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Journal articles on the topic 'Degradation state of a bearing'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Ta, Yuntian, and Tiantian Wang. "A rolling bearing state evaluation method based on deep learning combined with Wiener process." PHM Society European Conference 8, no. 1 (2024): 8. http://dx.doi.org/10.36001/phme.2024.v8i1.4095.

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As a key component of rotating parts, rolling bearings largely determine the operation safety of equipment. However, in practical applications, because the degradation trajectory of rolling bearings cannot be truly characterized, the existing model cannot accurately describe the degradation trajectory of rolling bearings, resulting in the running state of rolling bearings cannot be directly evaluated. Therefore, a method of rolling bearing state assessment based on deep learning combined with Wiener process is proposed in this paper. Firstly, a deep network model is constructed by deep learnin

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2

Du, Wenliao, Xukun Hou, and Hongchao Wang. "Time-Varying Degradation Model for Remaining Useful Life Prediction of Rolling Bearings under Variable Rotational Speed." Applied Sciences 12, no. 8 (2022): 4044. http://dx.doi.org/10.3390/app12084044.

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It is difficult to accurately extract the health index of non-stationary signals of rolling bearings under variable rotational speed, which also leads to greater prediction error for bearing degradation models with fixed parameters. For this reason, an angular domain unscented particle filter model with time-varying degradation parameters is proposed to deal with the remaining useful life (RUL) prediction of rolling bearings. Order analysis is first performed to transform the variable-speed signal from time domain to angular domain for extracting the health index in the angular domain represen

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3

Fan, Jiayi, Lijuan Zhao, and Minghao Li. "Research on Digital Twin Modeling and Fault Diagnosis Methods for Rolling Bearings." Sensors 25, no. 7 (2025): 2023. https://doi.org/10.3390/s25072023.

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This paper proposed a digital twin modeling method based on digital twin technology to improve the operational stability of rolling bearings and the accuracy of fault diagnosis methods. A comprehensive digital twin model for the entire lifecycle of rolling bearings was constructed using Modelica language. This model included a multi-state rolling bearing digital twin and integrated twin models for both the bearing drive and load ends. The model employed hybrid noise component to simulate the bearing’s actual operating state and degradation process with high fidelity. Based on experimental life

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4

Xiong, Ruolan, Aihua Liu, Dongfang Xu, Chunyang Qu, and Yulong Wu. "A New Heavy-Duty Bearing Degradation Evaluation Method with Multi-Domain Features." Sensors 24, no. 23 (2024): 7769. https://doi.org/10.3390/s24237769.

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Under heavy load conditions, bearings are subjected to non-uniform and frequently changing loads, which leads to randomness in the spatial distribution of bearing degradation characteristics. Aiming at the problem that the traditional degradation index cannot accurately reflect the degradation state of heavy-duty bearings in the whole life cycle, a new degradation evaluation method based on multi-domain features is proposed in this paper, which aims to capture the early degradation point of heavy-duty bearings and characterize their degradation trend. Firstly, the energy entropy feature is obt

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5

Zheng, Yuhuang. "Predicting Remaining Useful Life Based on Hilbert–Huang Entropy with Degradation Model." Journal of Electrical and Computer Engineering 2019 (February 3, 2019): 1–11. http://dx.doi.org/10.1155/2019/3203959.

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Prognostics health management (PHM) of rotating machinery has become an important process for increasing reliability and reducing machine malfunctions in industry. Bearings are one of the most important equipment parts and are also one of the most common failure points. To assess the degradation of a machine, this paper presents a bearing remaining useful life (RUL) prediction method. The method relies on a novel health indicator and a linear degradation model to predict bearing RUL. The health indicator is extracted by using Hilbert–Huang entropy to process horizontal vibration signals obtain

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6

Liang, Pan, Xudong Song, Shengqi Wang, Yuyang Cong, and Yilin Chen. "Remaining useful life prediction for rolling bearings using correlation coefficient and Kullback–Leibler divergence feature selection." Measurement Science and Technology 33, no. 2 (2021): 025005. http://dx.doi.org/10.1088/1361-6501/ac346d.

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Abstract The prediction accuracy of bearing remaining useful life (RUL) is not high due to the unreasonable stage division of bearing performance degradation and the blindness of feature selection. In order to solve this problem, RUL prediction for rolling bearings using Pearson product-moment correlation coefficient (PPMCC) and Kullback–Leibler divergence (KLIC) feature selection is proposed in this paper. First, in order to divide the bearing performance degradation status more accurately, a novel performance degradation state partition method is provided based on t-distributed stochastic ne

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7

Wang, Chenyang, Wanlu Jiang, Xukang Yang, and Shuqing Zhang. "RUL Prediction of Rolling Bearings Based on a DCAE and CNN." Applied Sciences 11, no. 23 (2021): 11516. http://dx.doi.org/10.3390/app112311516.

