Flagging Implausible Inspection Reports of Distribution Transformers via Anomaly Detection

Xiang, Bin; Liu, Zhixiong; Zhang, Kunyi

doi:10.1109/access.2020.2989330

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…The identification of the busduct overheating fault is essentially a two-classification problem in machine learning, that is, identifying the normal state and overheating state. In recent years, many classification algorithms have emerged in machine learning (Xiang et al, 2020), such as KNN (K-nearest neighbor) (Cover and Hart, 1967), SVM (support vector machine) (Schölkopf and Smola, 2002), Naive Bayes (Murphy, 2006), decision tree (Quinlan, 1986), random forest (Breiman, 2001), and AdaBoost (Freund and Schapire, 1995). Among them, AdaBoost has been proven to be extremely successful in generating accurate classifiers, especially, applied to two-classification problems (Zhu et al, 2009).…”

Section: Adaboost Classifier For Overheating Identificationmentioning

confidence: 99%

Overheating fault alarming for compact insulated busways in buildings by gas sensing

Xiong¹,

Li²,

Li³

et al. 2023

Front. Energy Res.

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Addressing the overheating fault detection and alarming of insulated busways in buildings, a system based on sensing volatile gases generated by the thermal degradation of the busduct insulation layer was proposed. By monitoring the concentration of volatile gases in the environment, the overheating fault of the busducts can be found early. The thermal degradation process of the busway insulating layer is analyzed, and the pyrolysis characteristic gas at low temperature is studied. The experimental platform has been built, by which the relation between the concentration of volatile gases and temperature of the insulated layer has been studied. By testing, the concentration of volatile organic compounds (VOCs) is proposed as the basis for judging the overheating fault in the alarming system. With the collected samples for training and testing, the AdaBoost classifier is used to identify the overheating fault. Finally, the design of the overheating fault alarming system is given.

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Section: Adaboost Classifier For Overheating Identificationmentioning

confidence: 99%

Overheating fault alarming for compact insulated busways in buildings by gas sensing

Xiong¹,

Li²,

Li³

et al. 2023

Front. Energy Res.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The identification of the busduct overheating fault is essentially a two-classification problem in machine learning, that is, identifying the normal state and overheating state. In recent years, many classification algorithms have emerged in machine learning (Xiang et al, 2020), such as KNN (K-nearest neighbor) (Cover and Hart, 1967), SVM (support vector machine) (Schölkopf and Smola, 2002), Naive Bayes (Murphy, 2006), decision tree (Quinlan, 1986), random forest , and AdaBoost (Freund and Schapire, 1995). Among them, AdaBoost has been proven to be extremely successful in generating accurate classifiers, especially, applied to two-classification problems (Zhu et al, 2009).…”

Section: Adaboost Classifier For Overheating Identificationmentioning

confidence: 99%

Advanced Protection for the Smart Grid

Nabab Alam,

Khurshaid,

Yen Shih

et al. 2023

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…Because the amount of current data is huge, we argue that current anomaly detection requires a scalable system architecture ability including real-time data processing, resource efficiency, fault tolerance, and extensibility. Xiang et al [ 5 ] identified abnormal samples in distribution transformer inspection reports, assisted inspectors to complete inspection work correctly and efficiently. Balouji et al [ 6 ] integrated an advanced generative adversarial network to detect anomalies in FDIA data.…”

Section: Introductionmentioning

confidence: 99%

Hot-Pressing Furnace Current Monitoring and Predictive Maintenance System in Aerospace Applications

Chen

Zhang

Wang

et al. 2023

Sensors

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This research combines the application of artificial intelligence in the production equipment fault monitoring of aerospace components. It detects three-phase current abnormalities in large hot-pressing furnaces through smart meters and provides early preventive maintenance. Different anomalies are classified, and a suitable monitoring process algorithm is proposed to improve the overall monitoring quality, accuracy, and stability by applying AI. We also designed a system to present the heater’s power consumption and the hot-pressing furnace’s fan and visualize the process. Combining artificial intelligence with the experience and technology of professional technicians and researchers to detect and proactively grasp the health of the hot-pressing furnace equipment improves the shortcomings of previous expert systems, achieves long-term stability, and reduces costs. The complete algorithm introduces a model corresponding to the actual production environment, with the best model result being XGBoost with an accuracy of 0.97.

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Flagging Implausible Inspection Reports of Distribution Transformers via Anomaly Detection

Cited by 7 publications

References 23 publications

Overheating fault alarming for compact insulated busways in buildings by gas sensing

Overheating fault alarming for compact insulated busways in buildings by gas sensing

Advanced Protection for the Smart Grid

Hot-Pressing Furnace Current Monitoring and Predictive Maintenance System in Aerospace Applications

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