CERES :: Browsing by Author "Starr, Andrew G."

Browsing by Author "Starr, Andrew G."

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Open Access
Acoustic monitoring of engine fuel injection based on adaptive filtering techniques
(Elsevier Science B.V., Amsterdam., 2010-12-31T00:00:00Z) Albarbar, A.; Gu, F.; Ball, A. D.; Starr, Andrew G.
Diesel engines injection process is essential for optimum operation to maintain the design power and torque requirements and to satisfy stricter emissions legislations. In general this process is highly dependent upon the injection pump and fuel injector health. However, extracting such information about the injector condition using needle movements or vibration measurements without affecting its operation is very difficult. It is also very difficult to extract such information using direct air-borne acoustic measurements.In this work adaptive filtering techniques are employed to enhance diesel fuel injector needle impact excitations contained within the air-borne acoustic signals. Those signals are remotely measured by a condenser microphone located 25cm away from the injector head, band pass filtered and processed in a personal computer using MatLab. Different injection pressures examined were 250, 240, 230, 220 and 210 bars and fuel injector needle opening and closing impacts in each case were thus revealed in the time-frequency domain using the Wigner-Ville distribution (WVD) technique. The energy of 7-15kHz frequency bands was found to vary according to the injection pressure. The developed enhancement scheme parameters are determined and its consistency in extracting and enhancing signal to noise ratio of injector signatures is examined using simulation and real measured signals; this allows much better condition monitoring information extraction.
Open Access
Amplitude of probability density function (APDF) of vibration response as a robust tool for gearbox diagnosis
(Blackwell Publishing Ltd, 2012-12-31T00:00:00Z) Rzeszucinski, P. J.; Sinha, J. K.; Edwards, Rodger; Starr, Andrew G.; Allen, B.
A ‘Go' or ‘No Go' assessment is a safety requirement for quick and robust estimation of the condition of gearboxes used in helicopters and other critical machines. A range of vibration-based condition indicators (CIs) has been developed to meet this requirement. CIs are compared automatically with pre-set threshold values representing a healthy system, so that the health of the gearbox can be assessed and diagnosis made where necessary. The use of kurtosis of the residual signal of the measured vibration data, computed as part of the ‘FM4' method, is widespread, because it is accepted as a good and reliable indicator. However, it has been observed in some cases that FM4 may not show a continually increasing trend with the propagation of a fault. This behaviour may lead to improper assessment of the severity of the fault. Hence, a new CI, based on the deviation in the normal probability density function (PDF) of the measured vibration data, is suggested which demonstrates an increasing trend that is more robustly and monotonically correlated with the fault propagation.
Open Access
Change detection in streaming data analytics: a comparison of Bayesian online and martingale approaches
(Elsevier, 2020-12-18) Namoano, Bernadin; Emmanouilidis, Christos; Ruiz-Carcel, Cristobal; Starr, Andrew G.
On line change detection is a key activity in streaming analytics, which aims to determine whether the current observation in a time series marks a change point in some important characteristic of the data, given the sequence of data observed so far. It can be a challenging task when monitoring complex systems, which are generating streaming data of significant volume and velocity. While applicable to diverse problem domains, it is highly relevant to monitoring high value and critical engineering assets. This paper presents an empirical evaluation of two algorithmic approaches for streaming data change detection. These are a modified martingale and a Bayesian online detection algorithm. Results obtained with both synthetic and real world data sets are presented and relevant advantages and limitations are discussed.
Open Access
A conceptual framework to assess the impact of training on equipment cost and availability in the military context
(Elsevier, 2015-10-27) Rodrigues, Duarte; Erkoyuncu, John Ahmet; Starr, Andrew G.; Wilding, Steve; Dibble, Alan; Laity, Martin; Owen, Richard
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.
Open Access
Context-based and human-centred information fusion in diagnostics
(Elsevier, 2016-12-16) Emmanouilidis, Christos; Pistofidis, Petros; Fournaris, Apostolos; Bevilacqua, Maurizio; Durazo-Cardenas, Isidro; Botsaris, Pantelis N.; Katsouros, Vassilis; Koulamas, Christos; Starr, Andrew G.
