the traditional spectral analysis methods' for bearings' fault diagnosis. The autoregressive model along with Bugh algorithm and Akaike information criterion was retained.…”

Section: The Development Of the Main Individual Physical Monitoring Tmentioning

confidence: 99%

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

Duan

Guo

et al. 2018

Int J Adv Manuf Technol

164

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A rolling bearing is an essential component of a rotating mechanical transmission system. Its performance and quality directly affects the life and reliability of machinery. Bearings' performance and reliability need high requirements because of a more complex and poor working conditions of bearings. A bearing with high reliability reduces equipment operation accidents, equipment maintenance costs and achieves condition-based maintenance. First in this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings are introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced, finally the advantages, disadvantages and trends developed in the future of the detection main individual physics technology and multi-sensors information fusion technology are summarized. This paper is expected to provide the necessary basis for the follow-up study of the fault diagnosis of rolling bearings and a foundational knowledge for researchers about rolling bearings.

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Section: The Development Of the Main Individual Physical Monitoring Tmentioning

confidence: 99%

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

Duan

Guo

et al. 2018

Int J Adv Manuf Technol

164

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“…Compared with other methods, the signal acquisition and analysis of vibration analysis are easier to implement. Therefore, various vibration analysis methods have been introduced in fault diagnosis of rolling bearings, such as autoregressive model [11,12], spectral kurtosis [13][14][15] and kurtogram [16], wavelet transform [17,18], matching pursuit order tracking [19,20] and empirical mode decomposition [21,22]. Although the above methods have been proved to be effective tools of fault diagnosis for rolling bearings, but the process of these methods is not simple enough, which is not conducive to the practical application of engineering.…”

Section: Introductionmentioning

confidence: 99%

A novel faults detection method for rolling bearing based on RCMDE and ISVM

Zhang¹,

Zhao²,

Teng³

et al. 2019

J. vibroeng.

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The rolling bearing is an essential element widely used in the rotating machinery. Bearing failures are among the main reasons for breakdown of rotating machinery. Therefore, fault detection of bearing is necessary to reduce the probability of breakdown and safety accidents. A novel fault diagnosis method for rolling bearing based on Refined Composite Multiscale Dispersion Entropy (RCMDE) and Improved Support Vector Machine (ISVM) is presented in this paper. The RCMDE is a new irregular index in biomedical signal analysis, which has lower computational cost and more stable results. Therefore, the RCMDE is introduced as fault feature to represent the bearing fault characteristics. After feature extraction, an improved support vector machine based on whale optimization algorithm (WOA) and support vector machine (SVM) is proposed as a fault classifier, which has the advantages of less training samples and good classification effect. The effectiveness of the proposed method in bearing fault diagnosis is verified by using bearing fault experimental data.

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“…Then the pulses created by the fault will be of very low amplitude and will be impossible to detect in the raw signal or spectrum. A lot of the suggested fault detection methods for ball bearings involve a noise reduction in the recorded signals to facilitate the detection of the pulses created by the fault(s) [2,3,5,6]. A successful detection procedure should be able to first extract the appropriate frequency content of the measured signal and then detect the presence of faults in the extracted frequency band.…”

Section: And1 Introductionmentioning

confidence: 99%

“…However after the application of all the procedures the decision whether a fault is present or not has to be taken by an expert through e.g introducing a threshold value of the considered feature or just on the basis of comparison to previous levels [2,3,5,6,8,27,30]. This paper suggests a method which does not require that: once the wavelet filtering is done and the principal components are extracted the PR procedure automatically issues a decision on whether a fault is present in the bearing or not.…”

Section: And1 Introductionmentioning

confidence: 99%

An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition

Trendafilova

2010

Mechanical Systems and Signal Processing

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The Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Αβστραχτ.Τηισ παπερ συγγεστσ αν αυτοmατεδ αππροαχη φορ φαυλτ δετεχτιον ανδ χλασσιφιχατιον ιν ρολλερ βεαρινγσ, ωηιχη ισ βασεδ ον παττερν ρεχογνιτιον ανδ πρινχιπαλ χοmπονεντσ αναλψσισ οφ τηε mεασυρεδ ϖιβρατιον σιγναλσ. Τηε σιγναλσ ρεχορδεδ αρε πρε−προχεσσεδ αππλψινγ α ωαϖελετ τρανσφορm ιν ορδερ το εξτραχτ τηε αππροπριατε ηιγη φρεθυενχψ (δεταιλεδ) αρεα νεεδεδ φορ βαλλ βεαρινγ φαυλτ δετεχτιον. Τηισ ισ φολλοωεδ βψ α παττερν ρεχογνιτιον (ΠΡ) προχεδυρε υσεδ το ρεχογνισε βετωεεν σιγναλσ χοmινγ φροm ηεαλτηψ βεαρινγσ ανδ τηοσε γενερατεδ φροm διφφερεντ βεαρινγ φαυλτσ. Φουρ χατεγοριεσ οφ σιγναλσ αρε χονσιδερεδ, ναmελψ νο φαυλτ σιγναλσ (φροm α ηεαλτηψ βεαρινγ) ιννερ ραχε φαυλτ, ουτερ ραχε φαυλτ ανδ ρολλινγ ελεmεντ φαυλτ σιγναλσ. Τηε ΠΡ προχεδυρε υσεσ τηε φιρστ σιξ πρινχιπαλ χοmπονεντσ εξτραχτεδ φροm τηε σιγναλσ αφτερ α προπερ πρινχιπαλ χοmπονεντ αναλψσισ (ΠΧΑ). Ιν τηισ ωορκ α mοδιφιεδ ΠΧΑ ισ συγγεστεδ ωηιχη ισ mυχη mορε αππροπριατε φορ χατεγοριχαλ δατα. Τηε χοmβινατιον οφ τηε mοδιφιεδ ΠΧΑ ανδ τηε ΠΡ mετηοδ ενσυρεσ τηατ τηε φαυλτ ισ αυτοmατιχαλλψ δετεχτεδ ανδ χλασσιφιεδ το ονε οφ τηε χονσιδερεδ φαυλτ χατεγοριεσ. Τηε mετηοδ συγγεστεδ δοεσ νοτ ρεθυιρε τηε κνοωλεδγε/ 1 δετερmινατιον οφ τηε σπεχιφιχ φαυλτ φρεθυενχιεσ ανδ/ορ ανψ εξπερτ αναλψσισ: ονχε τηε σιγναλ φιλτερινγ ισ δονε ανδ τηε ΠΧ'σ αρε φουνδ τηε ΠΡ mετηοδ αυτοmατιχαλλψ γιϖεσ τηε ανσωερ ιφ τηερε ισ α φαυλτ πρεσεντ ανδ ιτσ τψπε.Κεψωορδσ: φαυλτ δετεχτιον, ϖιβρατιον−βασεδ mονιτορινγ, βαλλ βεαρινγ φαυλτ δετεχτιον, σιγναλ αναλψσισ, παττερν ρεχογνιτιον, πρινχιπαλ χοmπονεντ αναλψσισ 2

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High-resolution methods in vibratory analysis: application to ball bearing monitoring and production machine

Cited by 19 publications

References 7 publications

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review

A novel faults detection method for rolling bearing based on RCMDE and ISVM

An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition

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