Analysis of infrared optical polishing effluents and reduction of COD and TSS levels by ultrafiltration and coagulation/flocculation

Samples of polishing effluent produced during infrared optics manufacture were analyzed. Their particle size, composition, Zeta potential, chemical oxygen demand (COD), total suspended solids (TSS), and settleable solids were determined. Feasibility of treatment methods such as ultrafiltration (UF) and coagulation/flocculation was investigated to reduce both COD and TSS. It was found that effluents consisted of a suspension of micro- and nanoparticles. Effluent particle size distribution reflected the removal rate of the originating polishing process. Their composition was primarily germanium and other polished substrates as well as polishing abrasives. The effluent Zeta potential was highly negative and prevented particle settling. COD of all specimens was very high, which prevented sewage discharge. Laboratory-scale trials using UF showed substantial COD abatement of up to 74.1%. TSS was reduced to zero after UF. Comparable coagulation/flocculation COD abatement was demonstrated for the highest COD sample

Data fusion strategy for precise vehicle location for intelligent self-aware maintenance systems

Abstract— Nowadays careful measurement applications are handed over to Wired and Wireless Sensor Network. Taking the scenario of train location as an example, this would lead to an increase in uncertainty about position related to sensors with long acquisition times like Balises, RFID and Transponders along the track. We take into account the data without any synchronization protocols, for increase the accuracy and reduce the uncertainty after the data fusion algorithms. The case studies, we have analysed, derived from the needs of the project partners: train localization, head of an auger in the drilling sector localization and the location of containers of radioactive material waste in a reprocessing nuclear plant. They have the necessity to plan the maintenance operations of their infrastructure basing through architecture that taking input from the sensors, which are localization and diagnosis, maps and cost, to optimize the cost effectiveness and reduce the time of operation

Integration of cost-risk assessment of denial of service within an intelligent maintenance system

As organisations become richer in data the function of asset management will have to increasingly use intelligent systems to control condition monitoring systems and organise maintenance. In the future the UK rail industry is anticipating having to optimize capacity by running trains closer to each other. In this situation maintenance becomes extremely problematic as within such a high-performance network a relatively minor fault will impact more trains and passengers; such denial of service causes reputational damage for the industry and causes fines to be levied against the infrastructure owner, Network Rail. Intelligent systems used to control condition monitoring systems will need to optimize for several factors; optimization for minimizing denial of service will be one such factor. With schedules anticipated to be increasingly complicated detailed estimation methods will be extremely difficult to implement. Cost prediction of maintenance activities tend to be expert driven and require extensive details, making automation of such an activity difficult. Therefore a stochastic process will be needed to approach the problem of predicting the denial of service arising from any required maintenance. Good uncertainty modelling will help to increase the confidence of estimates. This paper seeks to detail the challenges that the UK Railway industry face with regards to cost modelling of maintenance activities and outline an example of a suitable cost model for quantifying cost uncertainty. The proposed uncertainty quantification is based on historical cost data and interpretation of its statistical distributions. These estimates are then integrated in a cost model to obtain accurate uncertainty measurements of outputs through Monte-Carlo simulation methods. An additional criteria of the model was that it be suitable for integration into an existing prototype integrated intelligent maintenance system. It is anticipated that applying an integrated maintenance management system will apply significant downward pressure on maintenance budgets and reduce denial of service. Accurate cost estimation is therefore of great importance if anticipated cost efficiencies are to be achieved. While the rail industry has been the focus of this work, other industries have been considered and it is anticipated that the approach will be applicable to many other organisations across several asset management intensive industrie

Precise vehicle location as a fundamental parameter for intelligent selfaware rail-track maintenance systems