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Predicting the remaining useful life (RUL) of mechanical equipment can improve production efficiency while effectively reducing the life cycle cost and failure rate. This paper proposes a method for predicting the remaining service life of equipment through a combination of a deep convolutional autoencoder (DCAE) and a convolutional neural network (CNN). For rolling bearings, a health indicator (HI) could be built by combining DCAE and self-organizing map (SOM) networks, performing more advanced characterization against the original vibration data and modeling the degradation state of the roll

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8

Wang, Yaping, Chaonan Yang, Di Xu, Jianghua Ge, and Wei Cui. "Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM." Shock and Vibration 2021 (May 20, 2021): 1–15. http://dx.doi.org/10.1155/2021/6615920.

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It is significant for the evaluation and prediction of the performance degradation of rolling bearings. However, the degradation stage division of the rolling bearing performance is not obvious in traditional methods, and the prediction accuracy is low. Therefore, an Attention-LSTM method is proposed to improve the evaluation and prediction of the performance degradation of rolling bearings. First, to reduce the uncertainty of the manual intervention, performance degradation characteristic indexes of rolling bearings are evaluated and screened by the correlation, the monotonicity, and the robu

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9

Huang, Guangzhong, Wenping Lei, Xinmin Dong, Dongliang Zou, Shijin Chen, and Xing Dong. "Stage-Based Remaining Useful Life Prediction for Bearings Using GNN and Correlation-Driven Feature Extraction." Machines 13, no. 1 (2025): 43. https://doi.org/10.3390/machines13010043.

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Bearings are critical components in mechanical systems, and their degradation process typically exhibits distinct stages, making stage-based remaining useful life (RUL) prediction highly valuable. This paper presents a model that combines correlation analysis feature extraction with a Graph Neural Network (GNN)-based approach for bearing degradation stage classification and RUL prediction, aiming to achieve accurate bearing life prediction. First, the proposed Pearson–Spearman correlation metric, along with Kernel Principal Component Analysis (KPCA) and autoencoders, is used to group and fuse

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10

Zhang, Ying, Anchen Wang, and Hongfu Zuo. "Roller Bearing Performance Degradation Assessment Based on Fusion of Multiple Features of Electrostatic Sensors." Sensors 19, no. 4 (2019): 824. http://dx.doi.org/10.3390/s19040824.

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This paper presents a new method to assess the performance degradation of roller bearings based on the fusion of multiple features, with the aim of improving the early degradation detection ability of the electrostatic monitoring system. At first, a set of feature parameters of the electrostatic monitoring system indicating the normal state of the bearings are extracted from the perspective of the time domain, frequency domain and complexity. Then, the parameter set is processed to reduce the dimensions and eliminate the redundancy using spectral regression. With the processed features, a Gaus

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11

Gan, Zu Wang, Chen Lu, Hong Mei Liu, and Tian Min Shan. "Real-Time Reliability Evaluation and Life Prediction for Bearings Based on Normalized Individual State Deviation." Applied Mechanics and Materials 764-765 (May 2015): 343–49. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.343.

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Most of the existing methods for bearing real-time reliability evaluation employ real-time transformation of traditional reliability indices, performance degradation trajectory analysis, and performance degradation distribution, which are usually limited in terms of accuracy and applicability. A method for real-time reliability evaluation and life prediction for bearings based on normalized individual state deviation is proposed in this study. First, a self-organizing map neural network is utilized to obtain the individual state deviation of a running rolling bearing. Second, individual state

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12

Zhou, Qicai, Hehong Shen, Jiong Zhao, Xingchen Liu, and Xiaolei Xiong. "Degradation State Recognition of Rolling Bearing Based on K-Means and CNN Algorithm." Shock and Vibration 2019 (April 1, 2019): 1–9. http://dx.doi.org/10.1155/2019/8471732.

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Accurate degradation state recognition of rolling bearing is critical to effective condition based on maintenance for improving reliability and safety. In this work, a new architecture is proposed to recognize the degradation state of the rolling bearing. Firstly, the time-domain features including RMS, kurtosis, skewness and RMSEE, and Mel-frequency cepstral coefficients features are extracted from bearing vibration signals, which are then used as the input of k-means algorithm. These unlabeled features are clustered by k-means in order to define the different categories of the bearing degrad

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13

Song, Youshuo, Shaoqiang Xu, and Xi Lu. "A sliding sequence importance resample filtering method for rolling bearings remaining useful life prediction based on two Wiener-process models." Measurement Science and Technology 35, no. 1 (2023): 015019. http://dx.doi.org/10.1088/1361-6501/acffe3.

