Remaining Life Prediction Method for Rolling Bearing Based on the Long Short-Term Memory Network

Wang, Fengtao; Liu, Xiaofei; Deng, Gang; Yu, Xiaoyang; Li, Hongkun; Han, Qingkai

doi:10.1007/s11063-019-10016-w

Cited by 74 publications

(46 citation statements)

References 12 publications

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“…Rodney et al [24] have used the same test rig (PRONOSTIA) as [23] but they used a data-driven methodology based on extended Kalman filtering; the average prediction accuracy was about 80%. In the case of a long short-term memory network [25], a prediction accuracy of 45.27 % is obtained.…”

Section: Resultsmentioning

confidence: 98%

Degradation state prediction of rolling bearings using ARX-Laguerre model and genetic algorithms

Najeh

Lundberg

2020

Int J Adv Manuf Technol

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This study is motivated by the need for a new advanced vibration-based bearing monitoring approach. The ARX-Laguerre model (autoregressive with exogenous) and genetic algorithms (GAs) use collected vibration data to estimate a bearing’s remaining useful life (RUL). The concept is based on the actual running conditions of the bearing combined with a new linear ARX-Laguerre representation. The proposed model exploits the vibration and force measurements to reconstruct the Laguerre filter outputs; the dimensionality reduction of the model is subject to an optimal choice of Laguerre poles which is performed using GAs. The paper explains the test rig, data collection, approach, and results. So far and compared to classic methods, the proposed model is effective in tracking the evolution of the bearing’s health state and accurately estimates the bearing’s RUL. As long as the collected data are relevant to the real health state of the bearing, it is possible to estimate the bearing’s lifetime under different operating conditions.

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Section: Resultsmentioning

confidence: 98%

Degradation state prediction of rolling bearings using ARX-Laguerre model and genetic algorithms

Najeh

Lundberg

2020

Int J Adv Manuf Technol

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“…In this process, the traditional algorithm mainly finds a set of features with high contribution rate. Some authors [26][27][28] defined different feature types based on the contribution of features to degradation information (DI). In this case, feature correlation is a measure of the degradation-stage-related information.…”

Section: Introductionmentioning

confidence: 99%

“…Some experts have proposed using the LSTM neural network to predict bearing RUL based on the bearing degradation bottleneck feature, waveform entropy (WFE) indicator, time factor, or based on the deep feature representation method [26,[31][32][33]. Compared with the previous artificial intelligence algorithms, the predictive ability of the LSTM significantly improved.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

Tian

Wang

2020

Applied Sciences

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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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“…For instance, Zhang et al [25] proposed a method for evaluating bearing performance degradation using LSTM recursive network. Wang et al [26] solved the gradient explosion problem by using LSTM and realized the time series prediction of rolling bearing vibration signals. Hinchi and Tkiouat [27] used LSTM to capture the degradation process and predict the time value to estimate the remaining useful life.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multiscale Deep Health Indicator with Bidirectional LSTM Network for Bearing Performance Degradation Trend Prognosis

Wang

Tang

Zhou

et al. 2020

Shock and Vibration

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As rolling bearings are the key components in rotating machinery, bearing performance degradation directly affects machine running status. A tendency prognosis for bearing performance degradation is thus required to ensure the stability of operation. This paper proposes a novel strategy for bearing performance degradation trend prognosis, including health indicator construction techniques and a performance degradation trend prediction method. To more accurately represent the degradation trend, the multiscale deep bottleneck health indicator is proposed as a new synthesized health indicator to remove high-frequency detail signals from features, which can reduce possible fluctuations in conventional synthetic health indicators. A suitable method for selecting the statistical characteristics required for fusion is also presented to solve the problem of information redundancy that affects trend representation. In addition, a stacked autoencoder network is used for deep feature extraction of selected statistical features. A bidirectional long short-term memory network prediction model is also proposed for the prediction of degradation trend, which can make full use of historical and future information to improve prediction accuracy. Finally, experiments are carried out to verify the effectiveness of the proposed method.

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Remaining Life Prediction Method for Rolling Bearing Based on the Long Short-Term Memory Network

Cited by 74 publications

References 12 publications

Degradation state prediction of rolling bearings using ARX-Laguerre model and genetic algorithms

Degradation state prediction of rolling bearings using ARX-Laguerre model and genetic algorithms

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

A Novel Multiscale Deep Health Indicator with Bidirectional LSTM Network for Bearing Performance Degradation Trend Prognosis

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