Fault coil location of inter‐turn short‐circuit for direct‐drive permanent magnet synchronous motor using knowledge graph

Lv, Ke; Gao, Caixia; Si, Jikai; Feng, Haichao; Cao, Wenping

doi:10.1049/iet-epa.2020.0123

Cited by 19 publications

(5 citation statements)

References 34 publications

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“…Xu et al [32] applied word to vector (word2vector) and priori-knowledge convolutional neural network to aircraft fault diagnosis. Lv et al [33] established a knowledge graph-based diagnosis tool to detect and locate the fault coil. However, the knowledgebased method lacks the support for real-time running data and is difficult to be applied in isolation.…”

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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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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“…Therefore, the validity of distinguishing the degree of failure based on the vibration signal under such conditions needs to be verified. Lv et al [12] proposed a diagnostic tool based on knowledge graph (KG) for coil fault detection and location of DDPMSM. However, it is necessary to build accurate diagnostic models, and the modeling process is complicated and of poor generality.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis in asynchronous motors based on an optimal deep bidirectional long short-term memory networks

Xu,

Li,

Liu

et al. 2023

Meas. Sci. Technol.

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Fault diagnosis of asynchronous motors has become a pressing need in the metallurgical industry. Due to the complex structure of asynchronous motors, fault types and fault characteristics are diverse, with strong nonlinear relationships between them, which leads to the difficulty of fault diagnosis. To efficiently and accurately diagnose various motor faults, we propose a fault diagnosis method based on an optimal deep bidirectional long short-term memory neural network. First, the three-phase current, multidimensional vibrational signal, and acoustic signal of the asynchronous motor are collected and construct diverse and robust data sample set to enhance the generalization ability of the model. Next, a modified 3D logistics-sine complex chaotic map (3D LSCCM) is constructed to improve the global and local search capabilities of the pigeon swarm optimization algorithm (PIO). Then, we construct a deep bidirectional long short-term memory network (Bid-LSTM) with attention mechanism to mine high-value fault characteristic information. Meanwhile, the optimal hyper-parameters of the deep ABid-LSTM are explored using the modified PIO to improve the training performance of the model. Finally, the fault data samples of asynchronous motor are induced to train and test the proposed framework. By fusing diverse data samples, the proposed method outperforms conventional deep Bid-LSTM and achieves fault diagnosis accuracy of 99.13%. It provides a novel diagnostic strategy for motor fault diagnosis.

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“…It is an efficient knowledge representation approach that can be modelled digitally and fused with numerical data collected by sensors. Since many researchers have proved the effectiveness of KG in the domain of power equipment [6,[15][16][17], the study of event KG systems is essential for achieving a self-understanding, self-decision-making behaviour of equipment O&M [5].…”

Section: Introductionmentioning

confidence: 99%

An event knowledge graph system for the operation and maintenance of power equipment

Tian

Song

Sheng

et al. 2022

IET Generation Trans & Dist

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Power equipment operation and maintenance (O&M) requires plenty of domain knowledge to improve equipment security and power grid reliability. However, most knowledge is implicitly represented by the semantic text, which is hard to be comprehended by algorithms and restricts the intelligence level of equipment O&M. Therefore, this article proposes an event knowledge graph system to automatically extract event knowledge from O&M reports and represent the knowledge by an event knowledge graph (KG). First, a bidirectional long short-term memory network (BiLSTM) combined with conditional random field (CRF) is employed to recognize the entities. Next, an attention-based sequence-to-sequence model is proposed to detect multiple events and extract documentlevel representation vectors. Then, a tree-based table filling strategy is utilized to complete the artificially designed event tables. Finally, the proposed system renders the event tables into an event KG. Verified by the experiments, the proposed system outperforms the baselines with the F-measure of 83.5% for event extraction and 94.2% for event detection. When dealing with coexisting events and scattering arguments, the F-measure of the proposed system achieves 74.8% and 79.0%, respectively. Moreover, the proposed system has positive effects on intelligent retrieval, fault diagnosis, and condition assessment, verifying its potential value for equipment O&M.

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Fault coil location of inter‐turn short‐circuit for direct‐drive permanent magnet synchronous motor using knowledge graph

Cited by 19 publications

References 34 publications

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

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

Fault diagnosis in asynchronous motors based on an optimal deep bidirectional long short-term memory networks

An event knowledge graph system for the operation and maintenance of power equipment

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