A statistical feature investigation of the spalling propagation assessment for a ball bearing

Liu, Jing; Xu, Zidan; Zhou, Li; Yu, Wennian; Shao, Yimin

doi:10.1016/j.mechmachtheory.2018.10.007

Cited by 135 publications

(81 citation statements)

References 35 publications

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“…In previous studies [6], [15] and [49] a plethora of features that could be extracted from the vibrational data were studied, specifically from the time domain, frequency domain or the timefrequency domain using various signal-processing tools such as the Fourier transform, Hilbert transform, Wavelet transform, etc. The feature-extraction part can greatly enhance the results of the classification and there is a lot of studies emerging on this topic [50]. However, since this paper represents the application of a classification method and its variants to one of the most dominant problems in the field of bearing and rotating-machinery fault detection we will simplify the feature-extraction process to only the statistical features of the vibrational signals in the time and frequency domains.…”

Section: Feature Extraction and Construction Of Classification Datasetsmentioning

confidence: 99%

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Panić

Klemenc

Nagode

2020

SV-JME

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Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring. Highlights• Gaussian-mixture-model-based classification was applied to the bearing-fault classification. • To discriminate the faulty from non-faulty bearings only simple statistics from vibrational data was used. • Two different datasets, the Case Western Rice University dataset and Bearing vibration data collected under time-varying rotational speed conditions dataset are used. • The Gaussian-mixture-model-based classification method showed to be a competitive and efficient method.

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Section: Feature Extraction and Construction Of Classification Datasetsmentioning

confidence: 99%

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Panić

Klemenc

Nagode

2020

SV-JME

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“…The characteristics of the vibration signals for different fault sizes cases were different, which was used to determine the fault size range. 29,30 The Pearson correlation coefficient (PCC) was used to determine the effective time-domain statistical in- dexes. It was defined by Liu, et al as 29,30…”

Section: Time-domain Parameter Analysis Modulementioning

confidence: 99%

“…The value y represented the healthy, slight, moderate, severe, and very severe faults, whose values were obtained by using the PCC. 30 This method was based on the testing data from the Case Western Reserve University data. 31 As discussed by Liu, et al, 5 or 6 statistical indexes were used to determine the fault size range.…”

Section: Time-domain Parameter Analysis Modulementioning

confidence: 99%

An Intelligent Detection System Development for Local Faults in a Ball Bearing

Liu¹,

Wang²,

Zhou³

et al. 2019

June 2019

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Local faults can be produced in ball bearings during their manufacturing process. An efficient, fast and accurate local fault detection method can help improve the quality of ball bearings. To overcome this problem, an intelligent detection system for a ball bearing with the local faults is developed based on the NI LabVIEW software. This system includes the determination of bearing fault parameters, signal acquisition, envelope analysis, time-domain parameter analysis and bearing fault status modules. In this system, the frequency-domain feature method is based on the envelope demodulation analysis, the effective statistical indexes, and the Pearson correlation coefficient. The frequency-domain feature method is used to determine the threshold range for each fault level in the system. This system can in turn be used to determine the fault location and sizes for the ball bearings. A case study for the calculation and analysis for the frequency and time-domain acceleration is presented to predict the location and size of the local faults in a ball bearing. The test data from the Case Western Reserve University Bearing Data Center is used to verify the developed intelligent detection system for local faults in the ball bearing. The results show that the proposed detection system can be used to detect the local fault in the ball bearings.

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“…e angular contact ball bearing is the kernel of high-speed motorized spindles, and the dN value is more than 0.6 × 10 6 mm•(r/min). Its dynamic stiffness directly affects the machining accuracy and dynamic characteristics of the spindle [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Model to Characterize Comprehensive Stiffness of Angular Contact Ball Bearings

Lei

Li²,

Gong

et al. 2020

Mathematical Problems in Engineering

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The bearing dynamic behaviors will be complicated due to the changes in the geometric sizes and relative positions of the bearing components at high speed. In this paper, based on the Hertz contact theory, elastohydrodynamic lubrication (EHL) model, and Jones’ bearing theory, the comprehensive stiffness model of the angular contact ball bearing is proposed in consideration of the effects of elastic deformation, centrifugal deformation, thermal deformation, and the ball spinning motion. The influences of these factors on bearing dynamic stiffness are investigated in detail. The calculation results show that the centrifugal deformation and thermal deformation increase with the increase in rotation speed. When the centrifugal deformation and thermal deformation are considered, the bearing radial contact stiffness increases as the speed increases, whereas the axial contact stiffness and the angular contact stiffness decrease. When the deformations and the EHL are all considered, the comprehensive bearing stiffness decreases with the increasing speed. It is also found that the spinning motion of the ball causes the comprehensive bearing stiffness to increase.

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A statistical feature investigation of the spalling propagation assessment for a ball bearing

Cited by 135 publications

References 35 publications

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

An Intelligent Detection System Development for Local Faults in a Ball Bearing

An Integrated Model to Characterize Comprehensive Stiffness of Angular Contact Ball Bearings

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