Stages prediction of the remaining useful life of rolling bearing based on regularized extreme learning machine

Wu, Chenchen; Sun, Hongchun; Zhang, Zihan

doi:10.1177/09544062211009556

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…(1) In Section 2 of this study, the total durability test time was 1508 h, including the high-speed (8 h), high-temperature (300 h) and heavy-load (1200 h) experiments. The bearing life was calculated according to Equation (1) [ 34 , 35 ]:

where C is the basic rated dynamic load (122 kN), P is the equivalent dynamic load (17.07 kN), and n is the average speed (4108 r/min); these values were derived from the actual working conditions.…”

Section: Comprehensive Analysismentioning

confidence: 99%

Multidimensional Study on the Wear of High-Speed, High-Temperature, Heavy-Load Bearings

et al. 2023

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The friction and wear performance of high-performance bearings directly affects the accuracy and maneuverability of weapons and equipment. In this study, high-speed, high-temperature, and heavy-load durability experiments of weapon bearings were carried out, and their wear properties (i.e., surface wear, metamorphic layer, scanning electron microscopy/energy-dispersive spectroscopy (SEM/EDS), residual stress, and retained austenite) were analyzed in multiple dimensions. The results showed the following: (1) The experimental temperature of the serviced front-end bearing is always lower than that of the rear bearing. (2) The metamorphic layer of the serviced rear bearing (i.e., inner ring, outer ring, rolling body, and cage) > the metamorphic layer of the serviced front-end bearing > the metamorphic layer of the unserviced bearing. (3) The rolling body of the rear bearing at high experimental temperatures contains not only elemental O, but also elemental P and Sr. (4) In the EDS analysis of the rolling elements, with the migration from the “ball edge” to the “ball center”, the elemental C in the rolling elements of serviced or unserviced bearings decreases slowly, while the elemental Fe content increases slowly.

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Section: Comprehensive Analysismentioning

confidence: 99%

Multidimensional Study on the Wear of High-Speed, High-Temperature, Heavy-Load Bearings

et al. 2023

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“…Next, the ON-LSTM is responsible for learning the time correlation of features extracted by the 1DCNN and dividing the operation process of the bearings into three stages: normal stage, initial degradation stage, and rapid degradation stage. The division of normal stage and degradation stage is to distinguish whether the current operating state of the bearing has a degradation trend, as the bearing do not have a significant degradation trend in the early stages of the operating process and can operate normally, conducting RUL prediction at this stage will reduce prediction accuracy [31,32]. Then we divide the degradation stage into the initial degradation stage and the rapid degradation stage, where the rapid degradation stage is mainly used to provide early warning for potential bearing failures [33].…”

Section: Dcnn-on-lstm Ensemble Frameworkmentioning

confidence: 99%

“…Although the operation time of each bearing is often different and leads to errors in RUL prediction, we can reduce the error through the RUL prediction method of the bearing based on the division of operation stages [35]. Since there is no obvious degradation trend in the early stages of bearing operation, prediction after dividing the operation stages can help the model obtain the degradation trend of the bearing and improve the prediction accuracy [31,34].…”

Section: Remarkmentioning

confidence: 99%

Operation stage division and RUL prediction of bearings based on 1DCNN-ON-LSTM

Guo,

Li,

Huang

2023

Meas. Sci. Technol.

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Remaining useful life (RUL) prediction of bearings is significantly important to ensure reliable operation of bearings. In practice, it is routinely impossible to obtain the full life cycle degradation data of bearings that needs to be used in prediction. The accuracy of the RUL prediction of bearings is often affected by incomplete degradation data. Regarding this situation, this paper proposes a multi-sensor three-stage RUL prediction framework based on the one-dimensional convolutional ordered neuron long short-term memory (1DCNN-ON-LSTM) neural network. Firstly, 1DCNN is used to extract spatial features adaptively from multi-sensor’s data and fuse them into one-dimensional feature. Next, the unsupervised hierarchy mechanism of time series information based ON-LSTM is developed to determine the ‘initial degradation stage point’ and ‘rapid degradation stage point’ of the bearing from the one-dimensional feature. Once the signal features collected by sensors input to the model reach the degradation stage point, select the corresponding sensitive features as input and construct the 1DCNN-ON-LSTM model that performs RUL prediction after the degradation stage point to improve the prediction accuracy of the model. Based on the proposed hierarchy mechanism, the bearings’ operation process is divided into three operation stages: normal stage, initial degradation stage and rapid degradation stage. Finally, the experiments verify that the proposed method can effectively divide the operation stages of bearings to predict the RUL and improve the generalization ability and prediction accuracy of the model.

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“…Che et al [30] proposed an intelligent fault prediction model based on gate recurrent units and hybrid autoencoders. Wu et al [31] proposed a staged prediction method based on the regularized learning machine to predict remaining useful life of the bearing with high accuracy and speed. Xu et al [32] proposed a remaining useful life prediction method of rolling bearing combining convolutional autoencoder networks and the status degradation model.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to the Degradation Stage Prediction of Rolling Bearings Using Hierarchical Grey Entropy and a Grey Bootstrap Markov Chain

Cheng,

Ma,

Gao

2023

Sensors

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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 prediction model, based on the GBMC, to obtain degradation stage prediction results more accurately. Meanwhile, three parameter indicators, namely the dynamic estimated interval, the reliability of the prediction result and dynamic uncertainty, are employed to evaluate the prediction results from different perspectives. The estimated interval reflects the upper and lower boundaries of the prediction results, the reliability reflects the credibility of the prediction results and the uncertainty reflects the dynamic fluctuation range of the prediction results. Finally, three rolling bearing run-to-failure experiments were conducted consecutively to validate the effectiveness of the proposed method, whose results indicate that HGE is superior to other entropies and the GBMC surpasses other existing rolling bearing degradation prediction methods; the prediction reliabilities are 90.91%, 90% and 83.87%, respectively.

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Stages prediction of the remaining useful life of rolling bearing based on regularized extreme learning machine

Cited by 7 publications

References 34 publications

Multidimensional Study on the Wear of High-Speed, High-Temperature, Heavy-Load Bearings

Multidimensional Study on the Wear of High-Speed, High-Temperature, Heavy-Load Bearings

Operation stage division and RUL prediction of bearings based on 1DCNN-ON-LSTM

A New Approach to the Degradation Stage Prediction of Rolling Bearings Using Hierarchical Grey Entropy and a Grey Bootstrap Markov Chain

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