Thermography investigation of soldered joints for LED mounting

Mamchur, Yaryna; Ivanova, Vita; Monastyrsky, G.E.; Melnychenko, T.V.; Zheng, Gaofeng; Voronov, Sergey A.

doi:10.1109/elnano50318.2020.9088886

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…The mentioned works are also limited regarding the class of anomaly they can detect. The work presented in [20] used thermography to detect hardware anomalies. However, in that case, only the soldered joint quality of PCB mounted LEDs was analyzed.…”

Section: Discussionmentioning

confidence: 99%

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Non-intrusive Embedded Systems Anomaly Detection using Thermography and Machine Learning

Oliveira¹,

Bastos-Filho²,

Oliveira³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Quality control in electronic system manufacturing is achieved mainly through system testing. Device miniaturization and multilayer Printed Circuit Boards have increased the electronic circuit test complexity considerably and processes based on manual inspections have become outdated and inefficient. The concept of Industry 4.0 has enabled the manufacturing of customized products based on customers’ demands, which demands a high degree of flexibility in production processes, with low cost and without placing numerous test points. In this paper, we propose two automated test solutions based on machine learning and thermographic analysis. We propose deploying autoencoders and random forest in two different manners to detect firmware or hardware anomalies based on the circuit board’s temperature signature. We validate our proposal using two firmware versions running independently on the test board. We obtained an anomaly detection rate above 98%. In the random forest approach, we require all data classes for training, whereas the autoencoder only requires the reference class, which is expected in real scenarios.

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Section: Discussionmentioning

confidence: 99%

“…Automated Optical Inspection (AOI) refers to test techniques where PCB images are compared to a reference image, and dissimilarities denote possible defects [18][19]. Thermography is a similar approach to AOI, where infrared images are used to detect defects [20].…”

Section: Theorical Backgroundmentioning

confidence: 99%

Non-intrusive Embedded Systems Anomaly Detection using Thermography and Machine Learning

Oliveira¹,

Bastos-Filho²,

Oliveira³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

View full text Add to dashboard Cite

show abstract

“…For PCB's internal anatomy inspection, X-ray images are frequently employed (Chuang et al, 2010). Finally, thermography is a promising test approach where infrared images are used to detect defects (Mamchur et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Improving Autoencoder-Based Anomaly Detection in Embedded Systems using Data Transformation

Oliveira

Bastos-Filho

Oliveira

2022

Preprint

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Quality control is a critical aspect of modern electronic circuit industry. In addition to being a prerequisite for proper functioning, circuit quality is closely related to safety, security, and economics. Deep learning models have been extensively used in embedded systems testing and anomaly detection. However, performance is heavily dependent on the data available for training. Often, the number of samples or even its quality is limited. This leads to poor training process and low performance. We present a solution to improve anomaly detection in embedded systems by transforming time signals acquired from the printed circuit board under test. The proposed approach is experimentally validated in two autoencoder-based anomaly detection systems. Hence, two types of signals are analyzed: electric current and thermographic signatures. In both cases, electrical or thermographic signals are pre-processed prior to being converted into spectrogram images, which are then used to train and test the autoencoder. The achieved anomaly detection accuracy improvement for the thermographic case is 71%, compared with the raw data. For the electric current case, we show how data transformations enable autoencoder training where, using raw data, training is not feasible. In this case, we find accuracy improvements of up to 98%. The results indicate that, even in a scenario where the available data are limited, it is possible to achieve an acceptable performance using the proposed technique.

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Non-invasive embedded system hardware/firmware anomaly detection based on the electric current signature

Oliveira

Bastos-Filho

Oliveira

2022

Advanced Engineering Informatics

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Thermography investigation of soldered joints for LED mounting

Cited by 4 publications

References 2 publications

Non-intrusive Embedded Systems Anomaly Detection using Thermography and Machine Learning

Non-intrusive Embedded Systems Anomaly Detection using Thermography and Machine Learning

Improving Autoencoder-Based Anomaly Detection in Embedded Systems using Data Transformation

Non-invasive embedded system hardware/firmware anomaly detection based on the electric current signature

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