Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Development of a Methodology for Evaluating and Anticipating Improvised Explosive Device Threat Activity Using a Fault Tree Based Process

This document is a redacted version of the original dissertation titled \u27Development of a Methodology for Evaluating and Anticipating Improvised Explosive Device Threat Activity Using a Fault Tree Based Process.\u27 To allow for publication, information was removed which was considered sensitive in nature or which could be used by those who employ the Improvised Explosive Device, to negate any advantage gained by this research. The complete un-redacted dissertation is available (with proper vetting) to those whishing to further develop the concepts outlined in this document. Those interested in obtaining access to the complete document should contact the Joint IED Defeat Organization (JIEDDO). To date there is little published evidence to believe that a sufficient IED threat prediction capability has been developed. Most of the countermeasures seen on the battlefield today are reactive in nature designed to neutralize the effects of a device before it causes injury to military and civilian personnel. These countermeasures have meet with varying levels of success. An efficient threat prediction capability will significantly increase the ability of military forces to eliminate the threat associated with the IED. The lack of an accurate threat prediction capability is a possible result of not having identified all of the variables or the variable relationships associated with IED placement. This research analyzes the variables associated with an IED incident and develops an IED threat prediction process using the Fault Tree model. This dissertation also explores the use of visualization software to determine their suitability in C-IED operations. Furthermore, the application of a Fault Tree based process as a decision support tool for use by decision makers involved in C-IED operations is analyzed. This research is conducted in three phases with the first phase dedicated to the development of a Fault Tree diagram representing an IED incident. During this phase a complete Fault Tree is constructed identifying, sequencing, and establishing relationships between all variable associated with a successful IED attack against a military vehicle operating on a road. The second phase outlines the development of a complete process intended to serve as an operational guide for those attempting to employ the concepts addressed. To ensure a more precise understanding of the required procedures, a theoretical case study was used to articulate and demonstrate the requisite activities. Through this research, events were identified as required for an effective attack to take place. Through the integration of the Fault Tree, probability information and visualization assets a threat prediction capability is demonstrated. The ability to predict IED activity will provide military personnel a distinct advantage in defeating the IED threat and directly contribute to the increased safety of military and civilian personnel living and operating in an IED environment

A spatio-temporal modelling and analysis of digital sensor data for underground mine health and safety

A Research Report submitted to the Faculty of Science, University of the Witwatersrand, in partial fulfilment of the requirements of the degree of Master of Science 2017Health and safety of employees within their work environment is critical. In the mining industry and especially in underground mines, monitoring and management of health and safety of employees is particularly important Most underground mines today are not fully mechanized, except for coal mines. The industry thus still relies on and employs human personnel. Monitoring and managing these mines and hence personnel health and safety as they undertake their trade is therefore a necessity. Implementation of technology, especially in digital sensor systems and real-time spatial analysis systems, provides a means by which health and safety risk factors can be monitored and information gathered to facilitate determination of prevailing risks or prediction of such risks. Technology therefore can be used to make better decisions and implement specialized emergency response to avert or reduce the extent of injuries, casualties and damages in an underground mine. This research project looks into determination of prominent risk factors in an underground mine, determination of parameters for modeling of such risk factors and the implementation of ESRI’s ArcGIS platform for the retrieval and analysis of streaming sensor data about this parameter from an underground mine. A proof of concept (POC) system is developed that analyses streaming digital sensor data and determines the status of the underground mine environment. The results from this analysis are displayed in a dashboard application for a control room environment. The results and achievements of this research project, especially from a dashboard system perspective, show the possibilities of an integrated GIS-based solution for real-time data processing and determination of the prevailing conditions in an underground mine. This solution also opens up a wide pool of possibilities through which systems integration and its benefits can be achieved, especially in underground mines and focusing on health and safety, as previously silo systems can be integrated at data levels, enabling data sharing, analysis, predictions and making of informed decisions.MT201

Sustainable Approaches for Highway Runoff Management During Construction and Operation

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Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Sustainable Approaches for Highway Runoff Management During Construction and Operation

