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1
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 obtained from bearings. We present a linear degradation model to estimate RUL using this health indicator. In the training phase, the degradation detection threshold and the failure threshold of this model are estimated by the distribution of 600 bootstrapped samples. These bootstrapped samples are taken from the six training sets. In the test phase, the health indicator and the model are used to estimate the bearing’s current health state and predict its RUL. This method is suitable for the degradation of bearings. The experimental results show that this method can effectively monitor bearing degradation and predict its RUL.
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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 robustness. Second, the original characteristic indicator curve is divided into the Health Indicator (HI) curve and the residual curve by means of fixed-window averaging to quantitatively and intuitively reflect the deterioration degree of the rolling bearing performance. Finally, the Attention mechanism is combined with the LSTM model, and a scoring function is established to enhance the prediction accuracy. The scoring function is used to adjust the intermediate output state weight of the LSTM model and improve the prediction accuracy. The appropriate network structure and the parameter configuration are determined, and the prediction model of rolling bearing degradation performance is established. Compared with other models, the method proposed by this paper makes full use of the historical data and is more sensitive to the key information in the long time series, and the eRMSE index and the eMAE index of the two sets of experimental data are minimum, and the prediction accuracy of rolling bearing degradation performance is higher. The model has the strong robustness and the generalization ability, which has the important engineering practical value for the prediction of the equipment health state.
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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 deviation is normalized into a state deviation degree, which is used to formulate a modified real-time reliability model for the realization of real-time reliability evaluation and residual life prediction. The proposed method combines population information with real-time monitoring information of individual bearings, and thus avoids the negligence of the real-time transformation of the monitored individual. The errors caused by the randomness of the individual bearing operational process are also reduced. Finally, the feasibility and efficiency of the proposed method is validated by performing run-to-failure experiments on bearings.
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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 degradation state. In this way, the original vibration signals can be labeled. Then, the convolutional neural network recognition model is built, which takes the bearing vibration signals as input, and outputs the degradation state category. So, interference brought by human factors can be eliminated, and further, the bearing degradation can be grasped so as to make maintenance plan in time. The proposed method was tested by bearing run-to-failure dataset provided by the Center for Intelligent Maintenance System, and the result proved the feasibility and reliability of the methodology.
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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 (February 17, 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 Gaussian mixed model is established to gauge the health of the bearing, providing the distance value obtained using Bayesian inference as a quantitative indicator for assessing the performance degradation. The method is applied to access the life of a bearing in which the mechanic fatigue is artificially accelerated. The test results show that the proposed method can better reflect the degradation process of the bearing compared to other evaluation methods. This enables the electrostatic monitoring technique to detect the degradation of the bearing earlier than the vibration monitoring, providing a powerful tool for the condition monitoring of roller bearings.
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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 (May 23, 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 joint feature compression calculation method based on redefined degradation indicator (DI) was proposed to determine the input data set. By combining the temporal convolution network with the quantile regression (TCNQR) algorithm, the probability density forecasting at any time is achieved based on kernel density estimation (KDE) for the conditional distribution of predicted values. The experimental results show that the proposed method can obtain the point prediction results with smaller errors. Compared with the existing quantile regression of long short-term memory network(LSTMQR), the proposed method can construct more accurate prediction interval and probability density curve, which can effectively quantify the uncertainty of bearing running state.
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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 (December 29, 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 indicator (HI) and a state model of bearing degradation. Then, according to Bayesian theory, a Bayesian model of state parameters and bearing life is established. The parameters of the Bayesian model are updated and bearing RUL is predicted by the Metropolis–Hastings algorithm. The method was validated by the XJTU-SY bearing open datasets and the prediction results are compared with the existing methods. Accuracy of the proposed method was demonstrated.
8
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 (June 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 are reconstructed respectively to extract energy features, which form feature vectors as the model input of GMM-HMM. A large number of samples are trained to get model parameters for different bearing faults, then several groups of test data are adopted to verify GMM-HMMs so different fault types of rolling bearings are recognized. By calculating the current state appearance probability of monitoring data in GMM-HMMs, different failure patterns are recognized and evaluated from the maximum probability. Similarly, we establish GMM-HMMs for different grade fault samples and evaluated the performance degradation state. Test results show that the proposed fault diagnosis approach can identify accurately the fault pattern of rolling bearings and evaluate performance degradation of bearings.
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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 deviation (STD), RMS, and root-square amplitude (XR) are extracted and compose the original multidomain feature set. And then, locally preserving projection (LPP) is utilized to reduce dimension of original fusion feature set and genetic algorithm is applied to optimize the process of feature fusion. Finally, fuzzy recognition of rolling bearing degradation state is carried out by GG clustering and the principle of maximum membership degree and excellent performance of the proposed method is validated by comparing the recognition accuracy of LPP and GA-LPP.
10
Zhu, Keheng. "Performance degradation assessment of rolling element bearings based on hierarchical entropy and general distance." Journal of Vibration and Control 24, no. 14 (April 5, 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 feature vectors of the normal data and those of the tested data is designed as a degradation indicator by combining Euclidean distance and cosine angle distance. The experimental results indicate that this indicator can detect the incipient defects well and can effectively reflect the whole degradation process of rolling element bearings. Moreover, the designed indicator has some advantages over kurtosis and root mean square (RMS) values.
