Complex Fuzzy System Based Predictive Maintenance Approach in Railways

Karaköse, Mehmet; Yaman, Orhan

doi:10.1109/tii.2020.2973231

Cited by 52 publications

(27 citation statements)

References 19 publications

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“…The paper [41] proposed a method for fuzzy classification of track surface fault types using image characteristic curves of different track defects. The paper [42] proposed a method to analyze the thermal imaging data of railway track and pantograph based on complex fuzzy system, and then to detect the railway status. The paper [43] proposed a method for condition monitoring and fault detection of the acquired orbital image using image processing and particle swarm optimization (PSO) methods.…”

Section: Related Workmentioning

confidence: 99%

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SCueU-Net: Efficient Damage Detection Method for Railway Rail

Liang

Lei

et al. 2020

IEEE Access

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Automatic detection of industrial product damage using machine learning is a promising research area. At the same time, various machine learning methods based on convolutional neural networks have a very important role in the application of visual automatic detection. Therefore, the machine visionbased automatic detection of high-speed railway rail damage has received widespread attention. This paper proposes an efficient detection method for the damage of high-speed railway rails called SCueU-Net. For the first time, the combination of U-Net graph segmentation network and the saliency cues method of damage location is applied to the task of high-speed railway rail damage detection. The experimental results show that our method has a detection accuracy rate of 99.76%, which is 6.74% higher than the recent method in damage identification accuracy, which improves the detection efficiency of rail damage significantly. INDEX TERMS High-speed railway, machine learning, data augmentation, rail damage detection. I. INTRODUCTION

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Section: Related Workmentioning

confidence: 99%

“…It overcomes the interference of image noise and solves the current problem of low detection efficiency. In addition, in order to curtail the research period, this paper selects Type-I RSDDs high-speed railway track dataset [42] for experiment. Experiment results prove that the method is the most advanced way to solve such problems.…”

mentioning

confidence: 99%

SCueU-Net: Efficient Damage Detection Method for Railway Rail

Liang

Lei

et al. 2020

IEEE Access

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“…Infrared thermography (IRT) has provided an advanced tool for equipment condition monitoring in recent years [17][18][19]. Compared with vibration monitoring, IRT holds unique superiorities such as non-contact, simple installation, high precision, high sensitivity, etc [16].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

Shao

Xia

Han

et al. 2021

IEEE Trans. Ind. Inf.

319

100

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The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes. In this paper, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using a modified convolutional neural network (CNN) with transfer learning. First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system. Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN. Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain. The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions. The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system.

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“…This relatively recent, wide spectrum, fast advancing, high potential, huge interest drawing research direction from the industrial branch is guided at the conceptual level by the Industrial Internet of Things (IIoT) [ 1 , 2 , 3 , 4 , 5 ] and Industry 4.0 [ 6 , 7 , 8 , 9 , 10 ] paradigms, both very similar approaches which are pleading towards the introduction of digitalization into industry and connecting the physical world to the Internet [ 11 ]. The potential benefits of this endeavor are not neglectable in any way and will be better highlighted with the introduction of more innovative technological solutions, such as the ones described in [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Complete Automation of an Energy Consumption Reduction Strategy from a Water Treatment and Distribution Facility, Inside an Industrial Internet of Things-Compliant Proactive Historian Application

Nicolae

Korodi

Silea

2021

Sensors

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The Industrial Internet of Things and Industry 4.0 paradigms are steering the industrial landscape towards better connected entities, superior interoperability and information exchange, which lays the basis for developing more intelligent solutions that are already starting to bring numerous benefits. The current research aligns to this course, in an attempt to build an automated and autonomous software tool, capable of reducing the energy consumption of a water treatment and distribution facility, by optimizing the water sources usage. Based on several previous researches, the present paper details both the complete automation of the optimizing strategy inside a proactive historian application and the tests executed with the finished solution. Possessing the abilities to directly influence the monitored system in a non-invasive manner, and to link all the sequences of the algorithm automatically, the solution is now ready for long-term functioning without any external interference.

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Complex Fuzzy System Based Predictive Maintenance Approach in Railways

Cited by 52 publications

References 19 publications

SCueU-Net: Efficient Damage Detection Method for Railway Rail

SCueU-Net: Efficient Damage Detection Method for Railway Rail

Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

Complete Automation of an Energy Consumption Reduction Strategy from a Water Treatment and Distribution Facility, Inside an Industrial Internet of Things-Compliant Proactive Historian Application

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