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Abstract The remaining useful life (RUL) prediction of rolling bearings is an important part of prognostic and health management of mechanical systems. The model based on Wiener process can describe the time variability in the degradation process of bearings. However, in practical engineering, the degradation trends of bearings are often inconsistent, and it is difficult to fit the actual degradation trends of bearings with a single Wiener process model-based filtering method. Therefore, to improve the generalization ability, this paper uses linear model and exponential model based on Wiener p

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14

Tian, Qiaoping, and Honglei Wang. "Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression." Applied Sciences 11, no. 11 (2021): 4773. http://dx.doi.org/10.3390/app11114773.

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High precision and multi information prediction results of bearing remaining useful life (RUL) can effectively describe the uncertainty of bearing health state and operation state. Aiming at the problem of feature efficient extraction and RUL prediction during rolling bearings operation degradation process, through data reduction and key features mining analysis, a new feature vector based on time-frequency domain joint feature is found to describe the bearings degradation process more comprehensively. In order to keep the effective information without increasing the scale of neural network, a

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15

Chen, Chunjun, and Lizhi Liu. "Health Assessment of Rolling Bearings Based on Multivariate State Estimation and Reliability Analysis." Applied Sciences 15, no. 10 (2025): 5396. https://doi.org/10.3390/app15105396.

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Rolling bearing is an indispensable part of mechanical rotating parts, which plays an important role in reducing friction and ensuring the rotation accuracy of rotating parts. It is necessary to carry out a health assessment of the bearing. Current health assessment methods for rolling bearings only extract strongly related feature indicators and input them into the health assessment model without considering the profound impact external conditions have on the fluctuation of feature indicators, which will lead to an inaccurate health assessment. Besides, most methods evaluating the health of r

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16

Galli, Federica, Philippe Weber, Ghaleb Hoblos, Vincent Sircoulomb, Giuseppe Fiore, and Charlotte Rostain. "Machine Learning Approach for LPRE Bearings Remaining Useful Life Estimation Based on Hidden Markov Models and Fatigue Modelling." Machines 12, no. 6 (2024): 367. http://dx.doi.org/10.3390/machines12060367.

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Ball bearings are one of the most critical components of rotating machines. They ensure shaft support and friction reduction, thus their malfunctioning directly affects the machine’s performance. As a consequence, it is necessary to monitor the health conditions of such a component to avoid major degradations which could permanently damage the entire machine. In this context, HMS (Health Monitoring Systems) and PHM (Prognosis and Health Monitoring) methodologies propose a wide range of algorithms for bearing diagnosis and prognosis. The present article proposes an end-to-end PHM approach for b

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17

Gao, Tianhong, Yuxiong Li, Xianzhen Huang, and Changli Wang. "Data-Driven Method for Predicting Remaining Useful Life of Bearing Based on Bayesian Theory." Sensors 21, no. 1 (2020): 182. http://dx.doi.org/10.3390/s21010182.

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Bearings are some of the most critical industrial parts and are widely used in various types of mechanical equipment. Bearing health status can have a significant impact on the overall equipment performance, and bearing failures often cause serious economic losses and even casualties. Thus, estimating the remaining useful life (RUL) of bearings in real time is of utmost importance. This paper proposes a data-driven RUL prediction method for bearings based on Bayesian theory. First, time-domain features are extracted from the bearing vibration signal and data are fused to build a health indicat

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18

Huang, Liangpei, Hua Huang, and Yonghua Liu. "A Fault Diagnosis Approach for Rolling Bearing Based on Wavelet Packet Decomposition and GMM-HMM." June 2019 24, no. 2 (2019): 199–209. http://dx.doi.org/10.20855/ijav.2019.24.21120.

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Considering frequency domain energy distribution differences of bearing vibration signal in the different failure modes, a rolling bearing fault pattern recognition method is proposed based on orthogonal wavelet packet decomposition and Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). The orthogonal three-layer wavelet packet decomposition is used to obtain wavelet packet decomposition coefficients from low frequency to high frequency. Rolling bearing raw vibration signals are firstly decomposed into the wavelet signals of different frequency bands, then different frequency band signals a

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19

Liu, Chang, Haiyang Wu, Gang Cheng, Hui Zhou, and Yusong Pang. "Rolling Bearing Degradation Identification Method Based on Improved Monopulse Feature Extraction and 1D Dilated Residual Convolutional Neural Network." Sensors 25, no. 14 (2025): 4299. https://doi.org/10.3390/s25144299.

