Infrared Thermography and Thermal Nondestructive Testing

Burleigh, Douglas; Burleigh, Doug; Вавилов, В. П.

doi:10.1007/978-3-030-48002-8

Cited by 74 publications

(47 citation statements)

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“…Based on the theory principles [ 35 ], the body's temperature regulation can be subject to two separate screening types: (1) is deterministic and usually set at 37.5 °C to prevent a large number of false positives; (2) the second is based on a statistical threshold value calculated on the sample measured temperature at actual measurement conditions and the pre-approved threshold value. For the selection of suspicious cases to comply with the decision laws, the unintended and structural ambiguity inputs need to be considered in the first instance [ 36 ].…”

Section: Resultsmentioning

confidence: 99%

Design a smart infrastructure monitoring system: a response in the age of COVID-19 pandemic

Saud

Kamal

2021

Innov. Infrastruct. Solut.

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Since the end of 2019, COVID-19 has been a challenge for the world, and it is expected that the world must take precautionary steps to tackle the virus spreading prior produces an efficient vaccine. Currently, most government efforts seek to avoid disseminating the coronavirus and forecast probable hot areas. The most susceptible to coronaviral infection are the healthcare staff due to their daily contact with potential patients. This article proposes a COVID-19 real-time system for tracking and identifying the suspected cases using an Internet of Things platform for capturing user symptoms and notify the authority. The proposed framework addressed four main components: (1) real-time symptom data collection via thermal scanning algorithm, (2) facial recognition algorithm, (3) a data analysis that uses artificial intelligence (AI) algorithm, and (4) a cloud infrastructure. A monitoring experiment was conducted to test three different ages, kid, middle, and older, considering the scanning distance influence compared with contact wearable sensors. The results show that 99.9% accuracy was achieved within a (500 ± 5) cm distance, and this accuracy tends to decrease as the distance the camera scanning and objects increased. The results also revealed that the scanning system's accuracy had been slightly changed as the environmental temperature dropped lower than 27 °C. Based on the high-temperature presence's simulated environment, the system demonstrated an effective and instant response via sending email and MQTT message to the person in charge of providing accurate identification of potential cases of COVID-19.

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Section: Resultsmentioning

confidence: 99%

Design a smart infrastructure monitoring system: a response in the age of COVID-19 pandemic

Saud

Kamal

2021

Innov. Infrastruct. Solut.

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“…In the following paragraphs, the methods which are most tightly connected to the topic presented here, are shortly discussed. Those include computer tomography [4], ultrasonic testing [6] and thermography [26]. Eddy current measurement [5] is the method which will be discussed in detail throughout this publication.…”

Section: Non-destructive Weld Testingmentioning

confidence: 99%

How To Develop a NDT Method For Weld Inspection in Battery Cell Manufacturing Using Deep Learning

Rohkohl¹,

Kraken²,

Schönemann³

et al. 2021

Preprint

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Battery cells are central components of electric vehicles. It is important for automotive OEM to utilize high quality battery cells to ensure high performance and safety of their vehicles. This results in the high demand for quality control measures and inspection methods in battery cell manufacturing. Particular relevant features of battery cells are welds for the internal electrical contact. Failures of these welds are often the cause for battery defects in the field and scrap during production. Consequently, there is a strong need to evaluate all welds during manufacturing. However, there is no established method which allows a quick, comprehensive, and cheap inline measurement of the weld quality. This paper presents a new eddy current based method for non-destructive testing of seam welds as well as a machine learning approach for its validation. A deep learning model has been trained on eddy current measurements to predict results from a reference inspection method, in this case computer tomography. The results prove that eddy current measurements can be used to replicate data acquired by computer tomography which means that eddy current measurements could be a suitable candidate for non-destructive 100% inline inspection. More general, this study demonstrates how machine learning may help to get deeper insights into measurement results and to validate new non-destructive testing techniques whose detailed features are yet unknown. The presented evaluation method enables understanding the capabilities and the limits of a new technique and to extract hidden features from the data. Furthermore, the usage of machine learning allows to perform these evaluations on artificial product samples with specific defects and features, which avoids the costly production physical samples.

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“…In the USSR, work on the creation of such devices was laid by the director of the Institute of Radio Engineering and Electronics Yu.V. Gulyaev and continued by his followers: Director of the small enterprise "IRTIS" at the Research Center for From the moment of the appearance of thermal imagers, up to the present day, developments are constantly underway to improve the electronic block of thermal imagers, which is responsible for converting a thermal image into an electrical signal [1], [7]. Examples of devices that make up such a unit include a thermoelectric converter Currently, computers are also being delivered together with thermal imagers with specialized software systems, which include the following methods for analyzing thermograms (IR images): a) measuring the absolute temperature at a point or zone; b) building a thermal profile in any position; c) construction of an isotherm; d) plotting the temperature changes over time (for a dynamic thermographic film); e) listing of thermograms and reports.…”

Section: Review Of Research In the Field Of Processing Ir Data Obtainmentioning

confidence: 99%

System for Automated Infrared Image Processing Based on Neural Network Technologies

Kananadze¹

2020

TEM Journal

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The article describes the main problems that non-destructive control experts encounter in processing and analyzing infrared (IR) images obtained using thermographic cameras: noisiness, information redundancy, excessive data volume, etc. A gradual process of automated digital processing and analysis of images on the basis of a software system is considered, which allows not only overcoming the drawbacks of the raw data described above, but also drawing a conclusion about the state of the object based on the obtained image independently. The ability to make expert decision is provided by a neural network component in a system on the basis of Kohonen network used for cluster image analysis. There are the examples of system operation applied to IR images of the lining of solid domestic waste (SDW) incinerator. The use of such systems allows reducing workload of an expert in processing large volumes of data and reducing the probability of errors due to such effect as vision "blurring".

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Infrared Thermography and Thermal Nondestructive Testing

Cited by 74 publications

References 0 publications

Design a smart infrastructure monitoring system: a response in the age of COVID-19 pandemic

Design a smart infrastructure monitoring system: a response in the age of COVID-19 pandemic

How To Develop a NDT Method For Weld Inspection in Battery Cell Manufacturing Using Deep Learning

System for Automated Infrared Image Processing Based on Neural Network Technologies

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