Maintenance management and engineering practice has progressed to adopt approaches which aim to reach maintenance decisions not by means of pre-specified plans and recommendations but increasingly on the basis of best contextually relevant available information and knowledge, all considered against stated objectives. Different methods for automating event detection, diagnostics and prognostics have been proposed, which may achieve very high performance when appropriately adapted and tuned to serve the needs of well defined tasks. However, the scope of such solutions is often narrow and without a mechanism to include human contributed intervention and knowledge contribution. This paper presents a conceptual framework of integrating automated detection and diagnostics and human contributed knowledge in a single architecture. This is instantiated by an e-maintenance platform comprising tools for both lower level information fusion as well as for handling higher level knowledge. Well structured maintenance relationships, such as those present in a typical FMECA study, as well as on the job human contributed compact knowledge are exploited to this end. A case study presenting the actual workflow of the process in an industrial setting is employed to pilot test the approach.
Open Access
Data fusion strategy for precise vehicle location for intelligent self-aware maintenance systems
(Institute of Electrical and Electronics Engineers, 2015) Bevilacqua, Maurizio; Tsourdos, Antonios; Starr, Andrew G.; Durazo-Cardenas, Isidro
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.
Open Access
Data-driven wheel slip diagnostics for improved railway operations
(Elsevier, 2022-09-27) Namoano, Bernadin; Ruiz-Carcel, Cristobal; Emmanouilidis, Christos; Starr, Andrew G.
Wheel slip activity detection is crucial in railway maintenance, as it can contribute to avoiding wheel damage but also track deteriorations leading to significant maintenance costs, trains delays, as well as the risk of accidents. Wheel slip activity is characterised by lower adhesion between track and wheel, especially in braking conditions, locking the wheels. It is complex to model or predict, being influenced by a multitude of factors including ambient conditions, global vehicle load, track and axle quality, leaves and objects present on the rail, steep incline, oxidation of the rails, and braking forces applied to the wheels. This paper presents a combined wavelet and tuned Long-Short Term Memory (LSTM) approach for the detection of wheel slip from time series data collected from real-world trains. Results provide evidence of superior performance over methods such as decision trees and random forests, naïve Bayes, k-nearest neighbours, logistic regression, and support vector machines.
Open Access
Experimental assessment of multiple contact wear using airborne noise under dry and lubricated conditions
(SAGE Publications (UK and US), 2017-03-29) Khan, Muhammad; Basit, K.; Khan, S. Z.; Khan, K. A.; Starr, Andrew G.
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.
Open Access
An intelligent framework and prototype for autonomous maintenance planning in the rail industry
(SciTePress, 2015-05) Turner, Christopher J.; Tiwari, Ashutosh; Starr, Andrew G.; Durazo-Cardenas, Isidro; Blacktop, K
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.
Open Access
Methodology and theory evaluation of overall equipment effectiveness based on market
(Emerald Group Publishing Limited, 2010-12-31T00:00:00Z) Anvari, Farhad; Edwards, Rodger; Starr, Andrew G.
Purpose - Continuous manufacturing systems used within the steel industry involve different machines and processes that are arranged in a sequence of operations in order to manufacture the products. The steel industry is generally a capital-intensive industry and, because of high capital investment, the utilisation of equipment as effectively as possible is of high priority. This paper seeks to illustrate a new method, overall equipment effectiveness market-based (OEE-MB) for the precise calculation of equipment effectiveness for full process cycle in order to respond to the steel market. Design/methodology/approach - A refinement of the existing concept of OEE is developed based on a new scheme for loss analysis within market time. The paper illustrates the concept with a case study based on compact strip manufacturing processes within the steel industry. Findings - While the results for OEE by ignoring a considerable amount of possible hidden losses might be satisfying, the OEE-MB report shows potential room for improvement. It reflects changes in both the internal and external market for the steel industry, and therefore provides a tool not only for monitoring but also for managing improvement. Practical implications - OEE-MB is an applicable method for the precise calculation of equipment effectiveness that provides a sound perspective on improvement of steel plants by taking into consideration all losses within market time for meeting both internal and external demands. Originality/value - OEE-MB monitors production and measures the equipment effectiveness for full process cycle in order to meet the market. It makes communication more efficient and easier within the steel industry and may be used as a benchmark to achieve world-class standard.
