An integration method using distributed optical fiber sensor and Auto-Encoder based deep learning for detecting sulfurized rust self-heating of crude oil tanks

Zhu, Zhichao; Chu, Cheng–Wei; Bian, Haitao; Jiang, Juncheng

doi:10.1016/j.jlp.2021.104623

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…In [ 135 ], a DL model based on a distributed optical fibre sensor (DOFS) was proposed to collect data concerning the temperature data along the optic fibre and identify the anomaly detected temperature in the early phase. The proposed model had the potential to be used for monitoring abnormal temperatures in crude oil tanks.…”

Section: DL Applications For Optical Sensorsmentioning

confidence: 99%

“…The studies concluded that the model with 600 × 200 × 100 × 200 × 600 achieved the best result with an F1-score of 99.9% and an area under the curve (AUC) of 1.00 when a window size of 36,000 was used. In [135], a DL model based on a distributed optical fibre sensor (DOFS) was proposed to collect data concerning the temperature data along the optic fibre and identify the anomaly detected temperature in the early phase. The proposed model had the potential to be used for monitoring abnormal temperatures in crude oil tanks.…”

Section: Autoencoder (Ae)-based Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Optical Sensor Applications: A Review

Al‐Ashwal

Soufy

Hamza

et al. 2023

Sensors

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Over the past decade, deep learning (DL) has been applied in a large number of optical sensors applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as a promising technology for modern intelligent sensing platforms. These sensors are widely used in process monitoring, quality prediction, pollution, defence, security, and many other applications. However, they suffer major challenges such as the large generated datasets and low processing speeds for these data, including the high cost of these sensors. These challenges can be mitigated by integrating DL systems with optical sensor technologies. This paper presents recent studies integrating DL algorithms with optical sensor applications. This paper also highlights several directions for DL algorithms that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future development of related research.

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Section: DL Applications For Optical Sensorsmentioning

confidence: 99%

Section: Autoencoder (Ae)-based Applicationsmentioning

confidence: 99%

Deep Learning for Optical Sensor Applications: A Review

Al‐Ashwal

Soufy

Hamza

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The studies concluded that the model with 600×200×100×200×600 achieved the best result with F1-score of 99.9% and AUC of 1.00 when a window size of 36,000 was used. In [136], DL model based on distributed optical fiber sensor (DOFS) was proposed to collect temperature data along the optic fiber and figure out the anomaly detected temperature at the early phase. The proposed model had the potential to be used for monitoring abnormal temperatures in crude oil tanks.…”

Section: Autoencoder (Ae)-based Applicationsmentioning

confidence: 99%

“…20 Collect the temperature data along the optic fiber set and figure out the anomaly detected temperature at the early phase. 98% [136] 21 detection of the defects on large-sized PCBs and measure their copper thickness before the mass production process [132] of ambient temperature changes on the detection of sulfurized rust self-heating anomalies. This method did not consider diverse weather conditions such as strong winds, rainfall, and temperature variations resulting from seasonal changes or diurnal fluctuations.…”

Section: 9% [135]mentioning

confidence: 99%

Deep Learning for Optical Sensor Applications: An Insight Review

Al‐Ashwal¹,

Soufy²,

Hamza³

et al. 2023

Preprint

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Over the past decade, Deep Learning (DL) had been applied in a large number of optical sensors’ applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as promise technology for the modern intelligent sensing platforms. These sensors are widely used to process monitoring, quality prediction, pollution, defense, security and many other applications. However, they suffer major challenges such as the large generated data and low processing speed for that data and moreover the much cost of these sensor. These challenges can be mitigated by integrating deep learning system with the optical sensor technologies. This paper presents recent studies that integrate DL algorithms with optical sensors applications. This paper also highlights several of DL algorithms directions that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future related research development.

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“…Distributed optical fiber sensing technology is a sensing technology that obtains the distribution information of the measured physical quantity in the sensing optical fiber area that changes with time and space according to the distribution parameters of light waves along the line. This sensing technology has the characteristics of high precision, electrical insulation, high spatial resolution, and corrosion resistance, which makes it suitable for large-scale structural strain temperature monitoring [1][2][3], seismic wave monitoring [4], sound monitoring [5], and other fields. In addition, the optical fiber is light in weight and soft in texture, so the distributed optical fiber can also be used for multi-point intensive measurement of precision components, such as silicon wafer warpage measurement, IGBT chip temperature monitoring, etc.…”

Section: Introductionmentioning

confidence: 99%

Coordinate Extraction of Measuring Points for Sensing Optical Fibers on Wafer Surface

Zhang

Zhu

2022

J. Phys.: Conf. Ser.

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In order to solve the problem of difficulty in extracting the coordinates of actual measuring points in the detection of precision components by distributed sensing optical fiber, a method of extracting the coordinates of optical fiber measuring points based on machine vision is proposed. Firstly, a strip light source should be applied to side lighting to avoid the specular reflection of the wafer. Take pictures from four directions of 0°, 45°, 90°, and 135°, and then superimpose them into a completely original image. And this image is preprocessed by image contrast enhancement and bilateral filtering. Secondly, the Canny algorithm is adopted for sub-pixel edge recognition of optical fiber images. The optical fiber is extracted segment by segment by using the Gaussian line detection method and obtaining the full optical fiber path. Finally, the coordinates of the measuring points along the sensing optical fibers can be determined under different spatial resolutions of the distributed optical fiber system.

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An integration method using distributed optical fiber sensor and Auto-Encoder based deep learning for detecting sulfurized rust self-heating of crude oil tanks

Cited by 12 publications

References 24 publications

Deep Learning for Optical Sensor Applications: A Review

Deep Learning for Optical Sensor Applications: A Review

Deep Learning for Optical Sensor Applications: An Insight Review

Coordinate Extraction of Measuring Points for Sensing Optical Fibers on Wafer Surface

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