Fault Tree Analysis of Pantograph Type Current Collector Based on Ontology Modeling

Song, Long Long; Wang, Tai Yong; Xu, Lei; Xiao, Song

doi:10.4028/www.scientific.net/kem.693.1371

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…The traditional pantograph detection method is based on the manual scheme [6]. The disadvantage of this scheme is that the state of the pantograph cannot be monitored in real time, which requires a large amount of labor costs [7].…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Detection Algorithm of Pantograph Based on Edge Computing Image Processing

2020

IEEE Access

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Traditional pantograph detection technology has installed a large number of various types of sensors on the pantograph, which is not conducive to the flow characteristics of the pantograph. Traditional detection technology can neither monitor the operation status of the pantograph in real time, nor guarantee the safe operation of the entire train. The existing schemes are only manual solutions, and intelligent real-time monitoring is performed without error, and a lot of labor costs are required. Focusing on the shortcomings of traditional pantographs in sliding plate wear and sliding plate crack detection, an improved mean filtering algorithm is proposed to optimize the image of pantograph, and the adaptive Canny edge detection technology is used to accurately calculate the skateboard wear. Firstly, the improved image algorithm is used to pre-treat the pantograph slide wear and crack length, then the improved mean filtering algorithm is combined with adaptive Canny operator for image optimization detection, and finally the edge calculation is used for crack identification. An optimized real-time on-line detection technology is proposed, which can accurately measure the wear and tear of skateboards and cracks. For different experimental environments, the optimization algorithm proposed in this paper is used to detect pantographs in real time. The experimental results show that, compared with the traditional detection algorithm, the detection accuracy of sliding plate wear can reach 0.5 mm, and and that of sliding plate cracks can reach 0.4 mm. The crack recognition accuracy of skates is as high as 93%.INDEX TERMS Adaptive canny edge monitoring, dynamic calibration filtering, pantograph failure detection, second generation curve wave, edge computing.

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Section: Introductionmentioning

confidence: 99%

Research on Fault Detection Algorithm of Pantograph Based on Edge Computing Image Processing

2020

IEEE Access

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An Ontology-Based Cognitive Model for Faults Diagnosis of Hazardous Chemical Storage Devices

Feng

Chen

Peng

2018

International Journal of Cognitive Informatics and Natural Intelligence

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Due to high temperature, high pressure, high corrosion, and many other factors, the hazardous chemical device is facing more severe security challenges than other industries. Now, the monitoring methods have been very mature, which play a basic monitoring role, not a predictive fault diagnosis. In this article, the hazardous chemical device's status data will be collected from the existing industrial monitoring network, the real-time data will be preprocessed and then stored in a database, and the data will be imported to the real-time data into the ontology cognitive model; the data will be performed by big data processing and automatic reasoning so that real-time status of hazardous chemical device and the warning of security risks predict are easily obtained at any time. The model is proposed to solve the problem of knowledge representation and reasoning of the hazardous chemical device based on ontology. The model is analyzed and implemented in Protégé software.

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Fault Tree Analysis of Pantograph Type Current Collector Based on Ontology Modeling

Cited by 2 publications

References 7 publications

Research on Fault Detection Algorithm of Pantograph Based on Edge Computing Image Processing

Research on Fault Detection Algorithm of Pantograph Based on Edge Computing Image Processing

An Ontology-Based Cognitive Model for Faults Diagnosis of Hazardous Chemical Storage Devices

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