Open Access
Normalised Root Mean Square and Amplitude of Sidebands of Vibration Response as Tools for Gearbox Diagnosis
(Blackwell Publishing Ltd, 2012-12-31T00:00:00Z) Rzeszucinski, P. J.; Sinha, J. K.; Edwards, Rodger; Starr, Andrew G.; Allen, B.
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.
Open Access
Ontology-based context modeling in physical asset integrity management
(Frontiers Research, 2020-10-30) Al-Shdifat, Ali; Emmanouilidis, Christos; Khan, Muhammad; Starr, Andrew G.
Asset management is concerned with the management practices, technologies and tools necessary to maximize the value delivered by physical engineering assets. IoT-generated data are increasingly considered as an asset and the data asset value needs to be maximized too. However, asset-generated data in practice are often collected in non-actionable form. Collected data may comprise a wide number of parameters, over long periods of time and be of significant scale. Yet they may fail to represent the range of possible scenarios of asset operation or the causal relationships between the monitored parameters, and so the size of the data collection, while adding to the complexity of the problem, does not necessarily allow direct data asset value exploitation. One way to handle data complexity is to introduce context information modeling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. The aim of the present paper is, therefore, 2-fold: to analyse current approaches to addressing IoT context information management, mapping how context-aware computing addresses key challenges and supports the delivery of monitoring solutions; and to develop a maintenance context ontology focused on failure analysis of mechanical components so as to drive monitoring services adaptation. The approach is demonstrated by applying the ontology on an industrially relevant physical gearbox test rig, designed to model complex misalignment cases met in manufacturing and aerospace applications.
Open Access
Parametric study for optimizing fiber-reinforced concrete properties
(Wiley, 2024-03-31) Khalel, Hamad Hasan Zedan; Khan, Muhammad; Starr, Andrew G.; Sadawi, Noureddin; Mohamed, Omar Ahmed; Khalil, Ashraf; Esaker, Mohamed
Concrete with fiber reinforcement is stronger and more ductile than concrete without reinforcement. Significant efforts have been made to demonstrate the properties and enhancements of concrete after reinforcement with various types and shapes of fibers. However, the issue of optimization in the reinforcement process is still unanswered. There is no academic study in the literature now available that can pinpoint the ideal fiber type, quantity, and shape and, more crucially, the overall technical viability of the reinforcement. The parametric analysis in this study determines the ideal shape, size, and proportion of fibers. The input and output parameters were separated from the optimization design variables. Input parameters included assessment of samples of fresh and mechanical concrete properties and the influence of type, length, and percentage of fiber on concrete performance. The aim was to establish the most efficient relationship between fiber dose and dimension to optimize the combined responses of workability and splitting tensile, flexural, and compressive strength. The mechanical and fresh properties of concrete reinforced with four different fibers, PFRC-1, PFRC-2, SFRC-1, and SFRC-2, were tested. The analysis showed that SFRC-2-20 mm-1%, with compressive, split tensile, flexural, and workability values of 44.7 MPa, 3.64 MPa, 5.3 MPa, and 6.5 cm respectively, was the most effective combination among the materials investigated. The optimization technique employed in this study offers new, important insights into how input and output parameters relate to one another.
Open Access
Perspectives on the commercial development of landing gear health monitoring systems
(Elsevier Science B.V., Amsterdam., 2011-12-31T00:00:00Z) Phillips, Paul; Diston, Dominic; Starr, Andrew G.
