scholarly journals Development of an automated diagnostic and inspection system based on artificial intelligence designed to eliminate risks in transport and industrial companies

2021 ◽  
Vol 55 ◽  
pp. 805-813
Author(s):  
Milan Sága ◽  
Michal Bartoš ◽  
Vladimír Bulej ◽  
Ján Stanček ◽  
Dariusz Wiecek
Keyword(s):  
Artificial Intelligence ◽  
Inspection System ◽  
Industrial Companies

Artificial intelligence-based automatic visual inspection system for built heritage

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lukman E. Mansuri ◽  
D.A. Patel
Keyword(s):  
Artificial Intelligence ◽  
Visual Inspection ◽  
Image Data ◽  
Inspection System ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Built Heritage ◽  
Automatic Visual Inspection ◽  
Inspection Systems

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

An Internet-Based Quality Control Laboratory via Integration of Remote Robotic Operation and Nondestructive Ultrasound Evaluation

2008 ◽  
Author(s):  
Richard Chiou ◽  
Vladimir Genis ◽  
Warren Rosen ◽  
Anthony Moulton ◽  
Yongjin Kwon
Keyword(s):  
Internet Access ◽  
Flaw Detector ◽  
Inspection System ◽  
Quality Inspection ◽  
Industrial Companies ◽  
Quality Control Laboratory ◽  
Ultrasound Evaluation ◽  
Increase Productivity ◽  
The One ◽  
Ethernet Connection

This paper discusses the integration of a remote robot laboratory with nondestructive ultrasound evaluation (NDE) experiments. A remotely automated quality inspection system is designed to analyze dimensions as well as detect internal flaws of parts via an Internet-based NDE system. The remote quality inspection system includes: Internet controllable robot via Ethernet connection, multiple Web-cameras, Ultrasonic Automatic Flaw Detector, LabVIEW module, and computers with Internet access capable of remote connection. The uniqueness of the project lies in making this process Internet-based and remote robot operated. An Internet-based procedure such as the one we are developing will allow industrial companies involved in NDE procedures to increase productivity and profits by allowing an employee to monitor multiple operations over the Internet without having to be at a specified location. In addition, the utilization of remotely controlled robots for educational purposes is expected to increase the degree of immersive presence of the students engaging in such Internet-based laboratory exercises as well as the level of online interactivity between the faculty and students.

scholarly journals Development and Application of a Multi-Objective Tool for Thermal Design of Heat Exchangers Using Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1120
Author(s):  
José Luis de Andrés Honrubia ◽  
José Gaviria de la Puerta ◽  
Fernando Cortés ◽  
Urko Aguirre-Larracoechea ◽  
Aitor Goti ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Pressure Drop ◽  
Heat Exchangers ◽  
Thermal Design ◽  
Design Parameters ◽  
User Friendliness ◽  
Multi Objective ◽  
Industrial Companies ◽  
Objective Tool

This paper presents the design of a multi-objective tool for sizing shell and tube heat exchangers (STHX), developed under a University/Industry collaboration. This work aims to show the feasibility of implementing artificial intelligence tools during the design of Heat Exchangers in industry. The design of STHX optimisation tools using artificial intelligence algorithms is a visited topic in the literature, nevertheless, the degree of implementation of this concept is uncommon in industrial companies. Thus, the challenge of this research consists of the development of a tool for the design of STHX using artificial intelligence algorithms that can be used by industrial companies. The approach is implemented using a simulated dataset contrasted with ARA TT, the company taking part in the project. The given dataset to develop a theoretical STHX calculator was modeled using MATLAB. This dataset was used to train seven neural networks (NNs). Three of them were mono-objective, one per objective to predict, and four were multi-objective. The last multi-objective NN was used to develop an inverse neural network (INN), which is used to find the optimal configuration of the STHXs. In this specific case, three design parameters, the pressure drop on the shell side, the pressure drop on the tube side and heat transfer rate, were jointly and successfully optimised. As a conclusion, this work proves that the developed tool is valid in both terms of effectiveness and user-friendliness for companies like ARA TT to improve their business activity.

