Experimental diagnostics of ball bearings using statistical and spectral methods

Karaçay, Tuncay; Aktürk, Nizami

doi:10.1016/j.triboint.2008.11.003

Cited by 79 publications

(49 citation statements)

References 11 publications

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“…Based from this results, it was confirmed that the presence of specific defect will increase the amplitude of vibration at the specific fundamental frequencies. Previous findings [8] also have proven these phenomena accordingly. In contrast, the spectrums of corroded bearings shows high amplitude values for all fundamental frequencies.…”

Section: Frequency Spectrumssupporting

confidence: 61%

“…In general, the major aims of feature extraction analysis is to extract the hidden signal features among the complex signal that could lead to the detection of damage occurrence in the system that was monitored. Basically, on one hand, the feature was extracted directly from the acquired signal by determining its statistical parameter [4][5][6][7] and fundamental frequencies [3,8] parameters. Meanwhile, on the other hand, the decomposition method such as wavelet analysis [9][10][11][12], and empirical mode decomposition [13][14][15] was applied before the feature is determined.…”

Section: Introductionmentioning

confidence: 99%

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Clustering of frequency spectrums from different bearing fault using principle component analysis

et al. 2016

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Abstract. In studies associated with the defect in rolling element bearing, signal clustering are one of the popular approach taken in attempt to identify the type of defect. However, the noise interruption are one of the major issues which affect the degree of effectiveness of the applied clustering method. In this paper, the application of principle component analysis (PCA) as a pre-processing method for hierarchical clustering analysis on the frequency spectrum of the vibration signal was proposed. To achieve the aim, the vibration signal was acquired from the operating bearings with different condition and speed. In the next stage, the principle component analysis was applied to the frequency spectrums of the acquired signals for pattern recognition purpose. Meanwhile the mahalanobis distance model was used to cluster the result from PCA. According to the results, it was found that the change in amplitude at the respective fundamental frequencies can be detected as a result from the application of PCA. Meanwhile, the application of mahalanobis distance was found to be suitable for clustering the results from principle component analysis. Uniquely, it was discovered that the spectrums from healthy and inner race defect bearing can be clearly distinguished from each other even though the change in amplitude pattern for inner race defect frequency spectrum was too small compared to the healthy one. In this work, it was demonstrated that the use of principle component analysis could sensitively detect the change in the pattern of the frequency spectrums. Likewise, the implementation of mahalanobis distance model for clustering purpose was found to be significant for bearing defect identification.

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Section: Frequency Spectrumssupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Clustering of frequency spectrums from different bearing fault using principle component analysis

et al. 2016

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“…The shaft and ball bearing are arranged as in Karacay and Akturk [27]. This arrangement is illustrated in Figure 2.…”

Section: Simulation Analysismentioning

confidence: 99%

Fault Diagnosis of Shaft- Ball Bearing System Using One-Way Analysis of Variance

Yüzükırmızı

Arslan

2014

MCA

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Abstract-Roller bearing is one of the most widely used and critical elements in rotating machinery. In consequence, bearing fault diagnosis in machines, as well as to discriminate the different fault conditions have been a great interest. In this study, firstly, analytical model of a shaft-ball bearing system is developed. The shaft is assumed to be perfectly rigid and uniform, and supported by two radial ball bearings. Then, the effect of localized defects on bearing running surfaces (i.e. surfaces of inner and outer rings and balls) on the shaft vibrations are obtained using the simulation program. Then, vibration signatures are analyzed by one-way analysis of variance (ANOVA) method. Finally, post-hoc tests are applied to differentiate the ball bearing element's localized defects in shaft-ball bearing simulation model.

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“…The most common values that are measured or calculated are (Karacay and Akturk, 2009;Niu et al, 2005):…”

Section: Vibration Signal Analysis On Rolling Element Bearingsmentioning

confidence: 99%

The Adverse Effect of Steel Particle Contaminants on Fatigue Life of Grease Lubricated Ball Bearings

Koulocheris

Raptis

2014

American Journal of Applied Sciences

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The rating life of ball bearings is reduced when hard particle contaminants are present in the lubricant. Usually, this life reduction is taken into account in the calculation of modified rating life by using the contamination factor through a general characterization of the lubrication conditions. However, the impact of contaminant's variables such as size, hardness and concentration level has to be specified in detail. This need is resolved by the present work, where greases contaminated with hard steel particles of different sizes are tested with the purpose of finding a pattern in the relationship between steel particle sizes and the progress of wear inside the bearings. A laboratory rig is utilized for these tests and vibration analysis is performed to estimate the condition and the residual life of the bearings. After the tests, optical inspections performed in a stereoscope verify the predictions of vibration analyses. It was found that large contaminating particles, after their initial deformation, produce high stresses and therefore higher wear of the bearings and that the wear mechanism is closely related to the interruption of the lubricating film in such a way that local overheating caused point melting and adhesion of the particles adhered on the raceways. In case of grease contaminated with steel particles, vibration analysis can indicate the severity of wear and monitor its progress. From the conducted tests of the present work it is proved that the size of the contaminant particles affect strongly the wear process in such a way that the larger steel particle contaminants cause greater damage to the bearing. The vibration levels were higher and the damage was greater as particle size increased. It is difficult to establish an equation that describes and quantifies the wear progress involving all the parameters of size and concentration levels of the steel contaminant particles due to the stochastic nature of the wear mechanisms and therefore presently a statistical approach of the wear and the residual life expectancy seems to be more practical. The collection of a large number of experimental results is underway.

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Experimental diagnostics of ball bearings using statistical and spectral methods

Cited by 79 publications

References 11 publications

Clustering of frequency spectrums from different bearing fault using principle component analysis

Clustering of frequency spectrums from different bearing fault using principle component analysis

Fault Diagnosis of Shaft- Ball Bearing System Using One-Way Analysis of Variance

The Adverse Effect of Steel Particle Contaminants on Fatigue Life of Grease Lubricated Ball Bearings

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