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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 (October 1, 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 of two vectors, and thus cannot reflect the running state of the rolling element bearings accurately. Based on this consideration, the algorithm of FuzzyEn is improved in this paper and the improved FuzzyEn is utilized as an indicator for bearing performance degradation evaluation. The vibration data from run-to-failure test of rolling element bearings are used to validate the proposed method. The experimental results demonstrate that, compared with the traditional kurtosis and root mean square, the proposed method can detect the incipient fault in advance and can reflect the whole performance degradation process more clearly.
12
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-hydrodynamic lubrication, with the ratio of lubrication film thickness to the surface roughness of λ in the range of 0.9 to 3.65. the un-lubricated bearing was damaged after 20 minutes whereas the grease lubricated bearing continued to operate for 6600 minutes. the observation under microscope showed that the surface underwent smoothening process where the surface roughness decreases initially (running-in state) followed by roughening at the steady state where the surface roughness increases. At damage, the value of λ = 0.9 can be associated with the high level of the bearing vibration. the increase of vibration level becomes rapid at the critical value of λ = 1.6. As such the overall vibration level of the bearing can be related to the surface degradation and low film thickness.
13
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 (June 3, 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 assessment and RUL prediction. Experimental results on accelerated degradation tests of rolling element bearings demonstrate the effectiveness of the proposed framework.
14
Hotait, Hassane, Xavier Chiementin, and Lanto Rasolofondraibe. "Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis." Entropy 23, no. 7 (June 22, 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 the third part of the RUL prediction, the LSTM approach is employed to improve the accuracy of the prediction. The rationale of this work is to combine the two methods—the density-based spatial clustering of applications with noise and LSTM—to identify the abnormal state in rolling bearings, then estimate the RUL. The suggested method is confirmed by experimental data of bearing life cycle, and the RUL prediction results of the model LSTM are compared with the nonlinear au-regressive model with exogenous input model. In addition, the constructed health indicator is compared with the spectral kurtosis feature. The results demonstrated that the suggested method is more appropriate than the nonlinear au-regressive model with exogenous input model for the prediction of bearing RUL.
15
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 (October 1, 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 the ETR for obtaining normal and failure state. A dominance level curve build using the dissimilarity data of test object and retained as health degradation indicator for the evaluation of bearing health. Findings Experiment conducts to verify and assess the effectiveness of ETR for the evaluation of performance of bearing degradation. To justify the preeminence of recommended approach, it is compared with the performance of random forest regression and multi-layer perceptron regression. Originality/value The experimental results indicated that the presently adopted method shows better performance for detecting the degradation more accurately at early stage. Furthermore, the diagnostics and prognostics have been getting much attention in the field of vibration, and it plays a significant role to avoid accidents.
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Yu, He, Hongru Li, and Baohua Xu. "Rolling Bearing Degradation State Identification Based on LCD Relative Spectral Entropy." Journal of Failure Analysis and Prevention 16, no. 4 (June 28, 2016): 655–66. http://dx.doi.org/10.1007/s11668-016-0133-y.
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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 (September 13, 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 proposes a novel method for diagnosing bearing faults and their degradation level under variable shaft speed. In the proposed method, vibration signals are represented by spectrograms to apply deep learning methods through preprocessing using Short-Time Fourier Transform (STFT). Then, feature extraction and health status classification are performed by a convolutional neural network (CNN), VGG16. According to our various experiments, our proposed method can achieve very high accuracy and robustness for bearing fault diagnosis even under noisy environments.
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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 (September 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 individual bearing operational process. The individual state deviation of a running rolling bearing geometrically measured by manifold distance is normalized into a state deviation degree, which is used to formulate a modified real-time reliability model for realtime reliability evaluation and lifetime prediction. Finally, the feasibility and efficiency of this method is validated by bearing run-to-failure experiments.
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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 (July 10, 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 effectively assess bearing performance degradation. Furthermore, it is robust to the outliers in the training set and is not influenced by the Gaussian kernel parameter.
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Cheng, Chao, Weijun Wang, Hao Luo, Bangcheng Zhang, Guoli Cheng, and Wanxiu Teng. "State-Degradation-Oriented Fault Diagnosis for High-Speed Train Running Gears System." Sensors 20, no. 4 (February 13, 2020): 1017. http://dx.doi.org/10.3390/s20041017.
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As one of the critical components of high-speed trains, the running gears system directly affects the operation performance of the train. This paper proposes a state-degradation-oriented method for fault diagnosis of an actual running gears system based on the Wiener state degradation process and multi-sensor filtering. First of all, for the given measurements of the high-speed train, this paper considers the information acquisition and transfer characteristics of composite sensors, which establish a distributed topology for axle box bearing. Secondly, a distributed filtering is built based on the bilinear system model, and the gain parameters of the filter are designed to minimize the mean square error. For a better presentation of the degradation characteristics in actual operation, this paper constructs an improved nonlinear model. Finally, threshold is determined based on the Chebyshev’s inequality for a reliable fault diagnosis. Open datasets of rotating machinery bearings and the real measurements are utilized in the case studies to demonstrate the effectiveness of the proposed method. Results obtained in this paper are consistent with the actual situation, which validate the proposed methods.
