Ontology-based Fault Diagnosis

Liu, Bin; Wu, Jian; Yao, Li; Ding, Zhijun

doi:10.1145/3307363.3307381

Cited by 15 publications

(6 citation statements)

References 25 publications

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“…For example, an ontology-based schema may contain "component-isPartOf-subsystem" while "receiver-isPartOf-radar" could be instances of this schema, i.e., a knowledge graph. Recently, researchers have more focused on ontology-based fault knowledge modeling to achieve fault knowledge extraction and sharing [39][40][41][42]. Therefore, ontology is well suited for describing a fault knowledge model, and knowledge graphs as instances of an ontology provide an ideal platform for acquiring and presenting fault knowledge.…”

Section: Knowledge Graphmentioning

confidence: 99%

Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis

Tang

Cui

et al. 2023

Sensors

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Fault diagnosis is crucial for repairing aircraft and ensuring their proper functioning. However, with the higher complexity of aircraft, some traditional diagnosis methods that rely on experience are becoming less effective. Therefore, this paper explores the construction and application of an aircraft fault knowledge graph to improve the efficiency of fault diagnosis for maintenance engineers. Firstly, this paper analyzes the knowledge elements required for aircraft fault diagnosis, and defines a schema layer of a fault knowledge graph. Secondly, with deep learning as the main method and heuristic rules as the auxiliary method, fault knowledge is extracted from structured and unstructured fault data, and a fault knowledge graph for a certain type of craft is constructed. Finally, a fault question-answering system based on a fault knowledge graph was developed, which can accurately answer questions from maintenance engineers. The practical implementation of our proposed methodology highlights how knowledge graphs provide an effective means of managing aircraft fault knowledge, ultimately assisting engineers in identifying fault roots accurately and quickly.

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Section: Knowledge Graphmentioning

confidence: 99%

Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis

Tang

Cui

et al. 2023

Sensors

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“…The core of fault diagnosis methods based on deep transfer learning is domain generalization. The main idea of fault diagnosis [10][11][12] based on lifelong learning is to learn new knowledge in real time without forgetting old knowledge.…”

Section: Background: Intelligent Fault Diagnosis Driven By Artificial...mentioning

confidence: 99%

Research on lifelong learning method for intelligent diagnosis of rail transit equipment

Cao

2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

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With the gradual maturity of statistical machine learning, it is now time to break the traditional model of isolated learning and turn to lifelong learning, pushing machine learning to a new level. Applications such as intelligent assistants, chat robots, and physical robots are also in urgent need of this lifelong learning ability. Without the ability to accumulate knowledge and use it to gradually learn more, a system may never be truly intelligent. Continuous learning algorithms attempt to achieve the same capabilities for neural networks and solve catastrophic forgetting problems. Therefore, in essence, continuous learning performs incremental learning for new tasks.

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“…Zhou et al [34] performed fault diagnosis of mechanical components based on ontology and signal analysis, which was verified by rolling bearing fault data, showing that the method can better handle fault diagnosis. Liu et al [35] analyzed the literatures on ontology-based fault diagnosis in the last decade from the perspective of knowledge source and usage and suggested that ontology can be combined with other methods to improve efficiency. Knowledge graph provides an ability to organize, manage and understand knowledge by describing concepts, entities, and relationships between them in the objective world in a structured way.…”

Section: B Knowledge Graph-assisted Fault Diagnosismentioning

confidence: 99%

CNC Machine Tool Fault Diagnosis Integrated Rescheduling Approach Supported by Digital Twin-Driven Interaction and Cooperation Framework

Liu

et al. 2021

IEEE Access

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The problems of CNC machine tool (CNCMT) fault diagnosis and production rescheduling have attracted continuous attention because of their great significance to the manufacturing industry. Digital twin is a supporting technology for achieving smart manufacturing and provides a new paradigm for solving these problems. This paper explores a digital twin-driven interaction and cooperation framework and proposes the architecture and implementation mechanism to enable the sharing of data, knowledge, and resource, to realize the fusion of physical space and cyber space, and to improve the accuracy of fault diagnosis. Under this framework, aiming at the influence of CNCMT failure on the initial production planning, a self-adaptation rescheduling method based on Monte Carlo Tree Search (MCTS) algorithm is proposed to provide support for developing more efficient production planning. Finally, the effectiveness of the proposed framework is validated by experimental study. The framework and integrated rescheduling approach can provide guidance for enterprises in implementing CNCMT maintenance and production scheduling to meet high accuracy and reliability requirements.

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Ontology-based Fault Diagnosis

Cited by 15 publications

References 25 publications

Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis

Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis

Research on lifelong learning method for intelligent diagnosis of rail transit equipment

CNC Machine Tool Fault Diagnosis Integrated Rescheduling Approach Supported by Digital Twin-Driven Interaction and Cooperation Framework

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