Rolling bearing fault diagnosis based on mean multigranulation decision-theoretic rough set and non-naive Bayesian classifier

Yu, Jun; Ding, Bo; He, Yongjun

doi:10.1007/s12206-018-1018-7

Cited by 22 publications

(11 citation statements)

References 36 publications

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“…Step 4. Since the curvature generated in the x direction is r ax = r ay = 4.365 mm and r bx = 35.003125 mm, then 1 Additionally, since the outer ratio r by is unknown, then from Equation ( 7) by using the compliance value of 0.52, the r by value is r by = dR r = (8.731 mm)(0.52) = 4.54 mm, therefore, 1 R y = .54 mm = 0.00883 mm −1 implying R y = 113.24 mm. Moreover, since 1 R = 0.20052 mm −1 + 0.00883 mm −1 = 0.20935 mm −1 , then the total curvature is R = 4.77 mm .…”

Section: Application Of the Proposed Methodsmentioning

confidence: 99%

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Stress-Based Weibull Method to Select a Ball Bearing and Determine Its Actual Reliability

et al. 2020

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Since the designed bearing’s reliability of 90% was determined in a lab environment, it does not represent the actual used environment. In this paper, a new methodology to determine the actual reliability that corresponds to the use conditions is offered. This new method is based on the standard method used to select the ball bearing. The proposed method is based on the two parameters of Weibull distribution, where the shape (β) and scale (η) parameters are both determined from the Hertz contact stresses values, which are generated under the surface of the motionless outer race, and by the forces transmitted between the ball and the outer race. Therefore, the derived reliability is different from the 90% index offered by manufacturers.

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Section: Application Of the Proposed Methodsmentioning

confidence: 99%

“…Ball bearings are important machine components that are used in vehicles, machines, airplanes, appliances, and precision equipment [1,2]. Defects of ball bearings are the main cause of failure of a rotating machine [3,4].…”

Section: Introductionmentioning

confidence: 99%

Stress-Based Weibull Method to Select a Ball Bearing and Determine Its Actual Reliability

et al. 2020

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“…The fault characteristic frequency method is commonly used [26] [27]. Other methods, such as hidden Markov models [28], rough set method [29], and neural networks [30] [31], Support Vector Machine [32] [33] [34] and other artificial intelligence algorithms are also applied to the classification of rolling bearing fault features. Considering the computational complexity and diagnostic accuracy, the envelope spectrum analysis method is widely used in engineering practice.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on the Coherent Demodulation Model

2020

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“…The working state of rolling bearings directly determines the reliable operation of a machine [1,2]. However, the occurrence probability of fault is always higher due to the influences of the load of rolling bearings of AC motor [3][4][5][6]. Thus, it is very important to improve the operation reliability and accurately diagnose faults for rolling bearings in time [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Study on a Novel Fault Diagnosis Method Based on VMD and BLM

et al. 2019

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The bearing system of an alternating current (AC) motor is a nonlinear dynamics system. The working state of rolling bearings directly determines whether the machine is in reliable operation. Therefore, it is very meaningful to study the fault diagnosis and prediction of rolling bearings. In this paper, a new fault diagnosis method based on variational mode decomposition (VMD), Hilbert transform (HT), and broad learning model (BLM), called VHBLFD is proposed for rolling bearings. In the VHBLFD method, the VMD is used to decompose the vibration signals to obtain intrinsic mode functions (IMFs). The HT is used to process the IMFs to obtain Hilbert envelope spectra, which are transformed into the mapped features and the enhancement nodes of BLM according to the complexity of the modeling tasks, and the nonlinear transformation mean according to the characteristics of input data. The BLM is used to classify faults of the rolling bearings of the AC motor. Next, the pseudo-inverse operation is used to obtain the fault diagnosis results. Finally, the VHBLFD is validated by actual vibration data. The experiment results show that the BLM can quickly and accurately be trained. The VHBLFD method can achieve higher identification accuracy for multi-states of rolling bearings and takes on fast operation speed and strong generalization ability.

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Rolling bearing fault diagnosis based on mean multigranulation decision-theoretic rough set and non-naive Bayesian classifier

Cited by 22 publications

References 36 publications

Stress-Based Weibull Method to Select a Ball Bearing and Determine Its Actual Reliability

Stress-Based Weibull Method to Select a Ball Bearing and Determine Its Actual Reliability

Rolling Bearing Fault Diagnosis Based on the Coherent Demodulation Model

Study on a Novel Fault Diagnosis Method Based on VMD and BLM

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