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Dong, Shaojiang, Dihua Sun, Baoping Tang, Zhengyuan Gao, Yingrui Wang, Wentao Yu, and Ming Xia. "Bearing degradation state recognition based on kernel PCA and wavelet kernel SVM." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, no. 15 (December 11, 2014): 2827–34. http://dx.doi.org/10.1177/0954406214563235.
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In order to effectively recognize the bearing’s running state, a new method based on kernel principal component analysis (KPCA) and the Morlet wavelet kernel support vector machine (MWSVM) was proposed. First, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD) to obtain the corresponding intrinsic mode function (IMF). The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. Therefore, the nonlinear feature extraction method KPCA was introduced to extract the characteristic features and to reduce the dimension. The extracted characteristic features were inputted into the MWSVM to train and construct the running state identification model, and the bearing’s running state identification was thereby realized. Cases of test and actual were analyzed. The results validate the effectiveness of the proposed algorithm.
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Tao, Laifa, Lipin Zhang, and Chen Lu. "Curve similarity recognition based rolling bearing degradation state estimation and lifetime prediction." Journal of Vibroengineering 18, no. 5 (August 15, 2016): 2839–54. http://dx.doi.org/10.21595/jve.2016.17377.
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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 (March 27, 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 enhance the accuracy of models. A classification model which is based on machine learning classification matrix to assess the degradation of bearing is proposed to improve the accuracy of classification model. Review work demonstrates the comparisons among the available state-of-the-art methods. In the end, unexplored research technical challenges and niches of opportunity for future researchers are discussed.
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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 (September 2, 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 well as the evolution in time of the change of state. This methodology allows us to develop a mathematical model that describes the process of bearing degradation, thus providing a good prediction of failures and efficient maintenance planning for systems whose behavior is only partially predictable.
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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 (December 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 is proposed in this article. First, a new criterion, named support vector data normalized correlation coefficient, is proposed to automatically divide the whole bearing life as normal state and fast degradation state. Second, deep features of bearing fault with good representation ability can be obtained from convolutional neural network by means of the marginal spectrum in Hilbert–Huang transform of raw vibration signals and health state label. Finally, by considering the temporal information of degradation process, these features are fed into a long short-term memory neural network to construct a remaining useful life prediction model. Experiments are conducted on bearing data sets of IEEE PHM Challenge 2012. The results show the significance of performance improvement of the proposed method in terms of predictive accuracy and numerical stability.
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Han, Te, Dong Xiang Jiang, and Wen Guang Yang. "Degradation State Assessment of Rolling Bearing Based on Variational Mode Decomposition and Energy Distribution." Key Engineering Materials 754 (September 2017): 371–74. http://dx.doi.org/10.4028/www.scientific.net/kem.754.371.
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Degradation state assessment of bearing is an important part of prognostic and health management (PHM) in rotating machinery. Generally, the energy distribution of frequency band is sensitive to degradation state for rolling bearing. Hence, a novel assessment method based on variational mode decomposition (VMD) and energy distribution is proposed in this work. Firstly, the VMD is used to decompose raw vibration signal into several components with different scales and frequency bands. These components is capable of reflecting the local characteristic of vibration signal. Then, the energy distribution of these components is utilized as feature vector. Finally, the different bearing states can be classified by the scatter plots of the first several principal components after principal component analysis (PCA). The analysis of an experimental dataset demonstrates the effectiveness of this methods. The comparative analysis shows the VMD is superior to traditional empirical mode decomposition (EMD) methods.
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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 selected according to the sensitive IMF selection algorithm based on the similarity evaluation. Then the distance evaluation factor of each characteristic parameter is calculated by the DET method. The differential method is used to extract sensitive characteristic parameters which compose the characteristic matrix. And then the extracted degradation characteristic matrix is used as the input of support vector machine (SVM) to identify the degradation state. Finally, It is demonstrated that the proposed hybrid domain feature extraction method has higher recognition accuracy and shorter recognition time by comparative analysis. The positive performance of the method is verified.
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Feng, Yi, Dawei Hu, Mo Tao, Zhiwu Ke, and Zhaoxu Chen. "DEGRADATION STAGE RECOGNITION OF BEARING WITHIN LIFE CYCLE." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2019.27 (2019): 1240. http://dx.doi.org/10.1299/jsmeicone.2019.27.1240.
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Tian, Qiaoping, and Honglei Wang. "An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction." Applied Sciences 10, no. 1 (January 2, 2020): 346. http://dx.doi.org/10.3390/app10010346.
