Robots, Drones, UAVs and UGVs for Operation and Maintenance: 1st Edition (Hardback) book cover

Robots, Drones, UAVs and UGVs for Operation and Maintenance

1st Edition

By Diego Galar, Uday Kumar, Dammika Seneviratne

CRC Press

432 pages | 212 B/W Illus.

Purchasing Options:$ = USD
Hardback: 9781138322110
pub: 2020-03-17
Available for pre-order. Item will ship after 17th March 2020

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Industrial assets (such as railway lines, roads, pipelines) are usually huge, span long distances, and can be divided into clusters or segments that provide different levels of functionality, subject to different loads, degradations and environmental conditions, and their efficient management is necessary. The aim of the book is to give comprehensive understanding about the use of autonomous vehicles (context of robotics) for the utilization of inspection and maintenance activities in industrial asset management, in different accessibility and hazard levels. The usability of deploying inspection vehicles in an autonomous manner is explained with the emphasis on integrating the total process.

Table of Contents

Chapter 1 Introduction

1.1 Autonomous Vehicles

1.2 Industrial Assets

1.3 Inspection of Industrial Assets

1.4 Maintenance of Industrial Assets


Chapter 2 Development of Autonomous Vehicles

2.1 History of development of Autonomous robots

2.2 Dynamics and Machine Architectures

2.3 Robots and Machine Intelligence

2.4 Programming of Autonomous Robots

2.5 Adaptive Algorithms and Utilization

Chapter 3 Distant Inspection Operations for industrial Assets

3.1 Autonomous Vehicle Inspection Platform

3.2 Inspection Communications and Transport Security

3.3 Obstacle Avoidance

3.4 Inspection Modes and Content

3.5 Inspection Methods


Chapter 4 Sensors for Autonomous Vehicles in Infrastructure Inspection Applications

4.1 Sensors and sensing strategies

4.2 Sensor types: introduction

4.3 Sensors for military missions

4.4 Sensor-based localization and mapping

4.5 Sensor fusion, sensor platforms and Global Positioning System


Chapter 5 Data acquisition and intelligent diagnosis

3.1 Data acquisition principle and process for laser scanning, visual imaging, infrared imaging, UV image

5.2 Cloud data post-processing technology

5.3 Cloud data intelligent diagnosis


Chapter 6 Inspection expert diagnosis and three-dimensional visualization

6.1 Overview

6.2 Line security diagnosis for multi-source data fusion

6.3 Three-dimensonal visualization applications


Chapter 7 Communications

7.1 Communication Methods

7.2 Radio Communication

7.3 Mid-Air Collision (MAC) Avoidance

7.4 Communications Data Rate and Bandwidth Usage

7.5 Antenna Types

7.6 Tracking with Multiple Autonomous Vehicles


Chapter 8 Autonomous vehicles for infrastructure inspection applications

8.1 Power Line Inspection

8.2 Building Monitoring

8.3 Railway Infrastructure Inspection

8.4 Waterways and Other Infrastructures


Chapter 9 Critical Failure Detection Application In Autonomous Vehicles

9.1 Repeated Inspections and Failure Identification

9.2 Autonomous Vehicle Emergency Inspection Applications

9.3 Autonomous Vehicle Navigation Security


Chapter 10 Autonomous inspection and maintenance with artificial intelligent infiltration

10.1 Artificial Intelligent Techniques Used in AVs

10.2 Artificial Intelligent Approaches for Inspection and Maintenance

10.3 Current developments of AVs with AI


Chapter 11 Big Data and Analytics for AV Inspection and maintenance

11.1 Big Data Analytics and Cyber-Physical Systems

11.2 Big Data Analytics in Inspection and Maintenance

11.3 Integration of Big Data Analytics in AV Inspection and Maintenance

11.4 Utilization of AVs in Industry 4.0 Environment


About the Authors

Dr. Diego Galar is Full Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Luleå University of Technology where he is coordinating several H2020 projects related to different aspects of cyber physical systems, Industry 4.0, IoT or Industrial AI and Big Data. He was also involved in the SKF UTC centre located in Lulea focused on SMART bearings and also actively involved in national projects with the Swedish industry or funded by Swedish national agencies like Vinnova.

