Real-time fault detection in manufacturing environments using face recognition techniques

Megahed, Fadel M.; Camelio, Jaime A.

doi:10.1007/s10845-010-0378-3

Cited by 47 publications

(12 citation statements)

References 36 publications

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“…This reduces the computational time involved in image processing step, which may be important if 100% sampling is to be used. Megahed and Camelio provided a more detailed discussion on some of the practical considerations in using MVSs for quality purposes.…”

Section: A Practitioner's Guide For Implementing the Proposed Methodsmentioning

confidence: 99%

“…neighboring pixels typically have similar intensity values) in the image, and as a result, the image can be resized without any significant information loss, but with a substantial gain in computational efficiency. Megahed and Camelio provided a detailed discussion on the effect of part detection and image compression on the computational speed in a quality monitoring framework.…”

Section: A Practitioner's Guide For Implementing the Proposed Methodsmentioning

confidence: 99%

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A Spatiotemporal Method for the Monitoring of Image Data

Megahed

Wells

Camelio

et al. 2012

Quality & Reliability Eng

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Machine vision systems are increasingly being used in industrial applications because of their ability to quickly provide information on product geometry, surface defects, surface finish, and other product and process characteristics. Previous research for monitoring these visual characteristics using image data has focused on either detecting changes within an image or between images. Extending these methods to include both the spatial and the temporal aspects of image data would provide more detailed diagnostic information, which would be of great value to industrial practitioners. Therefore, in this article, we show how image data can be monitored using a spatiotemporal framework that is based on an extension of a generalized likelihood ratio control chart. The performance of the proposed method is evaluated through computer simulations and experimental studies. The results show that our proposed spatiotemporal method is capable of quickly detecting the emergence of a fault. The computer simulations also show that our proposed generalized likelihood ratio control charting method provides a good estimate of the change point and the size/location of the fault, which are important fault diagnostic metrics that are not typically provided in the image monitoring literature. Finally, we highlight some research opportunities and provide some advice to practitioners.

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Section: A Practitioner's Guide For Implementing the Proposed Methodsmentioning

confidence: 99%

Section: A Practitioner's Guide For Implementing the Proposed Methodsmentioning

confidence: 99%

A Spatiotemporal Method for the Monitoring of Image Data

Megahed

Wells

Camelio

et al. 2012

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…For the image processing step will be reduced. Readers are referred to Megahed and Camelio for more information about the practical considerations in using MVS for quality purposes. Determine appropriate values for the factors used: Before using the proposed approach, it is necessary to decide on the appropriate values of the parameters. This involves determining the significance level α , the appropriate dimensions of the image, the smallest size of the fault that is intended to be detected, and the number of in‐control images for parameter estimation in phase I. Image capturing and preprocessing: This step involves capturing an appropriate image of the flawless product, which is called the nominal image.…”

Section: Guidelines For Practitionersmentioning

confidence: 99%

A novel framework for spatiotemporal monitoring and post‐signal diagnosis of processes with image data

Amirkhani

Amiri

2020

Quality & Reliability Eng

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Advances in digital equipment and organizations' interest in having comprehensive and real‐time information about products have increased the use of machine vision systems in organizations. In this paper, to monitor a sensory quality characteristic of a product based on images, the residual matrix of the intensity values of the nominal and captured images is divided into specific regions; then, the equality of the means of the regions is tested based on one‐way ANOVA. To do so, a P‐value–based control chart is applied to detect the out‐of‐control state as soon as possible. If an out‐of‐control alarm is received, Dunnett's test is used to identify region(s) with significant differences in the means of residuals (defective location[s] in the image) compared with other regions. After the locations of defective regions are identified, the change point of the process is estimated by using the maximum likelihood estimation approach. The performance of the proposed procedure is compared with some of the previous approaches in the literature. Then, the proposed procedure is implemented in a real‐world case. The simulation study demonstrates the merits of the proposed procedure: It is not limited to specific geometric types of faults; it has the ability to detect multiple faults in the image; it takes less computational time; and it performs well in estimating the real time of change, as well as the location(s) and dimension(s) of the fault(s).

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“…There is an extended interest in automated inspection systems that stems from the need to inspect 100% of produced parts and avoid statistical sampling approach. One of the commonly applied inspection systems is image based [16]. However, image-based approaches lack 3D representation and associated dimensional inspection capability.…”

Section: Related Workmentioning

confidence: 99%

Anomaly detection in Skin Model Shapes using machine learning classifiers

Yacob

Semere

Nordgren

2019

Int J Adv Manuf Technol

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The concept of Skin Model Shapes has been proposed as a method to generate digital twins of manufactured parts and is a new paradigm in the design and manufacturing industry. Skin Model Shapes use discrete surface representation schemes, such as meshes and point clouds, to represent surfaces, which makes them enablers to perform an accurate tolerance analysis and surface inspection. However, online inspection of manufactured parts through use of Skin Model Shapes has not been extensively studied. Moreover, the existing geometric variation inspection techniques do not detect unfamiliar changes within tolerance, which could be the precursors to the onset of the manufacturing of out of tolerance part. To detect the unfamiliar changes, as anomalies, and categorize them as systematic and random variations, some unique surface characteristics can be extracted and studied. Random surface deviations exhibit narrow normal distributions, and systematic deviations, on the other hand, exhibit wide, skewed, and multimodal distributions. Using those surface characteristics as key traits, machine learning classifiers can be used to classify deviations into systematic and random variations. To illustrate the method, multiple samples from a truck component manufacturing line were scanned and the collected 3D point cloud data was used to extract features. A prediction score of 97-100% can be achieved by decision tree, k-nearest neighbor, support vector machines, and ensemble classifiers. The purposed approach is expected to extend the existing online inspection approaches and applications of Skin Model Shapes in quality control.

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Real-time fault detection in manufacturing environments using face recognition techniques

Cited by 47 publications

References 36 publications

A Spatiotemporal Method for the Monitoring of Image Data

A Spatiotemporal Method for the Monitoring of Image Data

A novel framework for spatiotemporal monitoring and post‐signal diagnosis of processes with image data

Anomaly detection in Skin Model Shapes using machine learning classifiers

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