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The prediction of the remaining life of a bearing plays a vital role in reducing the accident-related maintenance costs of machinery and in improving the reliability of machinery and equipment. To predict bearing remaining useful life (RUL), the abilities of statistical characteristics to reflect the bearing degradation state differ, and the single prediction model has low generalization ability and a poor prediction effect. An ensemble robust prediction method is proposed here to predict bearing RUL based on the construction of a bearing degradation indicator set: the initial bearing degradation indicator subsets were constructed using the Fast Correlation-Based Filter with Approximate Markov Blankets (FCBF-AMB) and Maximal Information Coefficient (MIC) selection methods. Through the cross-operation of the obtained subsets, we obtained a set of robust degradation indicators. These selected degradation indicators were fed into the long short-term memory (LSTM) neural network prediction model enhanced by the AdaBoost algorithm. We found through calculation that the average prediction accuracy of the proposed method is 91.40%, 92.04%, and 93.25% at 2100, 2250, and 2400 rpm, respectively. Compared with other methods, the proposed method improves the prediction accuracy by 1.8% to 14.87% at most. Therefore, the method proposed in this paper is more accurate than the other methods in terms of RUL prediction.
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Wen, Juan, Hongli Gao, and Jiangquan Zhang. "Bearing Remaining Useful Life Prediction Based on a Nonlinear Wiener Process Model." Shock and Vibration 2018 (June 26, 2018): 1–13. http://dx.doi.org/10.1155/2018/4068431.
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Prognostic is an essential part of condition-based maintenance, which can be employed to enhance the reliability and availability and reduce the maintenance cost of mechanical systems. This paper develops an improved remaining useful life (RUL) prediction method for bearings based on a nonlinear Wiener process model. First, the service life of bearings is divided into two stages in terms of the working condition. Then a new prognostic model is constructed to reflect the relationship between time and bearing health status. Besides, a variety of factors that cause uncertainties toward the degradation path are considered and appropriately managed to obtain reliable RUL prediction results. The particle filtering is utilized to estimate the degradation state, qualify the uncertainties, and predict the RUL. The experimental studies show that the proposed method has a better performance in RUL prediction and uncertainty management than the exponential model and the linear model.
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Liu, Fang, Liubin Li, Yongbin Liu, Zheng Cao, Hui Yang, and Siliang Lu. "HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction." Sensors 20, no. 3 (January 24, 2020): 660. http://dx.doi.org/10.3390/s20030660.
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In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.
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Mao, Wentao, Bin Sun, and Liyun Wang. "A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault." Entropy 23, no. 2 (January 29, 2021): 162. http://dx.doi.org/10.3390/e23020162.
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With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate.
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Zhang, Nannan, Lifeng Wu, Zhonghua Wang, and Yong Guan. "Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions." Entropy 20, no. 12 (December 8, 2018): 944. http://dx.doi.org/10.3390/e20120944.
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Bearing plays an important role in mechanical equipment, and its remaining useful life (RUL) prediction is an important research topic of mechanical equipment. To accurately predict the RUL of bearing, this paper proposes a data-driven RUL prediction method. First, the statistical method is used to extract the features of the signal, and the root mean square (RMS) is regarded as the main performance degradation index. Second, the correlation coefficient is used to select the statistical characteristics that have high correlation with the RMS. Then, In order to avoid the fluctuation of the statistical feature, the improved Weibull distributions (WD) algorithm is used to fit the fluctuation feature of bearing at different recession stages, which is used as input of Naive Bayes (NB) training stage. During the testing stage, the true fluctuation feature of the bearings are used as the input of NB. After the NB testing, five classes are obtained: health states and four states for bearing degradation. Finally, the exponential smoothing algorithm is used to smooth the five classes, and to predict the RUL of bearing. The experimental results show that the proposed method is effective for RUL prediction of bearing.
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Zhang, Xiao, Tengyi Peng, Shilong Sun, and Yu Zhou. "New Multifeature Information Health Index (MIHI) Based on a Quasi-Orthogonal Sparse Algorithm for Bearing Degradation Monitoring." Computational Intelligence and Neuroscience 2021 (August 3, 2021): 1–14. http://dx.doi.org/10.1155/2021/2221702.
Full textAbstract:
Data-driven intelligent prognostic health management (PHM) systems have been widely investigated in the area of defective bearing signals. These systems can provide precise information on condition monitoring and diagnosis. However, existing PHM systems cannot identify the accurate degradation trend and the current fault types simultaneously. Given that different fault types have various effects on the mechanical system, the corresponding maintenance strategies also vary. Then, choosing the appropriate maintenance strategy according to the future fault type can reduce the maintenance cost of the equipment operation. Therefore, a multifeature information health index (MIHI) must be developed to trace various bearing degradation trends with various types of faults simultaneously. This paper reports a new quasi-orthogonal sparse project algorithm that can mutually convert the degraded processing feature vector sets (such as spectrum) for each type of fault to orthogonal approximate spatial straight lines. The algorithm builds a MIHI through the spectrum of current state measured points. The MIHI is then transformed by a quasi-orthogonal sparse project algorithm to trace the various bearing degradation trends and recognize the fault type simultaneously. The case study of bearing degradation data demonstrates that this approach is effective in assessing the various degradation trends of different fault types.
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Ding, Xiaoxi, Liming Wang, Wenbin Huang, Qingbo He, and Yimin Shao. "Feature Clustering Analysis Using Reference Model towards Rolling Bearing Performance Degradation Assessment." Shock and Vibration 2020 (March 28, 2020): 1–14. http://dx.doi.org/10.1155/2020/6306087.
