Proceedings ArticleDOI
Knowledge-Graph Based Multi-Target Deep-Learning Models for Train Anomaly Detection
Zhiliang Qin,Chen Cen,Wang Jie,Teo Sin Gee,Vijay Ramaseshan Chandrasekhar,Zhongbo Peng,Zeng Zeng +6 more
TLDR
By establishing a knowledge graph of the normally operating systems, a progressive approach to use a multi-target network to segment each component of the considered system sequentially by decoupling the segmentation and the classification task is proposed.Abstract:
The state-of-art image segmentation algorithms can be applied to accurately localize objects by using deep convolutional neural networks (CNN). In this paper, we consider the anomaly detection problem encountered in a train wheel system. We propose a progressive approach to use a multi-target network to segment each component of the considered system sequentially by decoupling the segmentation and the classification task. Moreover, we use the knowledge graph approach to establish a semantic consistency matrix by quantifying the spatial relationship between various components. We show that by establishing a knowledge graph of the normally operating systems, we are able to identify a faulty component effectively.read more
Citations
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Book ChapterDOI
A Tutorial and Survey on Fault Knowledge Graph
XiuQing Wang,ShunKun Yang +1 more
TL;DR: This article will provide an overview of the fault KG and summarize the key techniques for building a KG to guide the construction of the KG in the fault domain, and introduces some of the open source tools that can be used to build a KGs process, enabling researchers and practitioners to quickly get started in this field.
Journal ArticleDOI
Knowledge Integration in Smart Factories
TL;DR: In a Smart Factory, manufacturing systems act largely autonomously on the basis of continuously collected data and can autonomously adapt to events through the ability to intelligently analyze data and act as adaptive manufacturing systems that consider changes in production, the supply chain and customer requirements.
Journal ArticleDOI
Latency-aware service migration with decision theory for Internet of Vehicles in mobile edge computing
Journal ArticleDOI
Hierarchical Semantic Graph Reasoning for Train Component Detection
TL;DR: Cheng et al. as discussed by the authors proposed hierarchical graphical reasoning (HGR), which utilizes the hierarchical structures of trains for train component detection, which contains multiple graphical reasoning branches, each of which is utilized to conduct graphical reasoning for one cluster of train components based on their sizes.
Proceedings ArticleDOI
A Fault Knowledge Graph Creation Method and Application based on Fault Tree Analysis and Failure Mode, Effects and Criticality Analysis
Lixiang Wang,Jun Yu Cai +1 more
TL;DR: In this paper , a Fault Knowledge Graph (FKG) based on the knowledge graph (KG) in the field of faults is proposed for fault diagnosis, which can well analyze the relationship between various faults, achieve prediction, and promote development.
References
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Book ChapterDOI
Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images
TL;DR: This work trained CNNs on a database of photometric stereo images of metal surface defects, i.e. rail defects, and explored the impact of regularization methods such as unsupervised layer-wise pre-training and training data-set augmentation.
Proceedings ArticleDOI
Object detection meets knowledge graphs
TL;DR: A novel framework of knowledge-aware object detection is proposed, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm, which improves object detection through a re-optimization process to achieve better consistency with background knowledge.
Proceedings ArticleDOI
Material classification and semantic segmentation of railway track images with deep convolutional neural networks
TL;DR: A novel approach to visual track inspection using material classification and semantic segmentation with Deep Convolutional Neural Networks (DCNN) is described, showing that DCNNs trained end-to-end for material classification are more accurate than shallow learning machines with hand-engineered features and are more robust to noise.
Journal ArticleDOI
Multi-target deep neural networks
TL;DR: This work theoretically prove that different stable target models with shared learning paths are stable and can achieve optimal solutions respectively and proves that the multiple targets can boost each other to achieve optimization solutions.