High-speed train wheel set bearing fault diagnosis and prognostics: A new prognostic model based on extendable useful life

Xu, Guorui; Hou, Dongming; Qi, Hongyuan; Lin, Bo

doi:10.1016/j.ymssp.2020.107050

Cited by 78 publications

(29 citation statements)

References 23 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. This test bench is connected to a control and data acquisition system.…”

Section: Test Benchmentioning

confidence: 99%

“…The DBSCAN algorithm uses the signal density to separate the dense Entropy 2021, 23, 791 2 of 16 region from the noise region. Several analyses of bearing fault diagnostics and condition monitoring have been done as well as studying the prognostics of the defect in rolling bearings [5]. These predictions can reduce machine breakdowns, thereby minimizing the costs and support maintenance scheduling thus prolonging the life of the equipment.…”

Section: Introductionmentioning

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%

Section: Introductionmentioning

confidence: 99%

Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis

Hotait

Chiementin

Rasolofondraibe

2021

Entropy

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“…A concept that has changed in the Prognostics and Health Management (PHM) implementation from seeking the remaining useful life to determining extendable useful life [19]. Research in RCM area has been rapidly growing these years because of increasing challenges and expectations of maintenance [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Critical Success Factors of the Reliability-Centred Maintenance Implementation in the Oil and Gas Industry

et al. 2020

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Reliability-Centred Maintenance (RCM) is a strategic process to improve the maintenance planning of companies which contributes to sustainable production. This method has been applied by numerous industries to achieve an efficient maintenance process, but many have not fully completed their goals. The reason for this failure is that RCM implementation is complex, and organisations need to have adequate preparations before they implement it. In the pre-implementation phase, it is necessary to know the number of Critical Success Factors (CSFs) as a critical measure for implementing the RCM method successfully. Therefore, it is important for practitioners to apply a symmetric mechanism involving fuzzy systems to achieve the desired RCM implementation. There are a limited number of studies that have observed these factors regarding the characteristics of oil and gas companies, especially in the pre-implementation phase. Addressing RCM pre-implementation issues is of high importance from the economic perspective of sustainability for oil and gas organisations. The objective of this study is to investigate significant items in RCM pre-implementation through a combination of quantitative and qualitative analyses. The Nominal Group Technique (NGT) method is applied by gaining the opinion of experts to determine the factors and prioritising them using mathematical modelling. A group of related experts from the oil and gas industry were initially interviewed and surveyed to determine the critical success factors. These identified factors were then analysed using quantitative analysis to identify the important degrees and scored using Fuzzy Analytic Network Process (FANP). Fifteen major factors affecting the criticality of successful RCM implementation have been identified and prioritised, based on their weights. The model proposed in this study could be used as a guideline for assessing CSFs in other countries. To apply the proposed model in different contexts, it needs to be modified according to the needs, policies, and perspectives of each country.

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“…In the first step, signals are measured by different types of sensors attached to the machine. The signals can be vibration signals [3], current signals [4], acoustic emission signals [5], and so on. Since noise is inevitable in signal acquisition, the measured signals are always contaminated by noise components.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis

Hoang

Tran

Van

2021

Sensors

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This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data.

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High-speed train wheel set bearing fault diagnosis and prognostics: A new prognostic model based on extendable useful life

Cited by 78 publications

References 23 publications

Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis

Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis

Critical Success Factors of the Reliability-Centred Maintenance Implementation in the Oil and Gas Industry

A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis

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