A review on deep learning applications in prognostics and health management

Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain

A Digital Twin System for the Integration of Railway Infrastructure Data.

Mechanical and Mechatronic Engineerin

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Infrastructure Design, Signalling and Security in Railway

Railway transportation has become one of the main technological advances of our society. Since the first railway used to carry coal from a mine in Shropshire (England, 1600), a lot of efforts have been made to improve this transportation concept. One of its milestones was the invention and development of the steam locomotive, but commercial rail travels became practical two hundred years later. From these first attempts, railway infrastructures, signalling and security have evolved and become more complex than those performed in its earlier stages. This book will provide readers a comprehensive technical guide, covering these topics and presenting a brief overview of selected railway systems in the world. The objective of the book is to serve as a valuable reference for students, educators, scientists, faculty members, researchers, and engineers

Artificial intelligence for advanced manufacturing quality

100 p.This Thesis addresses the challenge of AI-based image quality control systems applied to manufacturing industry, aiming to improve this field through the use of advanced techniques for data acquisition and processing, in order to obtain robust, reliable and optimal systems. This Thesis presents contributions onthe use of complex data acquisition techniques, the application and design of specialised neural networks for the defect detection, and the integration and validation of these systems in production processes. It has been developed in the context of several applied research projects that provided a practical feedback of the usefulness of the proposed computational advances as well as real life data for experimental validation

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

An analysis of human behaviour which can cause fatalities in the bus and train tunnel during a tunnel fire event

This dissertation develops the analysis of human behaviour which can cause fatalities in the Bus and Train Tunnel during a tunnel fire event. The project aim is to utilise the Root Cause Analysis framework to produce recommendations for the BaT tunnel design with respect to human behavioural fire safety. Tunnel fire safety is a young area of research. There is much ambiguity in tunnel fire science and includes many unanswered questions such as; human behaviour in relation to tunnel fire emergencies with a particular reference to tunnel operators and emergency services. The Root Cause Analysis framework was utilised to discover the underlying causes of fatality within a tunnel due to human behaviour. The framework allows for the root causes to be discovered and ensures that recommendations are produced for each event that has the potential to cause loss of life. Throughout the study publically available information surrounding the BaT tunnel was documented. A literature review was then conducted into the tunnel operations and fire safety within tunnels. Following the literature review, extensive data gathering was conducted to include statistics on historic tunnel fires and case studies that are applicable to the aims of the study. A root cause analysis was carried out pertaining to tunnel fire safety within tunnels. The root cause analysis was conducted upon a specified tunnel fire design which utilizes publically available information along with assumptions that are based on prescriptive measures. The assumed tunnel fire design root cause analysis was undertaken on both the busway and the railway. The Root Cause Analysis highlighted that both the busway and the railway had identical root causes. The causes of fatality were discovered to be due to Communication breakdowns, slow reaction times, inadequate understanding and inadequate maintenance. The ways recommended to mitigate these risks is through intensive training of all staff, educating the public through marketing and the establishment of sound management within well-defined processes. There are many limitations involved within the analysis which cause the recommendations to be incomplete. Hence, before implementation of the recommendations the study should be carried out upon complete design data. The Root cause analysis is an effective framework that could be used to find the causes of risk and failure within the BaT tunnel. The framework was effective in identifying the root causes of the defined scenario. For a more complete analysis, more scenarios should be analysed, with true design data and including the modelling of the ventilation system where possible

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data