Paper V and paper VI have not been published yet.Environmentally friendly approaches for highway runoff management during construction and operation are considered in this project. First, the state of the art in runoff management in terms of characterization, treatment, and modeling approaches were surveyed, and knowledge gaps were identified. Then, the characterization and treatment of tunneling wastewater (by natural and chemical coagulants) was investigated. In the next stage, the vulnerability of water quality to road construction activities was investigated by analyzing field monitoring data. In addition, two different approaches, involving information theory and gamma test theory, were suggested to optimize the water quality monitoring network during road construction. Lastly, the application of satellite data (i.e., Sentinel-2 Multi-Spectral Imager satellite imagery products) for water quality monitoring was examined. Based on the results, it can be shown that site-specific parameters (e.g., climate, traffic load) cause spatiotemporal variation in the characterization of highway runoff and performance of best management practices (BMP) to protect water quality. There is a knowledge gap regarding the characterization of highway runoff under different climatic scenarios, as well as the continuous monitoring and assessment of roadside water bodies. Analysis of the field monitoring data indicates that blasting, area cleaning, and construction of water management measures have the highest impact on surface water quality during road construction. Additionally, the application of information theory and gamma test theory indicate that the primary monitoring network assessed here is not optimally designed. The number and spatial distribution of monitoring stations could be modified and reduced, as the construction activities vary over time. Additionally, the suggested remote sensing techniques applied in this project are able to estimate water quality parameters (i.e., turbidity and chlorophyll-a) in roadside water bodies with a reliability consistent with field observations, reflecting the spatiotemporal effects of road construction and operations on water quality. Finally, an efficient two-step treatment strategy (15 min sedimentation followed by chemical coagulation and 45 min sedimentation) is suggested for the treatment of tunneling wastewater. The optimum coagulant dosages in the jar test exhibit high treatment efficiency (92-99%) for both turbidity and suspended solids (SS), especially for particle removal in the range of 10-100 μm, which is hard to remove by sedimentation ponds and may pose serious threats to the aquatic ecosystem. It is hoped the knowledge generated by this project will help decision-makers with management strategies and support UN Sustainable Development Goals (SDGs). The proposed approaches directly contribute to managing highway runoff and achieving SDG 6 (clean water and sanitation) and especially target 6.3 (water quality).publishedVersio

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

Effects of Slope Geometry Alterations on Rockfall Mitigation along Highway Rock Cut Slopes in West Virginia

This report presents the findings of an analysis of current highway rock cut slope design practices in West Virginia, in terms of rockfall mitigation, using the rockfall simulation computer software Colorado Rockfall Simulation Program Version 4.0. Additionally, this report presents the results of two case studies, conducted on highway rock cut slopes constructed in West Virginia, to determine the feasibility of reducing the number of geotechnical benches currently used in cut slope design and construction while still safely retaining rockfall and remaining structurally stable.;Two case studies were conducted on as-built rock cut slopes in West Virginia. The objective of the case studies was to determine if any amount of geotechnical benches could be removed from the current design and construction practices put forth by the WVDOT in an effort to reduce excavation and maintenance costs while maintaining structural stability and adequate rockfall retention. In addition to CRSP, a numerical modeling software (SoilVision SVSlope RTM) was used to determine the overall Factor of Safety of the slope section.;The first case study slope consisted mostly of hard, competent bedrock (limestone and sandstone), and initially had five benches. After modeling, it was found to have a Factor of Safety of 3.63, and a lowest on-slope rockfall retention of 75%. After three bench reduction trials, the final slope had one bench, a slope stability Factor of Safety of 1.47 and a lowest on-slope rockfall retention of 88%. The reduction in excavation for this slope section after removing four benches was 3670 ft2 per foot of slope length. The second case study slope was composed of interbedded layers of softer, more friable bedrock (siltstone and coal) and hard bedrock (limestone). The initial as-built slope had 6 benches, and was found to have a slope stability Factor of Safety of 1.26 and an on-slope rockfall retention of 92%. After four bench reduction trials, the final slope had two benches, a slope stability Factor of Safety of 1.48 and an on-slope rockfall retention of 88%. The reduction in excavation for this slope section after removing four benches was 4600 ft2 per foot of slope length. The results of the case study analyses showed that, with adequate bench widths and rockfall catchment ditches, backslope heights can be increased from the WVDOT-recommended 50 to 60 feet high to heights over 100 feet, while still retaining a safe amount of rockfall

Distributed localized contextual event reasoning under uncertainty

We focus on Internet of Things (IoT) environments where sensing and computing devices (nodes) are responsible to observe, reason, report and react to a specific phenomenon. Each node captures context from data streams and reasons on the presence of an event. We propose a distributed predictive analytics scheme for localized context reasoning under uncertainty. Such reasoning is achieved through a contextualized, knowledge-driven clustering process, where the clusters of nodes are formed according to their belief on the presence of the phenomenon. Each cluster enhances its localized opinion about the presence of an event through consensus realized under the principles of Fuzzy Logic (FL). The proposed FLdriven consensus process is further enhanced with semantics adopting Type-2 Fuzzy Sets to handle the uncertainty related to the identification of an event. We provide a comprehensive experimental evaluation and comparison assessment with other schemes over real data and report on the benefits stemmed from its adoption in IoT environments