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The health monitoring and management have been accepted in modern industrial machinery for an intelligent industrial production. To timely and reliably assess the bearing performance degradation, a novel health monitoring method called feature clustering analysis (FCA) has been proposed in this study. Along with the working time going, this new monitored chart picked by FCA aims to describe the feature clustering distribution transition by a series of reference models. First, the data provided by the reference state (healthy data) and the one from the monitor state (monitor data) are fused together to construct a reference model, which is to explore the active role of healthy status and activate the difference between healthy status and unhealthy status. Manifold learning is later implemented to mine the discriminated features for good class-separable clustering measure. In this manner, heterogeneous information hidden in this reference model will appear once degradation happened. Finally, a clustering quantification factor, named as feature clustering indicator (FCI), is calculated to assess distribution evolution and migration of the monitor status as compared to the consistent healthy status. Furthermore, a single Gaussian model (SGM) based on these FCIs is used to provide a smooth estimate of the healthy condition level. The corresponding negative log likelihood probability (NLLP) and the fault occurrence alarm are developed for an accurate and reliable FCC. And it can well depict a comprehensibility of the real bearing performance degradation process for its whole life. Meanwhile, as compared to other health profiles based on the classical health indicators, the proposed FCC has provided a much more accurate degradation level and rather monotonic profile. The experimental results show the potential in machine health performance degradation assessment.
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Xu, Jing, Chen Lu, and Hong Mei Liu. "Real-Time Life Prediction for Rolling Bearings Based on Nonparametric Bayesian Updating Method." Applied Mechanics and Materials 764-765 (May 2015): 431–36. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.431.
Full textAbstract:
Real-time life prediction for rolling bearings contributes to maintenance decision-making and optimization based on the health state. Real-time life prediction based on Bayesian methods usually require that the priori distribution of the product be obtained; however, this task is extremely difficult to implement for new products or small sample sizes. To solve this problem, a nonparametric Bayesian updating method is proposed in this study. Kernel density estimation is employed to estimate the priori and posterior distribution of parameters by integrating real-time performance degradation information. Thus, bearing real-time life prediction based on nonparametric Bayesian updating is realized. In addition, this study investigates the calculation and normalization process of the working condition conversion factor. The effectiveness of the proposed method is verified by bearing run-to-failure experiments.
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Ma, Xin, Yu Hu, Menghui Wang, Fengying Li, and Youqing Wang. "Degradation State Partition and Compound Fault Diagnosis of Rolling Bearing Based on Personalized Multilabel Learning." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–11. http://dx.doi.org/10.1109/tim.2021.3091504.
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Yin, Rongrong, Jie Hu, Yu Liu, Qing Wu, Chenchen Zhang, and Yuxin Wang. "The degradation of macro-mechanical properties of shield tunnel segments." Modern Physics Letters B 32, no. 34n36 (December 30, 2018): 1840116. http://dx.doi.org/10.1142/s0217984918401164.
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In this paper, the degradation of mechanical properties for eccentrically compressed members was investigated by testing the bearing capacities under the combined effect of load, water permeation and salt environment of chloride and sulfate. The results showed that: (1) the bearing capacity of eccentric compression members under “Seepage stress field” increased with the increase of time; (2) when suffering the combined action of chloride and sulfate, the bearing capacity of eccentric compression members under “Seepage stress field” increased in the early stage of corrosion. However, the bearing capacity decreased afterwards. The higher the ions concentration, the greater the descent; (3) the greater the eccentricity, the greater the vertical displacement of eccentric compression members and the smaller the ultimate bearing capacity.
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Zhang, Ying, Hongfu Zuo, and Fang Bai. "Feature extraction for rolling bearing fault diagnosis by electrostatic monitoring sensors." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, no. 10 (August 31, 2014): 1887–903. http://dx.doi.org/10.1177/0954406214550014.
Full textAbstract:
There are mainly two problems with the current feature extraction methods used in the electrostatic monitoring of rolling bearings, which affect their abilities to identify early faults: (1) since noises are mixed in the electrostatic signals, it is difficult to extract weak early fault features; (2) traditional time and frequency domain features have limited ability to provide a quantitative indicator of degradation state. With regard to these two problems, a new feature extraction method for rolling bearing fault diagnosis by electrostatic monitoring sensors is proposed in this paper. First, the spectrum interpolation is adopted to suppress the power-frequency interference in the electrostatic signal. Then the resultant signal is used to construct Hankel matrix, the number of useful components is automatically selected based on the difference spectrum of singular values, after that the signal is reconstructed to remove background noises and random pulses. Finally, the permutation entropy of the denoised signal is calculated and smoothed using the exponential weighted moving average method, which is used to be a quantitative indicator of bearing performance state. The simulation and experimental results show that the proposed method can effectively remove noises and significantly bring forward the time when early faults are detected.
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Zhang, Ying, and Anchen Wang. "Remaining Useful Life Prediction of Rolling Bearings Using Electrostatic Monitoring Based on Two-Stage Information Fusion Stochastic Filtering." Mathematical Problems in Engineering 2020 (March 17, 2020): 1–12. http://dx.doi.org/10.1155/2020/2153235.
