Design of thermal imaging-based health condition monitoring and early fault detection technique for porcelain insulators using Machine learning

Singh, Laxman; Alam, Altaf; Kumar, K. Vijay; Kumar, Devendra; Kumar, Parvendra; Jaffery, Zainul Abdin

doi:10.1016/j.eti.2021.102000

Cited by 52 publications

(12 citation statements)

References 17 publications

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“…In order to show the effectiveness of the proposed AFRS, its performance is compared with other state of art methods. The accuracy [33] obtained by these methods is compared with the proposed method and is given in Table 4 and Figure 13.…”

Section: Resultsmentioning

confidence: 99%

“…It is due to the MC-HNN AM that easily selects the correct attraction basin for large size noisy test fingerprint patterns that result in greater average accuracy.In order to show the effectiveness of the proposed AFRS, its performance is compared with other state of art methods. The accuracy[33] obtained by these methods is compared with the proposed method and is given in Table4and Figure13.The results given in Table4show that the proposed method…”

mentioning

confidence: 96%

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A Robust Automatic Fingerprint Recognition System Using Multi-Connection Hopfield Neural Network

Yadav¹,

Singh²,

Jaffrey³

2022

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Automatic fingerprint recognition has received significant attention because of its excellent fingerprint stability characteristics. Fingerprints will now explode in popularity as online stores allow payments and secure smartphones. The main objective of this study is to develop a fingerprint recognition system to identify individuals by using a new method, termed as ‘Multi-Connection Hopfield Neural Network’. This might pave the way for the development of a more robust fingerprint recognition system that is more accurate and capable of working in noisy environments. Furthermore, such a system makes operations less complex and saves memory by employing additional tranches of auto-associative memory. This study uses three databases, namely FVC-2004, International NIST-4, and internal database, containing 80, 2000, and 2000 fingerprint images. The proposed fingerprint recognition system achieves 99.65% accuracy without noise. This study also shows that the proposed system works well in the presence of noise and achieves 96.07% accuracy in the presence of 50% random noise.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 96%

A Robust Automatic Fingerprint Recognition System Using Multi-Connection Hopfield Neural Network

Yadav¹,

Singh²,

Jaffrey³

2022

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“…The ability of thermal imaging to separate portions of an insulator image in to the defective and none defective areas makes it feasible to construct an insulator vital signs surveillance system using Gaussian Kernel‐supported SVM classifier design and processing methods image. [ 294 ] With high true positive, false negative, false discovery rates, positive predictive value of the system, and high quality control, identification and system health monitoring can be done with assurance in industrial manufacturing processes. [ 295 ] The maintenance of electrical installations in industrial sectors, including examination of the electric cables, bus bars, panel boards, and more crucial components such as capacitor banks for reactive power compensation, transformers, and power meters, is a classic application of infrared thermal sensing, where experts use the infrared thermal technique to perceive equipment's efficient functioning in accordance with predetermined limits.…”

Section: Applicationsmentioning

confidence: 99%

“…Reproduced with permission. [ 294 ] Copyright 2023, Elsevier.b) Thermal Infrared temperature sensing in different industrial applications, (i) steel strips (ii) pig iron (iii) sinter material (iv) rotatory cooler. Reproduced with permission.…”

Section: Applicationsmentioning

confidence: 99%

Luminescence‐Based Infrared Thermal Sensors: Comprehensive Insights

Fahad,

Li,

Zhai

et al. 2023

Small

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Recent chronological breakthroughs in materials innovation, their fabrication, and structural designs for disparate applications have paved transformational ways to subversively digitalize infrared (IR) thermal imaging sensors from traditional to smart. The noninvasive IR thermal imaging sensors are at the cutting edge of developments, exploiting the abilities of nanomaterials to acquire arbitrary, targeted, and tunable responses suitable for integration with host materials and devices, intimately disintegrate variegated signals from the target onto depiction without any discomfort, eliminating motional artifacts and collects precise physiological and physiochemical information in natural contexts. Highlighting several typical examples from recent literature, this review article summarizes an accessible, critical, and authoritative summary of an emerging class of advancement in the modalities of nano and micro‐scale materials and devices, their fabrication designs and applications in infrared thermal sensors. Introduction is begun covering the importance of IR sensors, followed by a survey on sensing capabilities of various nano and micro structural materials, their design architects, and then culminating an overview of their diverse application swaths. The review concludes with a stimulating frontier debate on the opportunities, difficulties, and future approaches in the vibrant sector of infrared thermal imaging sensors.

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“…Recently, machine learning (ML) methods have achieved state-of-the-art performance in a wide range of innovative applications of TI, such as livestock science [29] , electric substation safety [30] , advanced driver-assistance systems [31] and especially disease detection [32] , [33] . During COVID-19 pandemic, TI based on ML has been employed to monitor the health conditions of people wearing masks by monitoring the respiratory systems.…”

Section: Introductionmentioning

confidence: 99%

Low-cost thermal imaging with machine learning for non-invasive diagnosis and therapeutic monitoring of pneumonia

Meng

Fan

et al. 2022

Infrared Physics & Technology

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Design of thermal imaging-based health condition monitoring and early fault detection technique for porcelain insulators using Machine learning

Cited by 52 publications

References 17 publications

A Robust Automatic Fingerprint Recognition System Using Multi-Connection Hopfield Neural Network

A Robust Automatic Fingerprint Recognition System Using Multi-Connection Hopfield Neural Network

Luminescence‐Based Infrared Thermal Sensors: Comprehensive Insights

Low-cost thermal imaging with machine learning for non-invasive diagnosis and therapeutic monitoring of pneumonia

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