Low-Power Drone-Mountable Real-Time Artificial Intelligence Framework for Road Asset Classification

2020 ◽  
pp. 036119812096517
Author(s):  
Shrey Mohan ◽  
Omidreza Shoghli ◽  
Adrian Burde ◽  
Hamed Tabkhi
Keyword(s):  
Artificial Intelligence ◽  
Low Power ◽  
Real Time ◽  
Total Power ◽  
Inspection System ◽  
Highway Traffic ◽  
Labor Costs ◽  
Continuous Increase ◽  
Embedded Platforms ◽  
Road Inspection

With the continuous increase in interstate highway traffic and demand for higher safety standards, there is a growing need for rapidly scalable road inspection. Currently, inspection and condition assessment of roadways involve manual operations which increase labor costs and limit the scalability and inspection coverage. Furthermore, manually inspecting highways adds additional safety risks for highway workers and road inspectors. To address these challenges, we envision a fully automated process of highway inspection. This paper presents a novel low-power drone-mountable real-time artificial intelligence (AI) framework for road asset classification through visual sensing, which is the first step toward a fully automated inspection system. We analyzed a state DOT dataset, consisting of 14 different kinds of defected road assets. To this end, we developed our baseline framework using MobileNet-V2, which is a convolutional neural network (CNN) specially developed for mobile and embedded platforms. Since our target dataset was small and CNNs networks require a huge amount of data, we leveraged transfer learning, by pretraining MobileNet-V2 using the ImageNet dataset and then fine-tuned it on our target dataset. This new framework was ported to embedded platforms Nvidia Jetson Nano with the capability to perform on-board drone processing. Overall, our results demonstrate 81.33% accuracy on the test set while processing 7.4 frames per second and occupying a total power of 1.9 W. It achieved a Power Reduction Factor (PRF) of 21.17 over Nvidia TitanV implementation, with only 8.74% impact on the projected drone flight time.

scholarly journals Digital Business Model Innovation: Empirical insights into the drivers and value of Artificial Intelligence

2021 ◽  
Vol 21 ◽  
pp. 63-75
Author(s):  
Johannes Winter
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Service Sector ◽  
Business Models ◽  
Business Model Innovation ◽  
Business Success ◽  
Successful Implementation ◽  
Related Services ◽  
Industrial Companies ◽  
Order Books

The business activities of traditional industrial companies have commonly focused on products and product-related services. Digital pioneers have evolved their offerings into product-service systems that are networked, intelligent, personalized, and adaptable. The speed at which business models must change continues to be underestimated by many market participants, especially when order books are well filled and the pressure to change appears to be low. Industrial and service companies need to adapt to the changes induced by new market players better today than tomorrow to secure future business success and remain competitive in the digital age. The aim of this article is to intensify the debate on digital business model innovation in industry and the service sector and to enrich it with practical examples of the successful implementation of artificial intelligence in products and services.

scholarly journals Collaborative Research and Innovation in Europe

2020 ◽  
Author(s):  
Hendro Wicaksono
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Management ◽  
Collaborative Research ◽  
Best Practice ◽  
Research Organization ◽  
Industrial Companies ◽  
Research And Innovation ◽  
Research Organizations ◽  
German Research

The presentation gives an overview of best practice collaborations of higher education and industry in Germany. It then describes different research organization and their roles in the German research landscape. Finally, the presentation shows some project examples in the area of artificial intelligence and data management which are funded by German and EU research agencies and involve collaboration between universities, research organizations, industrial companies, and municipalities.

Production Strategies of Companies in Machine Manufacturing Industry

2014 ◽  
pp. 240-297
Author(s):  
Vladimir Modrak ◽  
Sorin Mihai Radu
Keyword(s):  
Artificial Intelligence ◽  
Development Strategy ◽  
Manufacturing Industry ◽  
Central Place ◽  
Strategic Decisions ◽  
Industrial Companies ◽  
Regional Development Strategy ◽  
Production Strategies ◽  
Production Strategy ◽  
Advanced Production

In the portfolio of strategies of any industrial company, the production strategy occupies a central place. Preparing it is based on knowing well the technological process and its complexity. This chapter particularly studies the production strategies of the Romanian companies in the machine manufacturing industry (industrial machineries and equipment). It is recommended that the preparation of such a strategy would take into account the regional development strategy. In this context, the Advanced Production Strategies (APS) may successfully be used, which prefigure the transition to the machinist systems and then to systems based on artificial intelligence where an important role in preparing the production strategies is held by the systems based on artificial intelligence. The problem of elaborating some optimal strategic decisions is dealt with separately by using econometric models. In this context, the use of IT, respectively of expert systems, is essential in developing some very good strategies for industrial companies.

Sign in / Sign up

Export Citation Format

Share Document