Novel Efficient Technologies in Europe for Axle Bearing Condition Monitoring – the MAXBE Project

Aiming at the problems of weak dynamic response and difficulty in diagnosis of early damage of rolling bearings, a diagnosis method for early damage of rolling bearings is proposed. Taking radial rolling bearings as the main research object, the load symmetric structure of deep groove ball bearings is analyzed. Based on the mechanical second-order system theory, the sensor monitoring structure is constructed. The generalized resonance principle is used to identify weak signals, and the fiber Bragg grating is used for signal sensing. The signal is obtained through the fiber Bragg grating high-speed demodulator. When a continuous periodic generalized resonance wave appears in the amplitude–frequency analysis of the signal, and there is a high-frequency resonance frequency, it can be proved that the bearing is faulty. The diagnosis method can effectively avoid the interference of low-frequency signals, the frequency spectrum is pure and there is no electromagnetic interference. It fully shows that the fiber Bragg grating sensor is suitable for the monitoring and diagnosis of the early weak fault of the bearing.

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“…Common fault vibration modes include wear, fatigue, indentation, corrosion, electrical corrosion, cracking, gluing, cage damage, etc. [17].…”

Section: Bearing Fault Form and Fault Characteristic Frequencymentioning

confidence: 99%

Early Weak Fault Diagnosis of Rolling Bearings Based on Fiber Bragg Grating Sensing Monitoring

et al. 2021

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“…Acoustic emission sensing technology was also demonstrated as part of a European Commission funded project, MAXBE (Interoperable Monitoring, Diagnosis and Maintenance Strategies for Axle Bearings). This work has been presented in [64,10].…”

Section: Acoustic Emissionmentioning

confidence: 99%

Perspectives on railway axle bearing condition monitoring

Entezami

Roberts

Weston

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part F:

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Defects in railway axle bearings can affect operational efficiency, or cause in-service failures, damaging the track and train. Healthy bearings produce a certain level of vibration and noise, but a bearing with a defect causes substantial changes in the vibration and noise levels. It is possible to detect the bearing defects at an early stage of their development, allowing an operator to repair the damage before it becomes serious. When a vehicle is scheduled for maintenance, or due for overhaul, knowledge of bearing damage and severity is beneficial, resulting in fewer operational problems and optimised fleet availability. This paper is a review of the state of the art in condition monitoring systems for rolling element bearings, especially the axlebox bearings. This includes exploring the sensing technologies, summarising the main signal processing methods and condition monitoring techniques, i.e. wayside and on-board. Examples of commercially available systems and outputs of current research work are presented. The effectiveness of the current monitoring technologies is assessed and the p– f curve is presented. It is concluded that the research and practical tests on axlebox bearing monitoring are limited compared to the generic bearing applications.

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“…Therefore, how to identify the axle-box bearing fault accurately and quickly has become an urgent challenge to be solved. Currently, vibration analysis, acoustic analysis and temperature analysis are three main approaches for axle-box bearings failure detection [ 2 ]. However, the temperature does not raise much for an early-stage bearing fault, and noises from wheel/rail contacts and the train drive system, as well as aerodynamic forces, may contaminate the signal acquired by the acoustic arrays [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

Luo

Lin

Peng³

et al. 2020

Sensors

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Axle-box bearings are one of the most critical mechanical components of the high-speed train. Vibration signals collected from axle-box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF-CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one-dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF-CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.

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Novel Efficient Technologies in Europe for Axle Bearing Condition Monitoring – the MAXBE Project

Cited by 34 publications

References 3 publications

Early Weak Fault Diagnosis of Rolling Bearings Based on Fiber Bragg Grating Sensing Monitoring

Early Weak Fault Diagnosis of Rolling Bearings Based on Fiber Bragg Grating Sensing Monitoring

Perspectives on railway axle bearing condition monitoring

Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

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