Browsing by Author "Khan, Samir"

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    Advancing fault diagnosis through ontology-based knowledge capture and application
    (IEEE, 2024-07-25) Del Amo, Iñigo Fernández; Erkoyuncu, John Ahmet; Bulka, Dominik; Farsi, Maryam; Ariansyah, Dedy; Khan, Samir; Wilding, Stephen
    This article addresses a critical gap in the field of fault diagnosis for complex systems, focusing on the development and application of an ontology-based approach to capture and utilize expert knowledge. The key objective is to enhance fault diagnosis precision and effectiveness, specifically in challenging No-Fault-Found (NFF) scenarios, by harnessing the extensive, often implicit, understanding of seasoned professionals. The study uses a comprehensive methodology that includes creating a specialized ontology called DIAGONT, which captures the expert reasoning in fault diagnosis. Field experts contribute to the development of this ontology, ensuring its relevance and applicability. Real-world case studies and controlled experiments are used to rigorously validate the ontology. The goal of these experiments is to evaluate how effective the ontology is in enhancing fault diagnosis procedures when compared to traditional methods. Our case studies focused on two complex engineering assets, a loading arm and a helicopter mission system, due to their complexity and the frequency of non-functional failure scenarios. The analysis shows that using the DIAGONT ontology leads to improved accuracy and efficiency in fault diagnosis. A structured format allowed experts to successfully capture and reuse diagnostic knowledge, resulting in a noticeable reduction in NFF scenarios. The application of ontology-based approach exhibited potential in enhancing knowledge transfer between experts and less experienced technicians, potentially resulting in long-lasting improvements in maintenance practices. The results highlight how ontology-based systems can improve fault diagnosis in complex engineering systems.
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    Application of CNN for multiple phase corrosion identification and region detection
    (Elsevier BV, 2024-10-30) Oyedeji, Oluseyi Ayodeji; Khan, Samir; Erkoyuncu, John Ahmet
    Corrosion is a significant issue that contributes negatively to the degradation of materials most especially metals. To ensure proper maintenance, enhance reliability and prevent breakdown, it is very essential to not only effectively detect corrosion but to also understand its locations and distributions on the materials. A Multiple phase Convolutional Neural Network (CNN) model is created for this purpose. The Multiple phase CNN model consists of custom designed deep learning algorithms at various stages. This created the opportunity to make use of binary classification, multi-label classification and patch distribution algorithm to detect and identify corrosion regions on metallic materials. Six (6) different labels of corrosion were modelled to represent different levels of degradation using 600 anonymized images. The images were used in the various stages of the framework for training the respective models. Results at the binary level shows 94.87 % of corrosion detection. The multiclass stage of the Multiple phase CNN records the highest accuracy of 92.1 %. The patch distribution stage recorded a highest accuracy of 96.5 % and 94.6 % for the Average Image and Average Pixel ROCAUC (Region of Concentration Area Under Cover). It also shows a region segment average accuracy detection of 91.5 % (image level) and 89.2 %(pixel level) for 9 distinct regions. The research provides a comprehensive and detailed reliability and maintenance information for Aerospace, Transport and Manufacturing Materials experts and non-experts. The framework shows a robust approach to detecting corrosion which is essential for critical and safety applications as well as preventing economic loss due to corrosion. This can also be extended to other domains beyond the corrosion case study.
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    Data: Fast Augmented Reality Authoring: Fast Creation of AR step-by-step Procedures for Maintenance Operations
    (Cranfield University, 2023-08-14 14:15) Palmarini, Riccardo; Fernandez Del Amo Blanco, Inigo; Ariansyah, Dedy; Khan, Samir; ahmet Erkoyuncu, John; Roy, Rajkumar
    Augmented Reality (AR) has shown great potential for improving human performance in Maintenance, Repair, and Overhaul (MRO) operations. Whilst most studies are currently being carried out at an academic level, the research is still in its infancy due to limitations in three main aspects: limited hardware capabilities, the robustness of object recognition, and content-related issues. This article focuses on the last point, by proposing a new geometry-based method for creating a step-by-step AR procedure for maintenance activities. The Fast Augmented Reality Authoring (FARA) method assumes that AR can recognise and track all the objects in a maintenance environment when CAD models are available, to knowledge transfer to a non-expert maintainer. The novelty here lies in the fact that FARA is a human-centric method for authoring animation-based procedures with minimal programming skills and the manual effort required. FARA has been demonstrated, as a software unit, in an AR system composed of commercially available solutions and tested with over 30 participants. The results show an average time saving of 34.7% (min 24.7%; max 55.3%) and an error reduction of 68.6% when compared to the utilisation of traditional hard-copy manuals. Comparisons are also drawn from performances of similar AR applications to illustrate the benefits of procedures created utilising FARA.
