An intelligent fault detection and diagnosis monitoring system for reactor operational resilience: Power transient identification

Mendoza, Mario; Tsvetkov, Pavel V.

doi:10.1016/j.pnucene.2022.104529

Cited by 12 publications

(4 citation statements)

References 14 publications

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“…Rather, they deploy various hardware-based techniques as mentioned in Fig. 11, to diagnose abnormalities in data [104,[137][138][139][140][141][142][143][144][145]. Combinations of various hardware techniques can also be used.…”

Section: Hardware-based Techniquesmentioning

confidence: 99%

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

Sharma,

Gupta,

Gupta

et al. 2024

CMES

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The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making them more intelligent and connected. However, this advancement comes with challenges related to the effectiveness of IoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensure their proper functionality. The success of smart systems relies on their seamless operation and ability to handle faults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore, sensor faults can compromise the system's reliability and undermine the trustworthiness of smart environments. To address these concerns, various techniques and algorithms can be employed to enhance the performance of IoT devices through effective fault detection. This paper conducted a thorough review of the existing literature and conducted a detailed analysis. This analysis effectively links sensor errors with a prominent fault detection technique capable of addressing them. This study is innovative because it paves the way for future researchers to explore errors that have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essential factors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and their corresponding recommended techniques. Additionally, the paper presents a methodical overview of fault detection techniques employed in smart devices, including the metrics used for evaluation. Furthermore, the paper examines the body of academic work related to sensor faults and fault detection techniques within the domain. This reflects the growing inclination and scholarly attention of researchers and academicians toward strategies for fault detection within the realm of the Internet of Things.

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Section: Hardware-based Techniquesmentioning

confidence: 99%

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

Sharma,

Gupta,

Gupta

et al. 2024

CMES

View full text Add to dashboard Cite

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“…This study provides a novel approach to gas path diagnostics in two-engines for industrial gas turbines with shafts that integrate with SVM-ANN. Research on methods for diagnosing gear faults must prioritize increasing the diagnosis rate [14,15].…”

Section: Motivationmentioning

confidence: 99%

Research on Power Equipment Fault Diagnosis Based on Improved SVM Algorithm

Ningyuan Yu, Bin An

2024

jes

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Recently, Data collected from safety relays and circuit breakers has been used in fault diagnostics to identify power system components that are either failing or have tripped. A tiny percentage of heating zones may cause problems when identifying faults, although the chance of aberrant heat production in power equipment is modest. The power transformer is one of the most crucial components of any electrical system. Discovering any concealed defects in power equipment is essential to ensuring a continuous supply of electrical power for customers. The challenging characteristic of such fault diagnosis in power equipment is the greater potential for harm and a great deal of time invested in testing and training. Hence, in this research, Smart Support Vector Machine enabled Artificial Neural Network (SVM-ANN) technologies have been improved for fault diagnosis in power equipment for industry. The Genetic Algorithm (GA) process the power equipment for industrial data processing and monitoring in that fault diagnosis. The classification of strategies used and their relationships to power equipment processes to identify significant trends and research problems related to intelligent fault diagnosis systems for industry. The experimental analysis of SVM-ANN outperforms fault diagnosis using the power equipment industry in terms of performance, accuracy, prediction ratio, efficiency, and error rate.

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“…Whether high efficiency, short time, high quality to solve these contradictions related to the people's livelihood and China's comprehensive national strength of the enhancement of the research and development of power quality issues has also become the focus of many research institutions in our country and even abroad and the direction of continuous improvement and development [2]. The real-time voltage, real-time current RMS value and various power parameters in the power grid system are basically based on the detection of the power system and the power system in the state of operation of the equipment [3].…”

Section: Introductionmentioning

confidence: 99%

Design of Online Intelligent Detection System for Power Quality and Fault Identification in Distribution Networks

He,

Zhang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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This paper lays the foundation for the subsequent analysis of power quality in distribution networks by briefly describing the indicators of power quality. Utilizing the zero sequence voltage mutation detection fault of a small current routing algorithm, the instantaneous values of voltage and current of each phase of the three-phase circuit are quickly calculated based on the electrical parameters of instantaneous reactive power and the online intelligent detection system is established. The effectiveness and feasibility of the designed detection system and detection algorithm in detecting electrical energy parameters have been verified through experiments. The results show that the error of voltage measurement is within 0.0003-0.002, the frequency error of the system is up to 0.0117%, the measurement error of three-phase unbalance is distributed between 0.001 and 0.1, and the error of negative sequence and zero sequence is between ±0.003, which meet the system design requirements. It shows that the use of online intelligent detection systems can improve the quality of distribution network operation and fault identification accuracy, improve power quality, and effectively improve the reliability of power supply.

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An intelligent fault detection and diagnosis monitoring system for reactor operational resilience: Power transient identification

Cited by 12 publications

References 14 publications

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

Research on Power Equipment Fault Diagnosis Based on Improved SVM Algorithm

Design of Online Intelligent Detection System for Power Quality and Fault Identification in Distribution Networks

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