A Multi-Entity Knowledge Joint Extraction Method of Communication Equipment Faults for Industrial IoT

Liang, Kun; Zhou, Baoxian; Zhang, Yiying; He, Yeshen; Guo, Xiaojun; Zhang, Bo

doi:10.3390/electronics11070979

Cited by 7 publications

(4 citation statements)

References 24 publications

(48 reference statements)

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“…Saha et al [56] constructed a core domain ontology for joining processes (CDOJP) that utilizes web ontology language (OWL) to address semantic inconsistencies present in the welding process. Liang et al [57] proposed a new method of multi-entity knowledge joint extraction (MEKJE) for faulty communication devices in Industrial IoT. The method constructs a multi-task tightly coupled model for fault entity and fault relationship extraction.…”

Section: Process Basic Knowledgementioning

confidence: 99%

A Review and Prospects of Manufacturing Process Knowledge Acquisition, Representation, and Application

Wu,

Liang

2024

Machines

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The evolution of knowledge acquisition and representation in manufacturing technologies is vital for translating complex manufacturing data into actionable insights and advancing a comprehensive knowledge framework. This framework is pivotal in driving innovation and efficiency in intelligent manufacturing. This review aggregates recent research on knowledge acquisition and representation within the manufacturing process, addressing existing challenges and mapping potential future developments. It includes an analysis of 123 papers that focus on harnessing advanced intelligent analytics to extract operationally relevant knowledge from the extensive datasets typical in manufacturing environments. The narrative then examines the methodologies for constructing models of knowledge in manufacturing processes and explores their applications in manufacturing principles, design, management, and decision-making. This paper highlights the limitations of current technologies and projects emerging research avenues in the acquisition and representation of process knowledge within intelligent manufacturing systems, with the objective of informing future technological breakthroughs.

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Section: Process Basic Knowledgementioning

confidence: 99%

A Review and Prospects of Manufacturing Process Knowledge Acquisition, Representation, and Application

Wu,

Liang

2024

Machines

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“…However, the more corpus data provided, the more privacy and security challenges might occur during data processing and sharing [3,4]. Corpus, a set of wellsampled and processed electronic texts, is the basic resource for studies of linguistics and computational, especially language engineering for applications and devices in the IoT [5]. With the ever-widening coverage of IoT-related devices, the volume of data continues to increase, while the danger to privacy security increases simultaneously [6].…”

Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Multilingual Comparable Corpus Construction Method in Internet of Things

Weng,

Dong,

Chaomurilige

2024

Mathematics

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With the expansion of the Internet of Things (IoT) and artificial intelligence (AI) technologies, multilingual scenarios are gradually increasing, and applications based on multilingual resources are also on the rise. In this process, apart from the need for the construction of multilingual resources, privacy protection issues like data privacy leakage are increasingly highlighted. Comparable corpus is important in multilingual language information processing in IoT. However, the multilingual comparable corpus concerning privacy preserving is rare, so there is an urgent need to construct a multilingual corpus resource. This paper proposes a method for constructing a privacy-preserving multilingual comparable corpus, taking Chinese–Uighur–Tibetan IoT based news as an example, and mapping the different language texts to a unified language vector space to avoid sensitive information, then calculates the similarity between different language texts and serves as a comparability index to construct comparable relations. Through the decision-making mechanism of minimizing the impossibility, it can identify a comparable corpus pair of multilingual texts based on chapter size to realize the construction of a privacy-preserving Chinese–Uighur–Tibetan comparable corpus (CUTCC). Evaluation experiments demonstrate the effectiveness of our proposed provable method, which outperforms in accuracy rate by 77%, recall rate by 34% and F value by 47.17%. The CUTCC provides valuable privacy-preserving data resources support and language service for multilingual situations in IoT.

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“…The advantage of this type of method is that it is easy to analyze, good interpretability, but it needs to manually analyze and extract the features. At the same time, the model only considers the current time equipment failure-related features the lacks the consideration of the equipment's historical state information, which is a non-temporal prediction and can not be done to the future state of the equipment [6].…”

Section: Introductionmentioning

confidence: 99%

Research on neural network-based fault diagnosis and prediction method for power communication equipment

Yang,

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, facing the digital development of the power grid and the status quo of massive power communication equipment access and targeting the demand for highly intelligent operation and maintenance management of the power grid, combined with neural network technology, we propose an intelligent diagnosis model of power communication equipment faults. Adopting BERT as the vector embedding layer to obtain the vector sequence of fault text, designing a fault entity recognition model for power communication equipment based on BERT-BiGRU-CRF, and completing the construction of the relationship set of fault text. The proposed knowledge graph-based power communication equipment fault intelligent diagnosis model combined with the WBLA-based power communication equipment fault severity level recognition algorithm to obtain different severity fault information, from which a TFIDF-COS-based power communication equipment fault intelligent diagnosis algorithm is designed to realize intelligent diagnosis of power communication equipment faults. After testing, the TFIDF-COS algorithm can get the best optimization effect when the number of hidden layers of the selected algorithm is 1, and the initial learning rate is 0.05, and its accuracy rate can be kept above 98%. Compared with the traditional fault diagnosis system, in terms of the order of magnitude 100M, 500M, 1G, and 5G, the running time is reduced by 322s, 1874s, 4617s, and 7467s, and the accuracy rate is increased by 2.33%, 2.6%, 32.02%, and 61.4%, respectively. Therefore, this paper realizes the accurate positioning of power communication equipment faults and provides technical support for intelligent operation and maintenance of the power grid.

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A Multi-Entity Knowledge Joint Extraction Method of Communication Equipment Faults for Industrial IoT

Cited by 7 publications

References 24 publications

A Review and Prospects of Manufacturing Process Knowledge Acquisition, Representation, and Application

A Review and Prospects of Manufacturing Process Knowledge Acquisition, Representation, and Application

A Privacy-Preserving Multilingual Comparable Corpus Construction Method in Internet of Things

Research on neural network-based fault diagnosis and prediction method for power communication equipment

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