Evaluation of Dynamic Uncertainty of Rolling Bearing Vibration Performance

Liang, Ye; Xia, Xintao; Chang, Zhen

doi:10.1155/2019/2896046

Cited by 7 publications

(3 citation statements)

References 28 publications

(34 reference statements)

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“…The evaluation accuracy will even drop when the prior information is distorted. In recent years, scholars have carried out relevant research on fault prediction, process evaluation and event prediction in poor information situation, and achieved good results [38][39][40][41][42]. But the core prediction model of this method is the use of Grey Model alone or in combination with the Bootstrap.…”

Section: Literature Reviewmentioning

confidence: 99%

A machine learning-based approach for product maintenance prediction with reliability information conversion

Zhang

Xue

Yan

et al. 2022

Auton. Intell. Syst.

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Predictive maintenance (PdM) cannot only avoid economic losses caused by improper maintenance but also maximize the operation reliability of product. It has become the core of operation management. As an important issue in PdM, the time between failures (TBF) prediction can realize early detection and maintenance of products. The reliability information is the main basis for TBF prediction. Therefore, the main purpose of this paper is to establish an intelligent TBF prediction model for complex mechanical products. The reliability information conversion method is used to solve the problems of reliability information collection difficulty, high collection cost and small data samples in the process of TBF prediction based on reliability information for complex mechanical products. The product reliability information is fully mined and enriched to obtain more reliable and accurate TBF prediction results. Firstly, the Fisher algorithm is employed to convert the reliability information to expand the sample, and the compatibility test is also discussed. Secondly, BP neural network is used to realize the final prediction of TBF, and PSO algorithm is used to optimize the initial weight and threshold of BP neural network to avoid falling into local extreme value and improve the convergence speed. Thirdly, the mean-absolute-percentage-error and the Coefficient of determination are selected to evaluate the performance of the proposed model and method. Finally, a case study of TBF prediction for a remanufactured CNC milling machine tool (XK6032-01) is studied in this paper, and the results show that the feasibility and superiority of the proposed TBF prediction method.

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Section: Literature Reviewmentioning

confidence: 99%

A machine learning-based approach for product maintenance prediction with reliability information conversion

Zhang

Xue

Yan

et al. 2022

Auton. Intell. Syst.

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show abstract

“…The other is the design of appropriate methods for prediction. Current research depends mostly on the laboratory environment to obtain complete life cycle monitoring data through an accelerated degradation experiment [5][6][7]. The experimental conditions are relatively ideal; relevant research on solving practical engineering problems in complex environments is scarce.…”

Section: Introductionmentioning

confidence: 99%

A performance degradation prediction approach for turbo-generator bearing considering complex working conditions based on clustering indicator and self-optimized deep learning model

Duan¹,

Zhou²,

Liu³

et al. 2021

Meas. Sci. Technol.

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The reliability of turbo-generator bearings affects the stability and safety of power plants. To detect early symptoms of failure, prediction of the performance degradation of turbo-generator bearings is particularly important. At present, most related research relies on accelerated degradation test platforms; however, the industrial field environment and complex working conditions may affect practical application. In this study, a performance degradation prediction approach for turbo-generator bearings considering complex working conditions based on a clustering indicator and a self-optimized deep learning model (SODLM) is proposed. First, the Dirichlet process-Gaussian mixture clustering model is introduced to construct the performance degradation indicator (PDI) of the turbo-generator. Then through introducing the hyper-parameter constraint, the hyper-parameter influence significance comparison mechanism, and the minimum training epoch, an improved hyperband (IH) is established to realize the automatic tuning of the hyper-parameters in the prediction model. Furthermore, the SODLM is constructed based on the IH and a stacked one-dimensional convolutional neural network. Finally, the performance degradation trend of a turbo-generator bearing in a practical industrial field is successfully predicted through the proposed approach. The experimental analysis demonstrates that the proposed PDI avoids the influence of sampling frequency and complex working conditions. Compared with traditional optimization algorithms and prediction models, the proposed performance degradation prediction approach demonstrates better accuracy and stability.

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“…Xia et al [10] evaluated the uncertainty of vibration by a grey prediction model. Ye et al [11] predicted the vibration performance by chaos prediction model. Ren et al [12] proposed a performance evaluation method by manifold space fuzzy k-master curve similarity analysis.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Performance Evaluation Model for a Gearbox System Using Hidden Markov Model

Huang

et al. 2020

IEEE Access

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In order to track and grasp the operation situations of the gearboxes, the vertical vibration signals of three different gear fault states, normal, worn and broken teeth, are collected via a gearbox vibration experiment. An online diagnosis and performance evaluation model with hidden Markov model (HMM) and fuzzy comprehensive evaluation is proposed. To address the limitation of maximum membership principle in the case of equal membership or the membership is very close to each other, a closeness evaluation strategy is proposed by defining the likelihood ratio of HMM as a similarity and selecting an combined membership function of the semi-trapezoidal and intermediate-ridge distribution. Results show that the online diagnosis has achieved a good performance with the similarity strategy. Compared with the evaluation strategy of the maximum membership principle, the proposed gearbox performance model with the closeness evaluation strategy is more accurately distinguished from the evaluation results of the broken teeth state and the worn state, especially for the case of the equal membership.

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Evaluation of Dynamic Uncertainty of Rolling Bearing Vibration Performance

Cited by 7 publications

References 28 publications

A machine learning-based approach for product maintenance prediction with reliability information conversion

A machine learning-based approach for product maintenance prediction with reliability information conversion

A performance degradation prediction approach for turbo-generator bearing considering complex working conditions based on clustering indicator and self-optimized deep learning model

A Fuzzy Performance Evaluation Model for a Gearbox System Using Hidden Markov Model

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