The rail industry in the UK is undergoing substantial changes in response to a modernisation vision for 2040. Development and implementation of these will lead to a highly automated and safe railway. Real-time regulation of traffic will optimise the performance of the network, with trains running in succession within an adjacent movable safety zone. Critically, maintenance will use intelligent trainborne and track-based systems. These will provide accurate and timely information for condition based intervention at precise track locations, reducing possession downtime and minimising the presence of workers in operating railways. Clearly, precise knowledge of trains’ real-time location is of paramount importance. The positional accuracy demand of the future railway is less than 2m. A critical consideration of this requirement is the capability to resolve train occupancy in adjacent tracks, with the highest degree of confidence. A finer resolution is required for locating faults such as damage or missing parts, precisely. Location of trains currently relies on track signalling technology. However, these systems mostly provide an indication of the presence of trains within discrete track sections. The standard Global Navigation Satellite Systems (GNSS), cannot precisely and reliably resolve location as required either. Within the context of the needs of the future railway, state of the art location technologies and systems were reviewed and critiqued. It was found that no current technology is able to resolve location as required. Uncertainty is a significant factor. A new integrated approach employing complimentary technologies and more efficient data fusion process, can potentially offer a more accurate and robust solution. Data fusion architectures enabling intelligent self-aware rail-track maintenance systems are proposed

An intelligent framework and prototype for autonomous maintenance planning in the rail industry

This paper details the development of the AUTONOM project, a project that aims to provide an enterprise system tailored to the planning needs of the rail industry. AUTONOM extends research in novel sensing, scheduling, and decision-making strategies customised for the automated planning of maintenance activities within the rail industry. This paper sets out a framework and software prototype and details the current progress of the project. In the continuation of the AUTONOM project it is anticipated that the combination of techniques brought together in this work will be capable of addressing a wider range of problem types, offered by Network rail and organisations in different industries

Track geometry deterioration modelling for asset management: a visual analytics approach

To maintain safe operations and cost-effective maintenance, British railway tracks must be monitored. Track recording assets which include trains and cars, regularly monitor key components of the track in order to detect and diagnose early incipient faults. The measurements accumulate over time, providing time series data that can be used to model track geometry deterioration process. However, the modelling results are often too sophisticated to be used to their full potential in track asset management. As a result, the goal of this research is to use visualisation approaches to display the results of track geometry deterioration, which would simplify and enhance track asset management. Two visual techniques have been used. The first visual includes two dimensional plots enabling visual fault detection and localisation and the second is a 3D plot which gives a better sight for the decision makers to act. These visual analytics allowed a better understanding of fault occurrence, enable a vast amount of data integration, flexible and simple for stakeholders to use. The limitations of such approaches include the inability to visualise more than 5 dimensions and human interpretation

Development of porous-ceramic hydrostatic bearings

Porous-ceramic hydrostatic bearings have been recently developed. These bearings have demonstrated an exceptional overall performance when compared with conventional technology bearings. However, despite all the benefits, porous-ceramic hydrostatic bearings have yet to find widespread acceptance due to the problems found in tailoring the bearings geometry and size to suit precision engineering applications, while producing porous-structures with consistent and reproducible permeability. Using a series of fine grades of alumina powders in combination with maize starch granules, a new method for the manufacture of porous-ceramic bearings has been developed, based on the starch consolidation technique. By employing this method, it has been demonstrated that is possible to manufacture bearings of different geometries and shapes, with consistent and reproducible properties. The new method also proved to be low cost and environmentally sound. The performance of the new journal bearings has been investigated in a highly instrumented test-rig, and a comparable performance to that of previous porous- ceramic journal research has been observed. In a direct performance comparison between a porous-ceramic hydrostatic journal bearing and a conventional hydrostatic bearing of the same size, the porous-ceramic bearing demonstrated a significant performance improvement in terms of stiffness, power consumption and thermal performance. In previous research, water lubrication proved to significantly improve the spindle thermal performance. However, water lubrication is feared to promote corrosion within the spindle components. In the present research, the effects of water lubrication in porous-ceramic bearing systems were investigated. As a result, it has been demonstrated that corrosion in typical machine-tool materials can be effectively controlled by using inhibitors and low cost surface coatings. On the other hand, it has been also demonstrated that undesirable foaming, air entrainment and microbial growth can potentially develop in water/inhibitors lubrication systems. In this sense, the use of low viscosity oils proved to offer a comparable performance.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Challenges in cost analysis of innovative maintenance of distributed high-value assets