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The accurate prediction of the remaining useful life (RUL) of rolling bearings is of great significance for a rational formulation of maintenance strategies and the reduction of maintenance costs. According to the two-stage nonlinear degradation characteristics of rolling bearing operation, this paper proposes a prognosis model based on modified stochastic filtering. First, multiple features reextracted from the time domain, frequency domain, and complexity angles, and the baseline Gaussian mixture model (GMM) is established using the normal operating data after spectral regression. The Bayesian-inferred distance (BID) is used as a quantitative indicator to reflect the bearing performance degradation degree. Then, taking multiparameter fusion results as input, the relationship between BID and remaining life is established by the two-stage stochastic filtering model to realize online dynamic remaining useful life prediction. The method in this paper overcomes the difficulty of accurately defining the failure threshold of rolling bearing. At the same time, it reduces the computational burden, avoiding the need of calculating the joint probability distribution for high-dimensional data. Finally, the proposed method has been verified experimentally to have high precision and engineering application value.
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SZYCA, MIKOŁAJ. "ANALYSIS OF THE BMA K2400 VERTICAL CENTRIFUGE TURBINE IN TERMS OF BALANCING AND VIBRATION DIAGNOSTICS." HERALD OF KHMELNYTSKYI NATIONAL UNIVERSITY 297, no. 3 (July 2, 2021): 71–80. http://dx.doi.org/10.31891/2307-5732-2021-297-3-71-80.
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Physical damage to a material is a diffuse defect in the form of vacancies, microcracks, micro-voids or damaged micro-volumes, which reduce the effective or load-bearing part of the material. Surface fatigue defects, such as deformation and cracks, occur in the bearing during the load transfer. Imbalance is a practical problem in the operation of many rotating machines, causing not only increased vibration of the machine, but also leading to accelerated wear of the rotor bearings. The subject of this work is the analysis of the dynamics of the BMA K2400 centrifuge in terms of the possibility of correcting the balance in the given dynamic state. The paper describes the individual stages of solving the problem of excessive machine vibrations, assuming that its bearings were replaced before the diagnostic test. As a result of the lack of effects after replacing the motor bearings and after analyzing the vibration measurement results presented in article, a decision was made to inspect the centrifuge bearings. The diagnostics was performed again, but it concerned only the bearing node No. 1 with the disassembled basket. The measurements were performed using the DIAMOND 401 AX device, equipped with Wilcoxon 780B acceleration sensors with a sensitivity of 100mV/g. The appearance of a technological defect on the outer ring of the bearing, which is a friction pair with a housing, is not a typical damage for this type of machines and was an interesting problem. The consequence of the occurrence of bearing defects may be an increase in statistical values of the vibration signal and the appearance of new amplitudes in the FFT spectra. A vicious circle is created here, where bearings in poor dynamic condition increase the transmission of vibrations through the machine, and high vibrations accelerate the degradation of the bearings. The poor condition of rolling bearings may also prevent dynamic balancing of the rotor, and thus – lead to further propagation of bearing damage caused by an increased level of the machine’s own vibrations.
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Ge, Chenglong, Yuanchang Zhu, and Yanqiang Di. "Hybrid Degradation Equipment Remaining Useful Life Prediction Oriented Parallel Simulation considering Model Soft Switch." Computational Intelligence and Neuroscience 2019 (March 12, 2019): 1–18. http://dx.doi.org/10.1155/2019/9179870.
Full textAbstract:
Equipment parallel simulation is an emerging simulation technology in recent years, and equipment remaining useful life (RUL) prediction oriented parallel simulation is an important branch of parallel simulation. An important concept in equipment parallel simulation is the model evolution driven by real-time data, including model selection and model parameter evolution. The current research on equipment RUL prediction oriented parallel simulation mainly focuses on a single continuous degradation mode, such as linear degradation and nonlinear degradation. Under this degradation condition, the model parameter evolution methods in parallel simulation can effectively predict equipment RUL. However, in practice, most of the equipment degradation processes exhibit a mixture of continuous degradation and discrete shock. So this requires adaptive selection of simulation models based on real-time degradation data. In this paper, the hybrid degradation equipment RUL prediction oriented parallel simulation considering model soft switch is studied. Firstly, under the modeling framework of the state space model (SSM), two kinds of degradation simulation models are established using the Wiener process and Poisson effect. Driven by the real-time degradation data, the model probability is calculated by using the forward interactive multiple model filtering algorithm to realize the model soft switch and data assimilation. On the basis of model soft switch, the expectation maximization algorithm is utilized to achieve model parameter evolution. Through the iteration between model soft switch and model parameter evolution, the simulation fidelity can be effectively improved and the actual equipment degradation state is continuously approached. According to the full probability theorem and the concept of first hitting time, the simulated degradation state distribution is integrated into the inverse Gaussian distribution. Then the analytical expression of the RUL probability density function is obtained to achieve RUL real-time prediction. Finally, a case study was conducted by using a bearing degradation data. The results show that the parallel simulation can effectively model the hybrid degradation process of the bearing. Compared with the single-model method that only considers the model parameter evolution, the RUL obtained by the method proposed in this paper has higher prediction accuracy and smaller uncertainty.