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To address the challenges of extracting rolling bearing degradation information and the insufficient performance of conventional convolutional networks, this paper proposes a rolling bearing degradation state identification method based on the improved monopulse feature extraction and a one-dimensional dilated residual convolutional neural network (1D-DRCNN). First, the fault pulse envelope waveform features are extracted through phase scanning and synchronous averaging, and a two-stage grid search strategy is employed to achieve FCC calibration. Subsequently, a 1D-DRCNN model is constructed t

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20

Zhou, Bingxu, and Yang Yu. "Prediction of the Remaining Life of Rolling Bearings based on the Classification of Degradation States." Frontiers in Computing and Intelligent Systems 11, no. 3 (2025): 25–28. https://doi.org/10.54097/gpc2vf07.

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view of the fact that the healthy running time of rolling bearings in actual working conditions lasts for a long time, and the vibration signal fluctuation in the healthy operation stage is relatively stable, and it is difficult to extract useful degradation information from them, a remaining life prediction model based on the classification of degradation states based on the hidden Markov model is proposed. Firstly, the characteristics of the bearing vibration signal are extracted and the dimension reduction is carried out, and then the RMS is used as the observation sequence to divide the de

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21

Yang, Yongjie, Liumeng Sun, and Ningtao Zhang. "An MTBWO Algorithm Based on BiGRU Model." Electronics 13, no. 7 (2024): 1195. http://dx.doi.org/10.3390/electronics13071195.

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To address the challenge of distinguishing the health status of bearings, in this paper, a health index (HI) is developed through utilization of the multiple target time-varying black widow optimization–bidirectional gating recurrent unit (MTBWO-BiGRU) model and the Bray–Curtis distance. This index offers a visual representation of the health status of bearings, enabling more intuitive monitoring and prediction. The first step involves utilizing L1 regularization to extract effective features as degradation elements from the current bearing vibration data. Additionally, the characteristics of

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22

Zhu, Keheng. "Performance degradation assessment of rolling element bearings based on hierarchical entropy and general distance." Journal of Vibration and Control 24, no. 14 (2017): 3194–205. http://dx.doi.org/10.1177/1077546317702030.

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Performance degradation assessment is crucial to realize equipment’s near-zero downtime and maximum productivity. In this paper, a new method for performance degradation assessment of rolling element bearings is proposed based on hierarchical entropy (HE) and general distance. First, considering the nonlinear dynamic characteristics of bearing vibration signals, the HE method is utilized to extract feature vectors, which can obtain more bearing state information hidden in the vibration signals than sample entropy (SampEn) and multi-scale entropy (MSE). Then, the general distance between the fe

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23

Zhu, Keheng, Xiaohui Jiang, Liang Chen, and Haolin Li. "Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy." Measurement Science Review 17, no. 5 (2017): 219–25. http://dx.doi.org/10.1515/msr-2017-0026.

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Abstract Rolling element bearings are an important unit in the rotating machines, and their performance degradation assessment is the basis of condition-based maintenance. Targeting the non-linear dynamic characteristics of faulty signals of rolling element bearings, a bearing performance degradation assessment approach based on improved fuzzy entropy (FuzzyEn) is proposed in this paper. FuzzyEn has less dependence on data length and achieves more freedom of parameter selection and more robustness to noise. However, it neglects the global trend of the signal when calculating similarity degree

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24

Yu, He, Hong-ru Li, Zai-ke Tian, and Wei-guo Wang. "Rolling Bearing Degradation State Identification Based on LPP Optimized by GA." International Journal of Rotating Machinery 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9281098.

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In view of the problem that the actual degradation status of rolling bearing has a poor distinguishing characteristic and strong fuzziness, a rolling bearing degradation state identification method based on multidomain feature fusion and dimension reduction of manifold learning combined with GG clustering is proposed. Firstly, the rolling bearing all-life data is preprocessed by local characteristic-scale decomposition (LCD) and six typical features including relative energy spectrum entropy (LREE), relative singular spectrum entropy (LRSE), two-element multiscale entropy (TMSE), standard devi

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25

Yusof, N. F. M., and Z. M. Ripin. "The Effect of Lubrication on the Vibration of Roller Bearings." MATEC Web of Conferences 217 (2018): 01004. http://dx.doi.org/10.1051/matecconf/201821701004.

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Proper lubrication is crucial to ensure smooth operation in machineries. In rolling bearing, the improper lubrication may induce high friction and vibration level due to metal to metal contact between the rolling elements. In this study, the roller bearings with and without lubrication are investigated. the natural surface degradation of the roller bearing is monitored and the surface roughness is measured for the lubricant film thickness calculation. the film thickness is determined by the Hamrock-Dowson equation which showed that the grease lubricated bearing operated under the elastro-hydro

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26

Jiang, Changhong, Xinyu Liu, Yizheng Liu, Mujun Xie, Chao Liang, and Qiming Wang. "A Method for Predicting the Remaining Life of Rolling Bearings Based on Multi-Scale Feature Extraction and Attention Mechanism." Electronics 11, no. 21 (2022): 3616. http://dx.doi.org/10.3390/electronics11213616.