The development of health monitoring technologies for aerospace systems creates a number of challenges for the community of engineers and technical specialists as they seek to integrate the technology into well defined working practices. These challenges do not just extend to the technical, but require a number of commercial questions to be addressed. It is of vital importance, that there is a clearly identified need for aerospace health monitoring, both from a technological and commercial viewpoint. If these needs cannot be identified, then any attempt for marketing health monitoring as a necessary future technological requirement is doomed for failure. Health monitoring technology will need to either deliver significant cost saving benefits to the aircraft operator or demonstrated increases in aircraft safety. The objective of the paper is to provide an assessment of the commercial benefits and development of aircraft landing gear health monitoring. The commercial need and challenges for health monitoring systems are explored in this paper within the context of a changing aerospace maintenance industry and the role in which new systems technology will play. The key findings of the research study are that within the aerospace industry there is a desired paradigm shift within aircraft maintenance towards offering maintenance systems with predictive capabilities. This maintenance revolution will not just incorporate new technologies, but will result in aircraft maintenance packages tailored towards individual customer requirements. The study illustrates the state-of-the-art in health monitoring currently restricts aerospace integration and a number of key technical and commercial issues need to be addressed. Predictive health monitoring offers a variety of commercial benefits for maintenance providers, aircraft operators and manufacturers. However, in order for these benefits to be realised increased transparency in maintenance related information is required between these key players.
Open Access
Precise vehicle location as a fundamental parameter for intelligent selfaware rail-track maintenance systems
(Elsevier, 2014-10-31) Durazo-Cardenas, Isidro; Starr, Andrew G.; Tsourdos, Antonios; Bevilacqua, Maurizio; Morineau, Julien
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.
Open Access
A review of data fusion models and architectures: Towards engineering guidelines
(Springer, 2005-06-21) Esteban, Jaime; Starr, Andrew G.; Willetts, Robert; Hannah, Paul; Bryanston-Cross, Peter
This paper reviews the potential benefits that can be obtained by the implementation of data fusion in a multi-sensor environment. A thorough review of the commonly used data fusion frameworks is presented together with important factors that need to be considered during the development of an effective data fusion problem-solving strategy. A system-based approach is defined for the application of data fusion systems within engineering. Structured guidelines for users are proposed.
Open Access
A review of key planning and scheduling in the rail industry in Europe and UK
(SAGE, 2016-02) Turner, Christopher J.; Tiwari, Ashutosh; Starr, Andrew G.; Blacktop, K.
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.
Open Access
Review of the modelling approaches for availability contracts in the military context
(Elsevier, 2015-05-14) Rodrigues, Duarte; Erkoyuncu, John Ahmet; Starr, Andrew G.; Wilding, Steve; Dibble, Alan; Laity, Martin
The defence context more recently has been experiencing a significant shift towards servitization. As competition has increased, commercial strategies are increasingly moving towards providing through-life solutions for complex engineering products such as submarines. Within such a context value for money is an essential driver in a life cycle sense for selecting a bid. The defence sector has largely been affected by this change in the business environment. Industrial Product Service System (IPS2) is a model of providing services that satisfy industrial customers and aims to reduce lifecycle impacts of products and services through product servicing, remanufacturing and recycling. This approach has proved to be an effective solution to enhance the services support in military projects. IPS2 offers client value by responding more efficiently to the client demands with reduced prices; it is delivered in the form of contracting approaches between Ministry of Defence (MoD) and industry; these contracts can differ in several aspects as risk sharing, application level, ownership policy and supportability specifications vary. This research focuses on Contracting for Availability (CfA), which is a particular approach of IPS2.The paper aims to present the review of literature in designing support strategies for CfA, identifying the good practices and challenges, and to propose a systematic approach to fill the industrial and academic gap towards an optimization of the current modelling process. This work starts by presenting a literature review in IPS2; it then moves into the optimization processes, describing how contractors currently design a long term service support contract in the military context with better value for money and high level of system readiness. The key cost and performance drivers are identified and a framework is presented to enhance the design process of CfA. The methodology of the paper relies on literature. This research aims to extend the work of several authors in predicting the cost of services in the military contracts.
Open Access
Size differentiation of a continuous stream of particles using acoustic emissions
(Acoustical Society of America, 2016-04-30) Nsugbe, Ejay; Starr, Andrew G.; Foote, Peter; Ruiz Carcel, Cristobal; Jennions, Ian K.
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.
Open Access
A software architecture for autonomous maintenance scheduling: Scenarios for UK and European Rail
(W I T Press, 2016-11-22) Turner, Christopher J.; Ravi, P. T.; Tiwari, Ashutosh; Starr, Andrew G.; Blacktop, K.
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.