Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

Shi, Huaitao; Guo, Jin; Bai, Xiaotian; Guo, Lei; Liu, Zhenpeng; Sun, Jie

doi:10.3390/app10072443

Cited by 17 publications

(6 citation statements)

References 37 publications

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“…On the basis of the above model, Shi et al proposed a general form of nonlinear model, namely, the state-dependent model. In the past two decades, studies on the combination of this model and the RB Qiusheng meridian network have also been developed and applied to relevant engineering elds [5]. Ren et al proposed the functional cocientar model, which is also a special case of the state-dependent model [6].…”

Section: Introductionmentioning

confidence: 99%

Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management

Liu

2022

Journal of Control Science and Engineering

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Aiming at the nonlinear time series of automatic building construction management, a neural network prediction model is proposed to analyze and process the nonlinear sequence of deformation monitoring number cutter. The specific content of this method is as follows: for the noise problems existing in deformation monitoring data, a wavelet is used to denoise the preprocessing; for the BP network and RBF network commonly used in neural networks, the performance of the two networks is compared and demonstrated by MATLAB program, which proves that RBF neural network can significantly improve the accuracy of deformation prediction. By comparing the results, the maximum relative error of BP network prediction is 18.59%, while the maximum relative error of RBF network prediction is 29.16%, and the average relative error of 13P network prediction is 7.02%, while the average relative error of RBF network prediction value is 10.5%. The comprehensive error of network prediction is 6.1%, RBF network prediction is 8.52%, the standard deviation RMSE of BP network prediction error is 15.347, and that of RBF network prediction error is 21.401, and it shows that the prediction accuracy of BP network is higher than that of RBF network.

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Section: Introductionmentioning

confidence: 99%

Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management

Liu

2022

Journal of Control Science and Engineering

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“…Through application of Gabor wavelet coefficient squared envelope spectrum analysis method, Wang et al [47] detected the incipient fault occurrence at the No.533 data file. Using an improved empirical mode decomposition method to extract the incipient fault characteristic frequency, Fan et al [7] detected the incipient fault occurrence at the No.532 data file.…”

Section: A Laboratory Data Validationmentioning

confidence: 99%

“…In [4][5][6], an IFD method based on prior physical knowledge, which requires the establishment of an accurate physical model, has been investigated. However, the internal stress and other physical quantities of the running rolling bearings are relatively difficult to characterize, which often results in poor model generalization [7]. An IFD rolling bearings method based on the analysis of fault characteristic frequencies is established in [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Online Incipient Fault Detection Method Based on Improved ℓ₁ Trend Filtering and Support Vector Data Description

et al. 2021

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Poor model generalization, missing or false alarms, and heavy dependence on expert's experience are some of the major problems which exist in traditional incipient fault detection (IFD) methods. An IFD rolling bearing application method based on combination of improved  1 trend filtering (L1TF) and support vector data description (SVDD) is proposed. First, spectral distance index and multi-scale dispersion entropy based on normal vibration data, which is sensitive to incipient faults, are extracted. The improved  1 trend filter (IL1TF) method is employed for processing the feature values and obtaining a trend factor with less fluctuation and better incipient fault indication ability. Then, after determining the kernel function bandwidth of the SVDD by analyzing the characteristics of the training data, a suitable offline SVDD model is trained. Finally, incipient faults are identified by estimating the distance between the trend factor of the real-time data and the center of the hypersphere in the SVDD model. This method employs full performance of SVDD to detect abnormal data files, while reducing the influence of abnormal data files on the model via IL1TF. Furthermore, the method increases the discrimination between the incipient fault data and the normal data. By utilizing Intelligent Maintenance Systems of University of Cincinnati bearing laboratory data and Chinese petrochemical company's centrifugal pump bearing engineering data, the effectiveness of the constructed model is demonstrated. In addition, the proposed method is compared against existing representative IFD methods. The results indicate that the method proposed in this paper can solve false alarms and detect incipient failure data files more accurately without depending on the external expert's experience. This is of great significance for providing guidelines to enterprises which employ predictive maintenance techniques. INDEX TERMS Improved  1 trend filtering, support vector data description, kernel function bandwidth determination, incipient fault detection

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“…The model was created by identifying patterns in the distribution of the experimental data and their interrelationships, based on non-linear regression analysis [27,28]. The analysis of the experimental data showed that the rate of change of a variable is proportional to its actual value.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Non-Linear Regression Model in Rolling Bearing Diagnostics

et al. 2022

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The reliable operation of a mechanical system is dependent on the condition of installed rolling bearings. This paper presents a measuring system and equipment for analyzing the condition of rolling bearings. The developed system is a modular device (VibroLog) for measuring several diagnostic parameters for bearings, including vibration, number of revolutions, temperature and sound pressure. The device is a data collector and analyzer. Software in the Java programming language for the analysis and presentation of data obtained by VibroLog has also been developed. This paper is concerned with the development of a mathematical model for the evaluation and prediction of the qualitative state of rolling bearings in real operating conditions. It also presents results of measurement and mathematical modelling of the results. The model was formed by non-linear regression analysis as one of the most widely used statistical techniques. The analysis of the experimental data showed that the rate of change of a variable is proportional to its actual value. The model was tested on several rolling bearings having different degrees of damage. The developed model makes it possible to evaluate and predict the condition of the bearing by measuring the sound pressure level, which is simpler than vibration measurement in real operating conditions. During testing, the model generated results within the prediction error limits. The developed vibrodiagnostic system and the created model enable condition assessment and prediction for a wide range of rolling bearings.

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Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

Cited by 17 publications

References 37 publications

Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management

Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management

Online Incipient Fault Detection Method Based on Improved ℓ₁ Trend Filtering and Support Vector Data Description

Implementation of a Non-Linear Regression Model in Rolling Bearing Diagnostics

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Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

Cited by 17 publications

References 37 publications

Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management

Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management

Online Incipient Fault Detection Method Based on Improved ℓ1 Trend Filtering and Support Vector Data Description

Implementation of a Non-Linear Regression Model in Rolling Bearing Diagnostics

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Online Incipient Fault Detection Method Based on Improved ℓ₁ Trend Filtering and Support Vector Data Description