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In response to the problems of difficult identification of degradation stage start points and inadequate extraction of degradation features in the current rolling bearing remaining life prediction method, a rolling bearing remaining life prediction method based on multi-scale feature extraction and attention mechanism is proposed. Firstly, this paper takes the normalized bearing vibration signal as input and adopts a quadratic function as the RUL prediction label, avoiding identifying the degradation stage start point. Secondly, the spatial and temporal features of the bearing vibration signal

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27

Sun, Zhiwei, Xiong Hu, and Kai Dong. "Remaining Useful Life Prediction of Quay Crane Hoist Gearbox Bearing under Dynamic Operating Conditions Based on ARIMA-CAPF Framework." Shock and Vibration 2021 (December 21, 2021): 1–13. http://dx.doi.org/10.1155/2021/9403401.

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The remaining useful life (RUL) prediction of quay crane (QC) bearings is of great significance to port production safety. An RUL prediction framework of QC bearing under dynamic conditions is proposed. Firstly, the load is discretized, and the corresponding operating conditions are classified. Then, the Autoregressive Integrated Moving Average (ARIMA) model is utilized to predict the load and corresponding operating conditions. Secondly, a Wiener process considering degradation rates and jump coefficients under different operating conditions is developed as the state transfer function. Finall

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28

Nistane, Vinod, and Suraj Harsha. "Performance evaluation of bearing degradation based on stationary wavelet decomposition and extra trees regression." World Journal of Engineering 15, no. 5 (2018): 646–58. http://dx.doi.org/10.1108/wje-12-2017-0403.

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Purpose In rotary machines, the bearing failure is one of the major causes of the breakdown of machinery. The bearing degradation monitoring is a great anxiety for the prevention of bearing failures. This paper aims to present a combination of the stationary wavelet decomposition and extra-trees regression (ETR) for the evaluation of bearing degradation. Design/methodology/approach The higher order cumulants features are extracted from the bearing vibration signals by using the stationary wavelet decomposition (stationary wavelet transform [SWT]). The extracted features are then subjected to t

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29

Jiang, Lingli, Heshan Sheng, Tongguang Yang, Hujiao Tang, Xuejun Li, and Lianbin Gao. "A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model." Sensors 23, no. 18 (2023): 7696. http://dx.doi.org/10.3390/s23187696.

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Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment’s smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper proposes a new strategy for bearing health assessment based on a model-driven dynamic interval prediction model. Firstly, the mapping proportion algorithm is used to determine whether the measured data are in the degradation stage. After finding the starting point of prediction, the improved annealing algo

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Liu, Zhiliang, Ming J. Zuo, and Yong Qin. "Remaining useful life prediction of rolling element bearings based on health state assessment." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 2 (2015): 314–30. http://dx.doi.org/10.1177/0954406215590167.

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Instead of looking for an overall regression model for remaining useful life (RUL) prediction, this paper proposes a RUL prediction framework based on multiple health state assessment that divides the entire bearing life into several health states where a local regression model can be built individually. A hybrid approach consisting of both unsupervised learning and supervised learning is proposed to automatically estimate the real-time health state of a bearing in cases with no prior knowledge available. Support vector machine is the main technology adopted to implement health state assessmen

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31

Zheng, Pan, Wenqin Zhao, Yaqiong Lv, Lu Qian, and Yifan Li. "Health Status-Based Predictive Maintenance Decision-Making via LSTM and Markov Decision Process." Mathematics 11, no. 1 (2022): 109. http://dx.doi.org/10.3390/math11010109.

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Maintenance decision-making is essential to achieve safe and reliable operation with high performance for equipment. To avoid unexpected shutdown and increase machine life as well as system efficiency, it is fundamental to design an effective maintenance decision-making scheme for equipment. In this paper, we propose a novel maintenance decision-making method for equipment based on Long Short-Term Memory (LSTM) and Markov decision process, which can provide specific maintenance strategies in different degradation stages of the system. Specifically, the LSTM model is firstly applied to predict

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Pham, Minh Tuan, Jong-Myon Kim, and Cheol Hong Kim. "Accurate Bearing Fault Diagnosis under Variable Shaft Speed using Convolutional Neural Networks and Vibration Spectrogram." Applied Sciences 10, no. 18 (2020): 6385. http://dx.doi.org/10.3390/app10186385.

