A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM

Huang, Gangjin; Li, Hongkun; Ou, Jin Ping; Zhang, Yuanliang; Zhang, Mingliang

doi:10.3390/s20071864

Cited by 16 publications

(8 citation statements)

References 37 publications

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“…The test bench shown in Figure 4, is essentially composed of an electric motor (1), that turns a spindle (2) on which one of the bearing rings is mounted. A shaft (3) transmits the axial load to the thrust bearing from a hydraulic pump (4), and a continuously operating lubrication circuit (5) for cooling.…”

Section: Test Benchmentioning

confidence: 99%

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Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis

Hotait

Chiementin

Rasolofondraibe

2021

Entropy

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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.

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Section: Test Benchmentioning

confidence: 99%

“…As the equipment runs, their performance will deteriorate and lead to failure. Consequently, early fault diagnosis and prediction of bearing life and safety are of high importance for predictive maintenance and the industrial reliability of mechanical equipment [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis

Hotait

Chiementin

Rasolofondraibe

2021

Entropy

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“…Deep learning (DL)-based data-driven techniques, as a new branch of artificial intelligence, provide a promising tool to automatically learn features from raw data by constructing hierarchical architectures. The typical DL algorithms, such as the stacked auto encoder (SAE), deep belief network (DBN), convolutional neural network (CNN), long short-term memory (LSTM) and their variants have been developed for mechanical fault diagnosis and detection [10][11][12][13][14][15]. Jia et al [16] proposed a local connection network constructed by a normalized sparse autoencoder for recognizing the mechanical health conditions.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous fault type and severity identification using a two-branch domain adaptation network

Chen

Huang

Liao

et al. 2021

Meas. Sci. Technol.

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Simultaneous fault type and severity identification is critical for timely maintenance actions to prevent accidents from industrial machinery. The former can indicate occurrences of specific faults, and the latter can track early fault evolutions. Existing methods generally assume training and testing data are drawn from the same data distribution. However, in real industries, due to the change of working conditions, domain shift phenomenon can be triggered. The existing intelligent diagnosis methods are less effective in such scenarios for lack of domain adaptation ability. To address such problems, a novel two-branch domain adaptation network is developed. A deep convolutional neural network with two branches, as the main hierarchical architecture, is designed to handle two recognition tasks. The maximum mean discrepancy based multi-kernel learning is embedded to reduce the distribution discrepancy between the source domain and target domain. As such, the domain-invariant features with a hierarchical structure can be effectively extracted and the fault types and fault severities can be recognized at the same time. Experiments on a bearing dataset, a gear dataset and a motor bearing dataset are carried out to validate the effectiveness of the proposed approach. The results demonstrate that the proposed method can effectively perform fault type and severity identification simultaneously and obviously outperforms other state-of-the-art methods.

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“…With the improvement of the computer processing speed, artificial intelligence technology is undergoing rapid development. With the aim of utilizing the data explosion of time series prediction [22], the artificial intelligence method can build a model adaptively for an unknown mechanical equipment state and improve the prediction accuracy of the model through repeated training. Among such methods, recurrent neural networks (RNNs) [23,24] and long-and short-term memory (LSTM) [25,26] are often used to predict the deterioration of mechanical equipment performance.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention‐LSTM

Wang

Yang

et al. 2021

Shock and Vibration

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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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A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM

Cited by 16 publications

References 37 publications

Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis

Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis

Simultaneous fault type and severity identification using a two-branch domain adaptation network

Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention‐LSTM

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