Condition monitoring is an increasingly important activity, but there is often little thought given to how a condition monitoring approach is going to impact the cost of operating a system. This paper seeks to detail the challenges facing such an analysis and outline the likely steps such an analysis will have to take to more completely understand the problem and provide suitable cost analysis. Adding sensors might be a relatively simple task, but those sensors come with associated cost; not only of the sensor, but of the utilities required to power them, the data gathering and processing and the eventual storage of that data for regulatory or other reasons. By adding condition monitoring sensors as a sub- system to the general system an organisation is required to perform maintenance to the new sensors sub-system. Despite these difficulties it is anticipated that for many high value assets applying condition monitoring will enable significant cost savings through elimination of maintenance activities on assets that do not need such cost and effort expended on them. Further savings should be possible through optimisation of maintenance schedules to have essential work completed at more cost efficient times

Effect of impact damage on fatigue performance of structures reinforced with GLARE bonded crack retarders

Fibre-Metal Laminates (FML) such as GLARE are of interest as bonded crack retarders (BCR) to improve the fatigue performance of aircraft structures. The degradation of the performance of the crack retarder in service if subjected to damage is a critical factor in designing with this concept. Bonded assemblies of an aluminium alloy substrate reinforced with a GLARE strap were prepared, and were subjected to low velocity impact damage onto the GLARE, with impact energies ranging from 10 to 60J. The thermal residual stresses developed during the bonding process of the GLARE to the aluminium were determined using neutron diffraction, and the change in the thermal residual stresses owing to impact damage onto the GLARE was evaluated. Pre- and post-impact fatigue performance of the BCR assemblies has been investigated. The results show that the BCR provides an improvement in fatigue life, but the reduction is impaired following impact damage. The results show that monitoring of impact damage will be critical in the damage tolerance assurance for aerospace structures containing bonded crack retarders

A railway track reconstruction method using robotic vision on a mobile manipulator: a proposed strategy

Autonomous robot integration in railways infrastructure maintenance accelerates the digitization and intelligence of infrastructure survey & maintenance, providing high-efficiency and low-cost execution. This paper proposes a health assessment based on 3D reconstruction technology for railway track maintenance using a mobile robotic sensing platform. By combining multiple sensing and taking advantage of a robotic manipulator, a digital model of the target track components is built by a robot-actuated vision system which provides better 3D structural and surface condition reconstruction. Global geo-location and surrounding laser scanning are integrated to reinforce the digital completeness of the model for intelligent management. The new method consists of the following steps: First, according to scheduled maintenance tasks, a Robotics Inspection and Repair System (RIRS) navigates to the task location and uses the onboard depth camera for positioning. Then robot-mounted vision system is guided with an automated trajectory to build the 3D reconstruction of the track or repair object using the vision modeling technique. Finally, the 3D reconstructed model is fused with surrounding mapping of depth vision and Lidar scanning. Both laboratory tests and a realistic track test validated the feasibility of the proposed method by creating an accurate 3D reconstructed model. The modeled rail steel section size is quantitively compared with the ground truth in dimension, demonstrating good accuracy with a size error of less than 0.3 cm. The main contribution includes: (1) unmanned automatic 3D reconstruction by a robotic mobile manipulator, (2) the technique trims the reconstruction details & data to the specific maintenance goal or components, which supports the infrastructure maintenance towards the high-detailed & target-oriented digital management. This combination strategy of robotic automation and sensor fusion lies down a promising foundation for automated digital twin establishment for railway maintenance with autonomous RIRS, and upgrades technology readiness and digital intelligence for maintenance managementEuropean Union funding: 881574/82625