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Predicting bearing faults is an essential task in machine health monitoring because bearings are vital components of rotary machines, especially heavy motor machines. Moreover, indicating the degradation level of bearings will help factories plan maintenance schedules. With advancements in the extraction of useful information from vibration signals, diagnosis of motor failures by maintenance engineers can be gradually replaced by an automatic detection process. Especially, state-of-the-art methods using deep learning have contributed significantly to automatic fault diagnosis. This paper propo

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Xu, Juan, Bin Ma, Weiwei Chen, and Chengwei Shan. "AsdinNorm: A Single-Source Domain Generalization Method for the Remaining Useful Life Prediction of Bearings." Lubricants 12, no. 5 (2024): 175. http://dx.doi.org/10.3390/lubricants12050175.

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The remaining useful life (RUL) of bearings is vital for the manipulation and maintenance of industrial machines. The existing domain adaptive methods have achieved major achievements in predicting RUL to tackle the problem of data distribution discrepancy between training and testing sets. However, they are powerless when the target bearing data are not available or unknown for model training. To address this issue, we propose a single-source domain generalization method for RUL prediction of unknown bearings, termed as the adaptive stage division and parallel reversible instance normalizatio

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Xin, Hongwei, Haidong Zhang, Yanjun Yang, and Jianguo Wang. "Evaluation of Rolling Bearing Performance Degradation Based on Comprehensive Index Reduction and SVDD." Machines 10, no. 8 (2022): 677. http://dx.doi.org/10.3390/machines10080677.

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The evaluation of rolling bearing performance degradation has important implications for the prediction and health management (PHM) of rotating equipment. A method for evaluation of rolling bearing performance degradation based on comprehensive index reduction and support vector data description (SVDD) is proposed in this study. Firstly, the improved variational mode decomposition (VMD) method was used to decompose vibration signals, and the defect frequency amplitude ratio index which is sensitive to early faults is extracted. Secondly, a comprehensive feature index set of rolling bearings is

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Li, Xianling, Kai Zhang, Weijun Li, Yi Feng, and Ruonan Liu. "A Two-Stage Transfer Regression Convolutional Neural Network for Bearing Remaining Useful Life Prediction." Machines 10, no. 5 (2022): 369. http://dx.doi.org/10.3390/machines10050369.

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Recently, deep learning techniques have been successfully used for bearing remaining useful life (RUL) prediction. However, the degradation pattern of bearings can be much different from each other, which leads to the trained model usually not being able to work well for RUL prediction of a new bearing. As a method that can adapt a model trained on source datasets to a different but relative unlabeled target dataset, transfer learning shows the potential to solve this problem. Therefore, we propose a two-stage transfer regression (TR)-based bearing RUL prediction method. Firstly, the incipient

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Hotait, Hassane, Xavier Chiementin, and Lanto Rasolofondraibe. "Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis." Entropy 23, no. 7 (2021): 791. http://dx.doi.org/10.3390/e23070791.

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This paper suggests a new method to predict the Remaining Useful Life (RUL) of rolling bearings based on Long Short Term Memory (LSTM), in order to obtain the degradation condition of the rolling bearings and realize the predictive maintenance. The approach is divided into three parts: the first part is the clustering to detect the damage state by the density-based spatial clustering of applications with noise. The second one is the health indicator construction which could give a better reflection of the bearing degradation tendency and is selected as the input for the prediction model. In th

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Chen, Zhipeng, Haiping Zhu, Liangzhi Fan, and Zhiqiang Lu. "Health Indicator Similarity Analysis-Based Adaptive Degradation Trend Detection for Bearing Time-to-Failure Prediction." Electronics 12, no. 7 (2023): 1569. http://dx.doi.org/10.3390/electronics12071569.

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Time-to-failure (TTF) prediction of bearings is vital to the prognostic and health management of rotating machines. Owing to the shifty degradation trends (DTs) of bearings, it is still difficult to obtain accurate TTF prognostic results. To solve this problem, this paper proposes an online, continuously updated TTF prognostic method based on health indicator (HI) similarity analysis and DT detection. First, multiple degradation features are extracted and fused to construct principal component HI by using dynamic principal component analysis. Next, exponential degradation models are fitted usi

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Tian, Miao, Xiaoming Su, Changzheng Chen, and Wenjie An. "A Novel Method for Multistage Degradation Predicting the Remaining Useful Life of Wind Turbine Generator Bearings Based on Domain Adaptation." Applied Sciences 13, no. 22 (2023): 12332. http://dx.doi.org/10.3390/app132212332.

