Thermographic Fault Diagnosis of Ventilation in BLDC Motors

Głowacz, Adam

doi:10.3390/s21217245

Cited by 80 publications

(27 citation statements)

References 24 publications

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“…We believe that the ideas used to build our model can also be extended to the field of feature extraction in the future. And it can be applied to many related problems (e. g. [ 10 , 11 ]).…”

Section: Discussionmentioning

confidence: 99%

Micro Learning Support Vector Machine for Pattern Classification: A High-Speed Algorithm

Yan

Wang

Lei

2022

Computational Intelligence and Neuroscience

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The support vector machine theory has been developed into a very mature system at present. The original support vector machine to solve the optimization problem is transformed into a direct calculation formula of line in this paper and the model is o n 2 time complexity. In the model of this article, weited theory, multiclassification problem and online learning have all become the direct inference, and we have applied the new model to the UCI data set. We hope that in the future, this model will be useful in real-world problems such as stock forecasting, which require nonlinear hi-speed algorithms.

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Section: Discussionmentioning

confidence: 99%

Micro Learning Support Vector Machine for Pattern Classification: A High-Speed Algorithm

Yan

Wang

Lei

2022

Computational Intelligence and Neuroscience

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“…Thermal images can be used for supervised learning, training a classifier with readings from the healthy conditions as completed e.g., for brushless DC motors in Ref. [148]. Furthermore, these can be used for the detection of non-mechanical failures such as fire detection and the monitoring of the high voltage transformer and other electrical systems (power electronics, control system, etc.).…”

Section: Passive Irtmentioning

confidence: 99%

Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years

Civera

Surace

2022

Sensors

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A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000–2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well.

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“…In addition to medical imaging analysis, some applications could be further realized for other related thermographic fault diagnosis in engineering. Recently, Adam Glowacz innovatively proposed the methods of feature extraction and fusion for thermal imaging (namely, Binarized Common Areas of Maximum Image Differences—Fusion method [ 143 ] and Common Part of Arithmetic Mean of Thermographic Images method [ 144 ]), which was demonstrated to be quite efficient for fault diagnosis of electrical devices and electric power tools. Similar to medical imaging, thermal images may present diverse features between machines with and without faults.…”

Section: Conclusion and Future Perspectivementioning

confidence: 99%

A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis

Guo

Chen

et al. 2022

BioMed Eng OnLine

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Despite numerous clinical trials and pre-clinical developments, the diagnosis of cracked tooth, especially in the early stages, remains a challenge. Cracked tooth syndrome is often accompanied by dramatic painful responses from occlusion and temperature stimulation, which has become one of the leading causes for tooth loss in adults. Current clinical diagnostical approaches for cracked tooth have been widely investigated based on X-rays, optical light, ultrasound wave, etc. Advances in artificial intelligence (AI) development have unlocked the possibility of detecting the crack in a more intellectual and automotive way. This may lead to the possibility of further enhancement of the diagnostic accuracy for cracked tooth disease. In this review, various medical imaging technologies for diagnosing cracked tooth are overviewed. In particular, the imaging modality, effect and the advantages of each diagnostic technique are discussed. What’s more, AI-based crack detection and classification methods, especially the convolutional neural network (CNN)-based algorithms, including image classification (AlexNet), object detection (YOLO, Faster-RCNN), semantic segmentation (U-Net, Segnet) are comprehensively reviewed. Finally, the future perspectives and challenges in the diagnosis of the cracked tooth are lighted.

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Thermographic Fault Diagnosis of Ventilation in BLDC Motors

Cited by 80 publications

References 24 publications

Micro Learning Support Vector Machine for Pattern Classification: A High-Speed Algorithm

Micro Learning Support Vector Machine for Pattern Classification: A High-Speed Algorithm

Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years

A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis

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