A review of key planning and scheduling in the rail industry in Europe and UK

Planning and scheduling activities within the rail industry have benefited from developments in computer-based simulation and modelling techniques over the last 25 years. Increasingly, the use of computational intelligence in such tasks is featuring more heavily in research publications. This paper examines a number of common rail-based planning and scheduling activities and how they benefit from five broad technology approaches. Summary tables of papers are provided relating to rail planning and scheduling activities and to the use of expert and decision systems in the rail industry.EPSR

A software architecture for autonomous maintenance scheduling: Scenarios for UK and European Rail

A new era of automation in rail has begun offering developments in the operation and maintenance of industry standard systems. This article documents the development of an architecture and range of scenarios for an autonomous system for rail maintenance planning and scheduling. The Unified Modelling Language (UML) has been utilized to visualize and validate the design of the prototype. A model for information exchange between prototype components and related maintenance planning systems is proposed in this article. Putting forward an architecture and set of usage mode scenarios for the proposed system, this article outlines and validates a viable platform for autonomous planning and scheduling in rail

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

Wear debris: basic features and machine health diagnostics

Modern high speed and power machinery components like gears, bearings, pumps, hydraulics and motors normally suffer from wear phenomena during operation. The study of wear debris can help estimate the condition of the surface of a component, so its basic features may be used to diagnose component health prior to failure. In this paper, a review is presented of the current literature related to wear debris and its analysis. The basic features of wear debris are highlighted, and their possible potential to diagnose the health of machine components is discussed. The basic features of wear debris have been classified with respect to the approach of measurement for component health diagnostics. In addition, each feature has been detailed with its possible measurement descriptors, its trend during machine component operation, and its distinct health diagnostics capability. Finally the paper proposes advances in machine component health diagnostics solution, by optimising the diagnostic capabilities of basic wear debris features

Normalised Root Mean Square and Amplitude of Sidebands of Vibration Response as Tools for Gearbox Diagnosis

Quick assessment of the condition of gearboxes used in helicopters is a safety requirement. One of the most widely used helicopter on-board-mounted condition monitoring system these days is the Health and Usage Monitoring System. It has been specifically designed to monitor the condition of all safety-critical components operating in the helicopter through calculation of so-called condition indicators (CIs) - signal processing routines designed to output a single number that represents the condition of the monitored component. Among number of available parameters, there is a couple of CIs that over the years of testing have earned a reputation of being the most reliable measures of the gear tooth condition. At the same time, however, it has been observed that in some cases, those techniques do not properly indicate the deteriorating condition with the propagation of a gear tooth fault with the period of operation. Hence, three more robust methods have been suggested, which are discussed in this article

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

A conceptual framework to assess the impact of training on equipment cost and availability in the military context

Designing military support is challenging and current practices need to be reviewed and improved. This paper gives an overview of the Industry current practices in designing military support under Ministry of Defence/Industry agreements (in particular for Contracting for Availability (CfA)), and identifies challenges and opportunities for improvement. E.g. training delivery was identified as an important opportunity for improving the CfA in-service phase. Thus, an innovative conceptual framework is presented to assess the impact of training on the equipment availability and cost. Additionally, guidelines for improving the current training delivery strategies are presented, which can also be applied to other Industry contexts

Size differentiation of a continuous stream of particles using acoustic emissions

Procter and Gamble (P&G) requires an online system that can monitor the particle size distribution of their washing powder mixing process. This would enable the process to take a closed loop form which would enable process optimization to take place in real time. Acoustic emission (AE) was selected as the sensing method due to its non-invasive nature and primary sensitivity to frequencies which particle events emanate. This work details the results of the first experiment carried out in this research project. The first experiment involved the use of AE to distinguish sieved particle which ranged from 53 to 250 microns and were dispensed on a target plate using a funnel. By conducting a threshold analysis of the peaks in the signal, the sizes of the particles could be distinguished and a signal feature was found which could be directly linked to the sizes of the particles

Experimental assessment of multiple contact wear using airborne noise under dry and lubricated conditions

The generation of wear and airborne noise is inevitable in the mechanical contacts of the machine components. This paper addresses the effectiveness of the airborne noise data in estimating the wear on a disc under multi-contact conditions. A pin-on-disc rig was employed to study the role of noise parameters on the evolution of the wear area. When a pin slides on the disc, the airborne noise is generated and subsequently a sound signal is obtained. These signals, for various sets of experiments, were recorded using a digital microphone. A Matlab code was developed and employed to estimate the noise parameters from the recorded sound. Noise parameters including values of voltage RMS, noise counts and amplitudes of dominant frequencies were used to analyse the variation in the disc wear at different time intervals. These parameters were found to be effective in the determination of the wear damage evaluation under different loads without lubrication