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Predicting the remaining useful life (RUL) of wind turbine generator rolling bearings can effectively prevent damage to the transmission chain and significant economic losses resulting from sudden failures. However, the working conditions of generator bearings are variable, and the collected run-to-failure data combine multiple working conditions, which significantly impacts the accuracy of model predictions. To solve the problem, a local enhancement temporal convolutional network with multistage degenerate distribution matching based on domain adaptation (MDA-LETCN) is proposed, extracting de

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Dong, Shaojiang, Yang Li, Peng Zhu, et al. "Rolling bearing performance degradation assessment based on singular value decomposition-sliding window linear regression and improved deep learning network in noisy environment." Measurement Science and Technology 33, no. 4 (2022): 045015. http://dx.doi.org/10.1088/1361-6501/ac39d1.

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Abstract It is difficult to evaluate the degradation performance and the degradation state of a rolling bearing in noisy environment. A new method is proposed to solve the problem based on singular value decomposition (SVD)-sliding window linear regression and ResNeXt–multi-attention mechanism’s deep neural network (RMADNN). Firstly, the root mean square (rms) gradient value is calculated on the basis of rms based on SVD and sliding window linear regression, which is used as the rolling bearing performance degradation indicator in noisy environment. Secondly, the degradation state of the rolli

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Pan, Y. N., J. Chen, and G. M. Dong. "A hybrid model for bearing performance degradation assessment based on support vector data description and fuzzy c-means." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 223, no. 11 (2009): 2687–95. http://dx.doi.org/10.1243/09544062jmes1447.

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Bearing performance degradation assessment is more effective than fault diagnosis to realize condition-based maintenance. In this article, a hybrid model is proposed for it based on a support vector data description (SVDD) and fuzzy c-means (FCM). SVDD, which holds excellent robustness to outliers, is used to obtain the clustering centre of normal state. The subjection of tested data to normal state is defined as a degradation indicator, which is computed by a FCM algorithm with final failure data. The results of applying this hybrid model to an accelerated bearing life test show that it can e

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Gan, Zu-wang, Jian Ma, Chen Lu, Hongmei Liu, and Tian-min Shan. "REAL-TIME RELIABILITY ASSESSMENT AND LIFETIME PREDICTION FOR BEARINGS USING THE INDIVIDUAL STATE DEVIATION BASED ON THE MANIFOLD DISTANCE." Transactions of the Canadian Society for Mechanical Engineering 39, no. 3 (2015): 691–703. http://dx.doi.org/10.1139/tcsme-2015-0055.

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In recent years, the real-time reliability evaluation and life prediction for rolling bearings has attracted more attention. Most of the existing methods employ real-time transformation of traditional reliability indices, performance degradation trajectory or distribution analysis, which usually have certain limitations in terms of accuracy and applicability. This paper proposes a method for bearing real-time reliability evaluation and life prediction to avoid the negligence of real-time transformation of the monitored individual, as well as reduce the errors caused by the randomness from indi

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Kumar, Satish, Paras Kumar, and Girish Kumar. "Degradation assessment of bearing based on machine learning classification matrix." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 2 (2021): 395–404. http://dx.doi.org/10.17531/ein.2021.2.20.

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In the broad framework of degradation assessment of bearing, the final objectives of bearing condition monitoring is to evaluate different degradation states and to estimate the quantitative analysis of degree of performance degradation. Machine learning classification matrices have been used to train models based on health data and real time feedback. Diagnostic and prognostic models based on data driven perspective have been used in the prior research work to improve the bearing degradation assessment. Industry 4.0 has required the research in advanced diagnostic and prognostic algorithm to

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Yan, Shiwei, Haining Liu, Fajia Li, and Huanyong Cui. "An integrated condition monitoring method for rolling element bearings based on perceptual vibration hashing and SOM." IOP Conference Series: Materials Science and Engineering 1207, no. 1 (2021): 012012. http://dx.doi.org/10.1088/1757-899x/1207/1/012012.

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Abstract Rolling element bearings are widely used in rotating machinery. Bearing faults will result in damage to property. So, the condition monitoring of bearings is of great significance, but few methods can achieve both degradation assessment and fault diagnosis. In this paper, an integrated condition monitoring method for rolling element bearings based on perceptual vibration hashing (PVH) and self-organizing maps (SOM) is proposed. Distance matric based on PVH is used as a health indicator for degradation assessment, in which the baseline of healthy state is selected based on the clusteri

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Mao, Wentao, Jianliang He, Jiamei Tang, and Yuan Li. "Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network." Advances in Mechanical Engineering 10, no. 12 (2018): 168781401881718. http://dx.doi.org/10.1177/1687814018817184.

