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

Luo, Honglin; Lin, Bo; Peng, Chenglin; Hou, Dongming

doi:10.3390/s20174930

Cited by 20 publications

(5 citation statements)

References 32 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research experience has proved that fault diagnosis can extend the operating life of the equipment and reduce the cost of regular maintenance before the secondary damage or major damage to the equipment [25,26]. The different types of faults could reflect the different aspects of the faulty components [27][28][29]. The review mainly introduces various related tests and algorithms from the aspects of vibration, acoustic signal fault diagnosis, and temperature prediction diagnosis, which are widely utilized in rolling bearing research and the application of railway vehicles.…”

mentioning

confidence: 99%

A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles

Yan

Jiang²,

Bai

et al. 2022

Processes

View full text Add to dashboard Cite

This paper reviews the current research status of rolling bearing fault diagnosis technology for railway vehicles. Several domains are covered, including vibration fault diagnosis, acoustic signal fault diagnosis, and temperature prediction diagnosis methods on train rolling bearing test principles and related research. The application scenarios, system diagnosis accuracies, and model structures of various studies in the literature are also compared and analyzed. Furthermore, the main technical points to be improved and the analysis of the possible research directions are proposed, which provide new research ideas for subsequent fault diagnosis methods and system innovation research and development.

show abstract

mentioning

confidence: 99%

A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles

Yan

Jiang²,

Bai

et al. 2022

Processes

View full text Add to dashboard Cite

show abstract

“…In addition, based on the specific application background, the structure of IA-optimal CNN can be improved to make it more targeted to solve different types of signals classification problems. For example, when using vibration signals for fault diagnosis, based on full research for the [41,59]. Taking into account the noise interference and data loss that may occur in the process of sensor signals transmission, the model for loss data recovery can also be considered when designing the algorithm.…”

Section: Future Workmentioning

confidence: 99%

Signals classification based on IA-optimal CNN

Zhang

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

The versatility of the existing A-optimal-based CNN for solving multiple types of signals classification problems has not been verified by different signals datasets. Moreover, the existing A-optimal-based CNN uses a simplified approximate function as the optimization objective function instead of precise analytical function, which affects the signals classification accuracy to a certain extent. In this paper, a classification method called IA-optimal CNN is proposed. To improve the stability of the classifier, the trace of the covariance matrix of the weights of the fully connected layer is used as the optimization objective function, and the parameter optimization model is established without any simplification of the optimization objective function. In addition, to avoid the difficulty of not being able to obtain the analytical expression formula of the partial derivative of the inverse matrix with regard to the networks parameters, a novel dual function is introduced to transform the optimization problem into an equivalent binary function optimization problem. Furthermore, based on the above analytical solution results, the parameters are updated using the alternate iterative optimization method and the accurate weight update formula is deduced in detail. Five signals datasets are used to test the universality of the IAoptimal CNN in signals classification fields. The performance of IA-optimal CNN is showed, and the experimental results are compared with the existing A-optimal-based classification algorithm. Lastly, the following conclusion is proved theoretically: For the A-optimal-based CNN, the trace of the covariance matrix will continue to decrease and approach a convergence value in the iterative process, but it is impossible for the networks to strictly reach the A-optimal state.

show abstract

“…Zhang et al [31] proposed an improved CNN model with multiscale feature extraction to diagnose bearing defects using limited training samples. Luo et al [32] proposed an improved CNN framework with shallow pooling layer information fusion to detect the faults of high-speed train axle-box bearing systems. Fu et al [33] proposed a residual-learning-based CNN with multiscale comprehensive feature fusion to recognize vehicle color.…”

Section: Introductionmentioning

confidence: 99%

An Improved Convolutional-Neural-Network-Based Fault Diagnosis Method for the Rotor–Journal Bearings System

Luo

Lin

Peng³

et al. 2022

Machines

Self Cite

View full text Add to dashboard Cite

More layers in a convolution neural network (CNN) means more computational burden and longer training time, resulting in poor performance of pattern recognition. In this work, a simplified global information fusion convolution neural network (SGIF-CNN) is proposed to improve computational efficiency and diagnostic accuracy. In the improved CNN architecture, the feature maps of all the convolutional and pooling layers are globally convoluted into a corresponding one-dimensional feature sequence, and then all the feature sequences are concatenated into the fully connected layer. On this basis, this paper further proposes a novel fault diagnosis method for a rotor–journal bearing system based on SGIF-CNN. Firstly, the time-frequency distributions of samples are obtained using the Adaptive Optimal-Kernel Time–Frequency Representation algorithm (AOK-TFR). Secondly, the time–frequency diagrams of the training samples are utilized to train the SGIF-CNN model using a shallow information fusion method, and the trained SGIF-CNN model can be tested using the time–frequency diagrams of the testing samples. Finally, the trained SGIF-CNN model is transplanted to the equipment’s online monitoring system to monitor the equipment’s operating conditions in real time. The proposed method is verified using the data from a rotor test rig and an ultra-scale air separator, and the analysis results show that the proposed SGIF-CNN improves the computing efficiency compared to the traditional CNN while ensuring the accuracy of the fault diagnosis.

show abstract

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

Cited by 20 publications

References 32 publications

A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles

A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles

Signals classification based on IA-optimal CNN

An Improved Convolutional-Neural-Network-Based Fault Diagnosis Method for the Rotor–Journal Bearings System

Contact Info

Product

Resources

About