Intelligent Fault Diagnosis of the High-Speed Train With Big Data Based on Deep Neural Networks

Hu, He-xuan; Bo, Tang; Gong, Xuejiao; Wei, Wei; Wang, Huihui

doi:10.1109/tii.2017.2683528

Cited by 190 publications

(91 citation statements)

References 26 publications

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“…It will be at highly risk to install the bogie with faults on real operating HST. In order to simulate the characteristics of faulty bogie in actual operation, the multibody dynamic model of bogie is built by SIMPACK software [28,29].…”

Section: Analysis Of Signals For Bogiementioning

confidence: 99%

Convolutional Recurrent Neural Network for Fault Diagnosis of High‐Speed Train Bogie

et al. 2018

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Timely detection and efficient recognition of fault are challenging for the bogie of high-speed train (HST), owing to the fact that different types of fault signals have similar characteristics in the same frequency range. Notice that convolutional neural networks (CNNs) are powerful in extracting high-level local features and that recurrent neural networks (RNNs) are capable of learning long-term context dependencies in vibration signals. In this paper, by combining CNN and RNN, a so-called convolutional recurrent neural network (CRNN) is proposed to diagnose various faults of the HST bogie, where the capabilities of CNN and RNN are inherited simultaneously. Within the novel architecture, the proposed CRNN first filters out the features from the original data through convolutional layers. Then, four recurrent layers with simple recurrent cell are used to model the context information in the extracted features. By comparing the performance of the presented CRNN with CNN, RNN, and ensemble learning, experimental results show that CRNN achieves not only the best performance with accuracy of 97.8% but also the least time spent in training model.

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Section: Analysis Of Signals For Bogiementioning

confidence: 99%

Convolutional Recurrent Neural Network for Fault Diagnosis of High‐Speed Train Bogie

et al. 2018

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“…Aluminum alloy is one of the most widely used materials in industrial production [1]. Given its low density, high strength, good plasticity, good electrical conductivity, high thermal conductivity, and corrosion resistance, it is widely applied in the automobile manufacturing, aerospace, shipbuilding, and chemical industries [2][3][4]. However, aluminum is an active metal and easily oxidizes in the natural environment, with an oxide film forming on the surface.…”

Section: Introductionmentioning

confidence: 99%

Investigation on the Surface Properties of 5A12 Aluminum Alloy after Nd: YAG Laser Cleaning

et al. 2019

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The surface of the aluminum alloy is prone to oxidation, which in turn affects the quality of the weld. The 5A12 aluminum alloy was cleaned by acousto-optic Q-switched diode-pumped Nd:YAG laser and the effects of different laser powers and different cleaning speeds on the surface roughness, microstructure, element content, microhardness, residual stress and corrosion resistance of aluminum alloy were investigated. The results show that when the power is 98W and the cleaning speed is 4.1 mm/s, the effect of Nd: YAG laser on the removal of oxide film on 5A12 aluminum alloy surface is the most effective. After laser cleaning, the smoothness and strength of aluminum alloy surface can be effectively improved. However, as a major element in 5A12 aluminum alloy, the content of magnesium decreased. At the same time, the residual tensile stress was generated on the surface of the aluminum alloy after cleaning, and the corrosion resistance slightly decreased.

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“…Consequently, new technological solutions that can support the decision-making to expend scarce resources in a smart way are needed. To optimize maintenance costs, many intelligent monitoring systems have recently been used for railway infrastructures, which need big data analyses over the data collected [2], [3]. The range of sensing technologies has expanded rapidly, and sensor devices have simultaneously become cheaper.…”

Section: Introductionmentioning

confidence: 99%

Pareto-Based Maintenance Decisions for Regional Railways With Uncertain Weld Conditions Using the Hilbert Spectrum of Axle Box Acceleration

Núñez

Jamshidi

Wang

2019

IEEE Trans. Ind. Inf.

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Abstract1 -This paper presents a Pareto-based maintenance decision system for rail welds in a regional railway network. Weld health condition data are collected using a train in operation. A Hilbert spectrum-based approach is used for data processing to detect and assess the weld quality based on multiple registered dynamic responses in the axle box acceleration measurements. The assessment of the welds is stochastic in nature and variant over time, so a set of robust and predictive key performance indicators is defined to capture the weld degradation dynamics during a given maintenance period. Using a scenario-based approach, two objective functions are defined, performance and the number of weld replacements. Evolutionary multi-objective optimization is employed to optimize the objective functions so that the trade-offs between performance and cost support decision-making for railway network maintenance. The results of the proposed methodology show that the infrastructure manager can localize field inspections and maintenance efforts on the area with the most critical welds. To showcase the capability of the proposed methodology, measurements from a regional railway network in Transylvania, Romania are employed.

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Intelligent Fault Diagnosis of the High-Speed Train With Big Data Based on Deep Neural Networks

Cited by 190 publications

References 26 publications

Convolutional Recurrent Neural Network for Fault Diagnosis of High‐Speed Train Bogie

Convolutional Recurrent Neural Network for Fault Diagnosis of High‐Speed Train Bogie

Investigation on the Surface Properties of 5A12 Aluminum Alloy after Nd: YAG Laser Cleaning

Pareto-Based Maintenance Decisions for Regional Railways With Uncertain Weld Conditions Using the Hilbert Spectrum of Axle Box Acceleration

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