A knowledge-based architecture for distributed fault analysis in power networks

Rayudu, Ramesh

doi:10.1016/j.engappai.2010.02.007

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…Data-driven modeling approaches have high requirements for the quality and distribution of monitoring data and have more difficulty identifying underlying mechanistic invariant relationships masked by dynamic data features [26]. Recently, fault diagnosis techniques based on the fusion of quantitative data and qualitative knowledge driven by the same have gained significant attention [27]. Fault diagnosis techniques based on qualitative methods focus on the causality and correlation between system states, which can compensate for the impact of data deficiencies on quantitative models, and with low computational cost, it is easier to realize real-time online diagnosis [28].…”

Section: Introductionmentioning

confidence: 99%

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

Yan

Jing

et al. 2023

Processes

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In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly facilitate the processing and management of industrial information flow, which helps the development of real-time fault diagnosis (RTFD) technology. This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. The RTFD process is introduced in detail, starting with the data acquisition process. The current RTFD methods are divided into methods based on independent feature extraction, methods based on “end-to-end” neural networks, and methods based on qualitative knowledge reasoning from a new perspective. In addition, this paper discusses the challenges and potential trends of RTFD in future development to provide a reference for researchers focusing on this field.

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Section: Introductionmentioning

confidence: 99%

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

Yan

Jing

et al. 2023

Processes

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“…The fault diagnosis problem of analog circuits can be considered to be far from a definitive solution, even if in the last years a large amount of research has been dedicated to it (e.g., [1][2][3][4][5][6][7][8][9][10][11][12]). This is because the growing complexity of electronic circuits and the increasing number of applications characterized by the coexistence of digital and analog systems.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

et al. 2021

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In this paper, we present a new method designed to recognize single parametric faults in analog circuits. The technique follows a rigorous approach constituted by three sequential steps: calculating the testability and extracting the ambiguity groups of the circuit under test (CUT); localizing the failure and putting it in the correct fault class (FC) via multi-frequency measurements or simulations; and (optional) estimating the value of the faulty component. The fabrication tolerances of the healthy components are taken into account in every step of the procedure. The work combines machine learning techniques, used for classification and approximation, with testability analysis procedures for analog circuits.

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“…With the promotion of China's smart grid process, how to organize and comprehensive application of grid knowledge, these are the urgent problems to be solved that realize "strong smart grid (self-healing)" characteristics [1][2] . However, the current research on grid data and information processing at our country and abroad country is still in the stage of information fusion,whose typical character is to obtain from single and multiple information sources of data and information.Then,these data are combined, associated and synthesized, in order to estimate or predict the running state of power network.…”

Section: Introductionmentioning

confidence: 99%

Power Grid Decision Information Generation Model Based on Knowledge Clustering-Ensemble

Wang

2014

AMM

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For there is a lower level of fusion grid decision-making process and decision-making are not smart enough ,this paper presents a power grid's management and control task demand – knowledge matching model, to help staff accurately and rapidly access to the necessary knowledge, before making a decision.At the same time,in order to solve the problem ,this paper, in the TBM framework ,presents a grid knowledge clustering-ensemble method. Then, knowledge clustering-ensemble model is applied to the electrical network management control decision-making process. Test of the fault diagnosis has proved that ,the model is able to maintain the original good performance of the evidence theory and TBM framework, also can get more useful information for decision-making.

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A knowledge-based architecture for distributed fault analysis in power networks

Cited by 11 publications

References 27 publications

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

Power Grid Decision Information Generation Model Based on Knowledge Clustering-Ensemble

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