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For bearing remaining useful life prediction problem, the traditional machine-learning-based methods are generally short of feature representation ability and incapable of adaptive feature extraction. Although deep-learning-based remaining useful life prediction methods proposed in recent years can effectively extract discriminative features for bearing fault, these methods tend to less consider temporal information of fault degradation process. To solve this problem, a new remaining useful life prediction approach based on deep feature representation and long short-term memory neural network

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Li, Xiao, Songyang An, Yuanyuan Shi, and Yizhe Huang. "Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm." Machines 10, no. 9 (2022): 729. http://dx.doi.org/10.3390/machines10090729.

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Rolling bearings are an important part of rotating machinery, and are of great significance for fault diagnosis and life monitoring of rolling bearings. Analyzing fault signals, extracting effective degradation information and establishing corresponding models are the premise of residual life prediction of rolling bearings. In this paper, first, the time-domain features were extracted to form the eigenvector of the vibration signal, and then the index representing the bearing degradation was found. It was found that the time-domain index could effectively describe the degradation information o

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Chen, Baiyan, Hongru Li, He Yu, and Yukui Wang. "A Hybrid Domain Degradation Feature Extraction Method for Motor Bearing Based on Distance Evaluation Technique." International Journal of Rotating Machinery 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/2607254.

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The vibration signal of the motor bearing has strong nonstationary and nonlinear characteristics, and it is arduous to accurately recognize the degradation state of the motor bearing with traditional single time or frequency domain indexes. A hybrid domain feature extraction method based on distance evaluation technique (DET) is proposed to solve this problem. Firstly, the vibration signal of the motor bearing is decomposed by ensemble empirical mode decomposition (EEMD). The proper intrinsic mode function (IMF) component that is the most sensitive to the degradation of the motor bearing is se

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Wen, Haobin, Long Zhang, and Jyoti K. Sinha. "Early Prediction of Remaining Useful Life for Rolling Bearings Based on Envelope Spectral Indicator and Bayesian Filter." Applied Sciences 14, no. 1 (2024): 436. http://dx.doi.org/10.3390/app14010436.

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On top of the condition-based maintenance (CBM) practice for rotating machinery, the robust estimation of remaining useful life (RUL) for rolling-element bearings (REB) is of particular interest. The failure of a single bearing often results in secondary defects in the connected structure and catastrophic system failures. The prediction of RUL facilitates proactive maintenance planning to ensure system reliability and minimize financial loss due to unscheduled downtime. In this paper, to acquire early and reliable estimations of useful life, the RUL prediction of REBs is formulated into nonlin

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Cheng, Li, Wensuo Ma, and Zuobin Gao. "A New Approach to the Degradation Stage Prediction of Rolling Bearings Using Hierarchical Grey Entropy and a Grey Bootstrap Markov Chain." Sensors 23, no. 22 (2023): 9082. http://dx.doi.org/10.3390/s23229082.

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Degradation stage prediction, which is crucial to monitoring the health condition of rolling bearings, can improve safety and reduce maintenance costs. In this paper, a novel degradation stage prediction method based on hierarchical grey entropy (HGE) and a grey bootstrap Markov chain (GBMC) is presented. Firstly, HGE is proposed as a new entropy that measures complexity, considers the degradation information embedded in both lower- and higher-frequency components and extracts the degradation features of rolling bearings. Then, the HGE values containing degradation information are fed to the p

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Louahem M’Sabah, Hanene, Azzedine Bouzaouit, and Ouafae Bennis. "Simulation of Bearing Degradation by the Use of the Gamma Stochastic Process." Mechanics and Mechanical Engineering 22, no. 4 (2020): 1309–18. http://dx.doi.org/10.2478/mme-2018-0101.

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AbstractAn effective predictive maintenance reposed on modeling, simulation, and on supervisory and prognostic techniques used to model the various phenomena. On this basis, and based on significant knowledge and parameters, we propose an approach based on stochastic processes that represent a mathematical structure for simulation, mainly the processes of continuous degradation and more particularly the Gamma process. Our work is devoted to the monitoring of the degradation process of the bearings at the level of a motor pump and makes it possible to evaluate the limiting operating time, as we

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Li, Jiahui, Zhihai Wang, Xiaoqin Liu, and Zhengjiang Feng. "Remaining Useful Life Prediction of Rolling Bearings Using GRU-DeepAR with Adaptive Failure Threshold." Sensors 23, no. 3 (2023): 1144. http://dx.doi.org/10.3390/s23031144.

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Aiming at the problem that a single neural network model has difficulty in accurately predicting trends of the remaining useful life of a rolling bearing, a method of predicting the remaining useful life of rolling bearings using a gated recurrent unit-deep autoregressive model (GRU-DeepAR) with an adaptive failure threshold was proposed. First, time domain and frequency domain features were extracted from the rolling bearing vibration signal. Second, its operation process was divided into a smooth operation stage and degradation stage according to the trend of the accumulated root mean square

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