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Kim, Taewan, and Seungchul Lee. "Deep Learning-based Health Indicator for Better Bearing RUL Prediction." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 6 (August 1, 2021): 493–98. http://dx.doi.org/10.3397/in-2021-1492.
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The prognostic performance of data-driven approaches closely depends on the features extracted from the measurement. For a high level of prognostic performance, features must be carefully designed to represent the machine's health state well and are generally obtained by signal processing techniques. These features are themselves used as health indicators (HI) or used to construct HIs. However, many conventional HIs are heavily relying on the type of machine components and expert domain knowledge. To solve these drawbacks, we propose a fully data-driven method, that is, the adversarial autoencoder-based health indicator (AAE-HI) for remaining useful life (RUL) prediction. Accelerated degradation tests of bearings collected from PRONOSTIA were used to validate the proposed AAE-HI method. It is shown that our proposed AAE-HI can autonomously find monotonicity and trendability of features, which will capture the degradation progression from the measurement. Therefore, the performance of AAE-HI in RUL prediction is promising compared with other conventional HIs.
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Huang, Gangjin, Hongkun Li, Jiayu Ou, Yuanliang Zhang, and Mingliang Zhang. "A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM." Sensors 20, no. 7 (March 27, 2020): 1864. http://dx.doi.org/10.3390/s20071864.
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Prognostics and health management technology (PHM), a measure to ensure the reliability and safety of the operation of industrial machinery, has attracted attention and application adequately. However, how to use the monitored information to evaluate the degradation of rolling bearings is a significant issue for its predictive maintenance and autonomic logistics. This work presents a reliable health prognosis approach to estimate the health indicator (HI) and remaining useful life (RUL) of rolling bearings. Firstly, to accurately capture the degradation process, a novel health index (HI) is constructed based on correlation kurtosis for different iteration periods and a Gaussian process latency variable model (GPLVM). Then, a multiple convolutional long short-term memory (MCLSTM) network is proposed to predict HI values and RUL values. Finally, we perform experimental datasets of rolling bearings, demonstrating that the presented method surpasses other state-of-the-art prognosis approaches. The results also confirm the feasibility of the presented method in industrial machinery.
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Wang, Peng, Li Zhang, Fu Min Wang, Zan Peng Zhang, and Yun He. "The Time-Dependent Effect of Corrosion of Steel Strands on Prestressed Concrete Beam Bridges." Applied Mechanics and Materials 638-640 (September 2014): 1038–44. http://dx.doi.org/10.4028/www.scientific.net/amm.638-640.1038.
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Based on the concrete carbonization theory, we analyze the time when prestressed steel strands begin to corrode, and put forward the degradation time-dependent model for cross-sectional area of a corroding steel strand; with the comparison of existing research results, we propose the degradation time-dependent model for mechanical properties of corroded steel strands, including the nominal ultimate strength and nominal elastic modulus. We deduce a time-dependent model for the prestressing loss due to steel strand corrosion. Taking a prestressed concrete hollow slab bridge as an example, we analyze the time-dependent effect of the corroded strands on the concrete stress at serviceability limit state and flexural bearing capacity at ultimate limit state. Research shows that the bottom edge stress and flexural bearing capacity at mid-span of the beams will reduce sharply if the prestressed strands corrode, even corrosion can damage the safety of bridge in just a few years. These results can be for information of further experiments and researches.
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Xu, Qianqian, and Kai Liu. "A New Feature Extraction Method for Bearing Faults in Impulsive Noise Using Fractional Lower-Order Statistics." Shock and Vibration 2019 (June 2, 2019): 1–13. http://dx.doi.org/10.1155/2019/2708535.
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According to the performance degradation problem of feature extraction from higher-order statistics in the context of alpha-stable noise, a new feature extraction method is proposed. Firstly, the nonstationary vibration signal of rolling bearings is decomposed into several product functions by LMD to realize signal stability. Then, the distribution properties of product functions in the time domain are discussed by the comparison of heavy tails and characteristic exponent estimation. Fractional lower-order p-function optimization is obtained by the calculation of the distance ratio based on K-means algorithms. Finally, a fault feature dataset is established by the optimal FLOS and lower-dimensional mapping matrix of covariation to accurately and intuitively describe various bearing faults. Since the alpha-stable noise is effectively suppressed and state described precisely, the presented method has shown better performance than the traditional methods in bearing experiments via fractional lower-order feature extraction.
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Zhao, Zhiao, Yong Zhang, Guanjun Liu, and Jing Qiu. "Sample selection of prognostics validation test based on multi-stage Wiener process." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 4 (November 2, 2018): 605–14. http://dx.doi.org/10.1177/1748006x18805835.