He is also principal researcher in Tecnalia (Spain), heading the Maintenance and Reliability research group within the Division of Industry and Transport.

He has authored more than five hundred journal and conference papers, books and technical reports in the field of maintenance, working also as member of editorial boards, scientific committees and chairing international journals and conferences and actively participating in national and international committees for standardization and R&D in the topics of reliability and maintenance.

In the international arena, he has been visiting Professor in the Polytechnic of Braganza (Portugal), University of Valencia and NIU (USA) and the Universidad Pontificia Católica de Chile. Currently, he is visiting professor in University of Sunderland (UK), University of Maryland (USA), and Chongqing University in China.

Uday Kumar, the Chaired Professor of Operation and Maintenance Engineering is Director of Luleå Railway Research Center and Scientific Director of the Strategic Area of Research and Innovation- Sustainable Transport at Luleå University of Technology, Luleå, Sweden. Before joining Luleå University of Technology, Dr. Kumar was Professor of Offshore Technology (Operation and Maintenance Engineering) at Stavanger University, Norway. Professor Kumar has research interest in the subject area of Reliability and Maintainability Engineering, Maintenance modelling, Condition Monitoring, LCC & Risk analysis etc. He has published more than 300 papers in International Journals and peer reviewed Conferences and has made contributions to many edited books. He has supervised more than 25 PhD Theses related to the area of reliability and maintenance. Prof Kumar has been a keynote and invited speaker at numerous congresses, conferences, seminars, industrial forums, workshops and academic Institutions. He is an elected member of the Swedish Royal Academy of Engineering Sciences.

Dammika Seneviratne currently works as Post-doctoral researcher in the Division of Operation and Maintenance – Luleå University of Technology, Luleå, Sweden and senior researcher in Tecnalia, Spain. He holds a B.Sc. degree in Mechanical Engineering from the University of Peradeniya, Sri Lanka, specialized in Production engineering. He received his M.Sc. degree in Mechatronics Engineering from the Asian Institute of Technology, Thailand. After working for a number of years as a Mechanical Maintenance Engineer in various organizations he attained a PhD degree in Offshore Technology from the University of Stavanger. His research interests include condition monitoring, operation and maintenance engineering in railway systems; risk based inspection planning in offshore oil and gas facilities; reliability and risk analysis and managements, and risk based maintenance.

About the Series

ICT in Asset Management

Industry 4.0 is a term that describes the fourth generation of industrial activity which is enabled by smart systems and Internet-based solutions. Two of the characteristic features of Industry 4.0 are computerization by utilizing cyber-physical systems and intelligent factories that are based on the concept of "internet of things". Maintenance is one of the application areas, referred to as maintenance 4.0, in form of self-learning and smart systems that predicts failure, makes diagnosis and triggers maintenance.

Indeed, assets are complex mixes of complex systems. Each system is built from components which, over time, may fail. When a component does fail, it is difficult to identify because the effects or problems that the failure has on the system are often neither obvious in terms of their source, nor unique. Previously the diagnosis of problems occurring in systems has been performed by experienced personnel with in-depth training and experience. Computer-based systems are now being used to automatically diagnose problems to overcome some of the disadvantages associated with relying on experienced personnel.

There is a clear need to be able to quickly and efficiently determine the cause of failures and propose optimum maintenance decisions, while minimizing the need for human intervention, but the above approaches either take a considerable amount of time before failures are diagnosed, or provide less than reliable results, or are unable to work well in complex systems. They also rely on a limited range of technical data regarding the system, component and the failure modes. The focus must be widened to properly reflect the complexity of the decision making situation, which relies on extensive knowledge about the asset.

Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the whole system. Therefore the integration of asset information is required to get an accurate health assessment of the whole system, i.e. infrastructure, factories; facilities, vehicles etc.., and determine the probability of a shutdown or slowdown. Moreover, the data collected are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. In this scenario, ICT play a key role from sensor level to decision making process supporting operators and maintainers in the daily work. Technologies like sensor networks, sensor fusion, cloud computing, data mining, big data analytics and others increase their relevance in this domain due to the need of data handling and become the real trigger for the fourth industrial revolution.

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Subject Categories

BISAC Subject Codes/Headings:
TECHNOLOGY & ENGINEERING / Industrial Engineering
TECHNOLOGY & ENGINEERING / Operations Research