Integrated Video and Acoustic Emission Data Fusion for Intelligent Decision Making in Material Surface Inspection System

In the field of intelligent surface inspection systems, particular attention is paid to decision making problems, based on data from different sensors. The combination of such data helps to make an intelligent decision. In this research, an approach to intelligent decision making based on a data int...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-11, Vol.22 (21), p.8554
Main Authors: Chernov, Andrey V., Savvas, Ilias K., Alexandrov, Alexander A., Kartashov, Oleg O., Polyanichenko, Dmitry S., Butakova, Maria A., Soldatov, Alexander V.
Format: Article
Language:English
Subjects:
Accuracy
Acoustic emission
acoustic emission control
Acoustic emission testing
Acoustics
Artificial intelligence
Classification
Computer vision
Data acquisition
data fusion
Data integration
Data processing
Datasets
Decision making
Deep learning
Defects
Digital computers
Digital signal processors
Inspection
intelligent system
Kurtosis
Machine learning
Machine vision
Multisensor fusion
Neural networks
Nondestructive testing
Pipe joints
Quality control equipment
Semiconductor industry
Sensors
Signal processing
surface inspection
Wavelet transforms
Welded joints
Welding
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Summary:In the field of intelligent surface inspection systems, particular attention is paid to decision making problems, based on data from different sensors. The combination of such data helps to make an intelligent decision. In this research, an approach to intelligent decision making based on a data integration strategy to raise awareness of a controlled object is used. In the following article, this approach is considered in the context of reasonable decisions when detecting defects on the surface of welds that arise after the metal pipe welding processes. The main data types were RGB, RGB-D images, and acoustic emission signals. The fusion of such multimodality data, which mimics the eyes and ears of an experienced person through computer vision and digital signal processing, provides more concrete and meaningful information for intelligent decision making. The main results of this study include an overview of the architecture of the system with a detailed description of its parts, methods for acquiring data from various sensors, pseudocodes for data processing algorithms, and an approach to data fusion meant to improve the efficiency of decision making in detecting defects on the surface of various materials.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22218554