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Sample allocation and selection technology is of great significance in the test plan design of prognostics validation. Considering the existing researches, the importance of prognostics samples of different moments is not considered in the degradation process of a single failure. Normally, prognostics samples are generated under the same time interval mechanism. However, a prognostics system may have low prognostics accuracy because of the small quantity of failure degradation and measurement randomness in the early stage of a failure degradation process. Historical degradation data onto equipment failure modes are collected, and the degradation process model based on the multi-stage Wiener process is established. Based on the multi-stage Wiener process model, we choose four parameters to describe different degradation stages in a degradation process. According to four parameters, the sample selection weight of each degradation stage is calculated and the weight of each degradation stage is used to select prognostics samples. Taking a bearing wear fault of a helicopter transmission device as an example, its degradation process is established and sample selection weights are calculated. According to the sample selection weight of each degradation process, we accomplish the prognostics sample selection of the bearing wear fault. The results show that the prognostics sample selection method proposed in this article has good applicability.
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Kuznetzov, О., О. Rubanenko, О. Khrenov, and E. Rafalskiy. "RESERVE CAPACITY OF LONGITUDINAL BEAM OF WAGON TRUCK UNDER THE ACTION OF UNIFORMLY DISTRIBUTED LOADING." Municipal economy of cities 1, no. 154 (April 3, 2020): 50–56. http://dx.doi.org/10.33042/2522-1809-2020-1-154-50-56.
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Priority directions and measures among the main branches of urban electric transport are resource saving in the subway transportation system during its operation. First of all, this problem must be solved by scientific support, that is, at the stage of designing parts and components of vehicles. One of the main tasks that are solved at the design stage is to increase the load-bearing capacity of the parts by analyzing their stress-strain state. The article is devoted to the calculation of the load capacity reserve of the longitudinal beam of the front subway trolley under the action of evenly distributed over the entire length of the load without taking into account the transverse forces. The priority of the research topic is substantiated, the purpose and tasks are formulated. Two approaches to the power calculation of the bearing capacity of the longitudinal beam are introduced: the calculation of the permissible stresses and the limit state. In both cases elastic models of beams are considered. In the case of calculation on the limit state, the mechanics of the occurrence of plastic hinges at the places of rigid fixing of the ends of the beam are first substantiated. The beam still retains its load capacity. With the further growth of the external load, the emergence of a plastic hinge is justified even in the middle of the beam with the simultaneous loss of the beam of the bearing capacity. To simulate the behavior of the beam according to its characteristics, including the stress and the degradation condition of its load capacity, the mathematical formulation of the problem of calculating the load capacity of the longitudinal beam when calculating the permissible stresses and the limit state without taking into account the transverse force. The load-bearing capacity of the longitudinal beam in the calculation of permissible stresses and the limit state is analyzed. The analysis of the obtained results allows us to judge the effectiveness of the proposed mathematical model as a whole. The obtained equations for the maximum allowable load when calculating the limit state and the allowable stresses allow us to reliably estimate the bearing capacity of the longitudinal beam in both cases. The increase in the bearing capacity of the beam in the case of calculation on the limit is three times. The conclusions about the adequacy of the analysis of the bearing capacity of the longitudinal beam bearing capacity were made. Keywords: resource saving, beam, bearing capacity, allowable stresses, limit state.
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Zhang, Qi, Tian Tian, Guangrui Wen, and Zhifen Zhang. "A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis." Shock and Vibration 2018 (December 2, 2018): 1–13. http://dx.doi.org/10.1155/2018/2913163.
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The application of the existing complex network in fault diagnosis is usually modelled based on the time domain, resulting in the loss of sign frequency-domain features, and the extracted topology features of network are too macroscopic and insensitive to local changes within the network. This paper proposes a new method of local feature extraction based on frequency complex network (FCN) decomposition and builds a new complex network structure feature on this basis, namely, subnetwork average degree. The variation law of signals in frequency domain is obtained with the aid of the structural features of complex network. The local features that are sensitive to local changes of the network are applied to characterize the whole network, with flexible application and without limitation in mechanism. The average degree of subnetwork could be regarded as feature parameters for rolling bearing fault diagnosis and degradation state recognition. Analysis on the experimental data and bearing life cycle data shows that the method proposed in this paper is effective, revealing that the extracted features have effective separability and high accuracy in fault recognition and the degradation detection of the life cycle of rolling bearings combined with neural networks. Moreover, the proposed method has reference value for the processing and recognition of other nonstationary signals.
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Roy, Biswajit, and Sudip Dey. "Machine learning-based performance analysis of two-axial-groove hydrodynamic journal bearings." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 235, no. 10 (February 9, 2021): 2211–24. http://dx.doi.org/10.1177/1350650121992895.
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The precise prediction of a rotor against instability is needed for avoiding the degradation or failure of the system’s performance due to the parametric variabilities of a bearing system. In general, the design of the journal bearing is framed based on the deterministic theoretical analysis. To map the precise prediction of hydrodynamic performance, it is needed to include the uncertain effect of input parameters on the output behavior of the journal bearing. This paper presents the uncertain hydrodynamic analysis of a two-axial-groove journal bearing including randomness in bearing oil viscosity and supply pressure. To simulate the uncertainty in the input parameters, the Monte Carlo simulation is carried out. A support vector machine is employed as a metamodel to increase the computational efficiency. Both individual and compound effects of uncertainties in the input parameters are studied to quantify their effect on the steady-state and dynamic characteristics of the bearing.