Experimental and computational evaluation of rolling bearing steel durability

Nohál, Libor; Vaculka, Miroslav

doi:10.1088/1757-899x/179/1/012054

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Studies have shown that more than 45% of equipment failures in rotating machinery are caused by bearing failure [ 1 ]. The financial losses and major safety accidents that this causes in the industry can be avoided by assessing the deterioration status of equipment, which would bolster an organization’s ability to detect faults in machine bearings [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Performance Degradation Prediction Using LSTM with Optimized Parameters

Wei²,

Yang³

et al. 2022

Sensors

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Predicting the degradation of mechanical components, such as rolling bearings is critical to the proper monitoring of the condition of mechanical equipment. A new method, based on a long short-term memory network (LSTM) algorithm, has been developed to improve the accuracy of degradation prediction. The model parameters are optimized via improved particle swarm optimization (IPSO). Regarding how this applies to the rolling bearings, firstly, multi-dimension feature parameters are extracted from the bearing’s vibration signals and fused into responsive features by using the kernel joint approximate diagonalization of eigen-matrices (KJADE) method. Then, the between-class and within-class scatter (SS) are calculated to develop performance degradation indicators. Since network model parameters influence the predictive accuracy of the LSTM model, an IPSO algorithm is used to obtain the optimal prediction model via the LSTM model parameters’ optimization. Finally, the LSTM model, with said optimal parameters, was used to predict the degradation trend of the bearing’s performance. The experiment’s results show that the proposed method can effectively identify the trends of degradation and performance. Moreover, the predictive accuracy of this proposed method is greater than that of the extreme learning machine (ELM) and support vector regression (SVR), which are the algorithms conventionally used in degradation modeling.

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Section: Introductionmentioning

confidence: 99%

Performance Degradation Prediction Using LSTM with Optimized Parameters

Wei²,

Yang³

et al. 2022

Sensors

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show abstract

“…Roller bearings are key components of rotating machinery and they are widely used in aerospace, railway and other industries [1]. Economic losses and major safety accidents can be avoided in industry through an accurate evaluation of the bearing degradation status of the equipment and a timely detection of bearing failures [2]. Two issues are key in the performance degradation evaluation of rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Liu

et al. 2020

Sensors

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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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Experimental and computational evaluation of rolling bearing steel durability

Cited by 2 publications

References 4 publications

Performance Degradation Prediction Using LSTM with Optimized Parameters

Performance Degradation Prediction Using LSTM with Optimized Parameters

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

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