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    An effective uncertainty based framework for sustainable industrial product-service system transformation
    (Elsevier, 2018-09-22) Erkoyuncu, John Ahmet; Roy, Rajkumar; Shehab, Essam; Durugbo, Christopher; Khan, Samir; Datta, Partha
    Industrial Product-Service Systems (IPS2) can provide insights to enhance the environmental sustainability and lower environmental impact. However, its successful realisation for preventing the production of waste, while increasing efficiencies in the uses of energy and human capital remains a highly convoluted problem. This research article aims to address this issue by presenting an innovative uncertainty-based framework that can be used to assist in achieving increased sustainability within the context of IPS2. The developed framework explains the drivers for decision-making and cost to enable sustainability improvements in transforming to industrial services. This is based on academic literature, and multiple case studies of seven industrial companies with over 30 h of semi-structured interviews. The validation of the framework through two case studies demonstrates that uncertainty management can enable resource efficiency and offer sustainable transformation to service provision.
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    Fast augmented reality authoring: fast creation of AR step-by-step procedures for maintenance operations
    (IEEE, 2023-01-24) Palmarini, Riccardo; Fernández del Amo, Iñigo; Ariansyah, Dedy; Khan, Samir; Erkoyuncu, John Ahmet; Roy, Rajkumar
    Augmented Reality (AR) has shown great potential for improving human performance in Maintenance, Repair, and Overhaul (MRO) operations. Whilst most studies are currently being carried out at an academic level, the research is still in its infancy due to limitations in three main aspects: limited hardware capabilities, the robustness of object recognition, and content-related issues. This article focuses on the last point, by proposing a new geometry-based method for creating a step-by-step AR procedure for maintenance activities. The Fast Augmented Reality Authoring (FARA) method assumes that AR can recognise and track all the objects in a maintenance environment when CAD models are available, to knowledge transfer to a non-expert maintainer. The novelty here lies in the fact that FARA is a human-centric method for authoring animation-based procedures with minimal programming skills and the manual effort required. FARA has been demonstrated, as a software unit, in an AR system composed of commercially available solutions and tested with over 30 participants. The results show an average time saving of 34.7% (min 24.7%; max 55.3%) and an error reduction of 68.6% when compared to the utilisation of traditional hard-copy manuals. Comparisons are also drawn from performances of similar AR applications to illustrate the benefits of procedures created utilising FARA.
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    A framework for constructing a common knowledge base for human-machine system to perform maintenance tasks
    (Cranfield University, 2022-11-08) Deng, Haoxuan; Khan, Samir; Erkoyuncu, John Ahmet
    A reliable and comprehensive maintenance is important to promise the system running in a normal state, but it is skill-intensive and heavily dependent on human labor. With the development of predictive maintenance in industry, an optimized solution can be posed for maintaining assets with less downtime and cost. However, most of current research on this topic is limited on a top-level algorithm design for prediction, but few consider how to perform the maintenance tasks according to the prediction results at a particular occasion and condition. Besides, the complexity of system is exploded, and it may take people much effort to cover every detail to achieve a credible maintenance result. Thus, machine is introduced to collaborate with human by undertaking some work and suggesting actions to take in order to reduce human physical and mental workload. This paper aims to present a framework to integrate human knowledge and machine learning into a common knowledge base to enable human and machine can contribute to shift the final maintenance decision from planning to performing. The proposed framework is based on a knowledge graph generated by ontology and machine learning, which can be conveniently retrieved by human via questions answering system or visualization platform and efficiently computed by machine via graph representation learning. Consequently, domain knowledge can be formally represented, systematically managed and easily reused by human-machine teaming to attack domain-specific problems. In a long term, the evolving knowledge based, with an accumulation on samples and information, can guide the team to draw a reasonable and delicate strategy for overhaul and recondition, moreover, ensure the next generation of maintenance: prescriptive maintenance.
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    A framework to estimate the cost of No-Fault Found events
    (Elsevier, 2015-12-29) Erkoyuncu, John Ahmet; Khan, Samir; Hussain, Syed Mohammed Fazal; Roy, Rajkumar
    The article investigates a generic framework to estimate maintenance costs attributed to the No Fault Found (NFF) phenomenon. Such overhead costs are particularly difficult to quantify due to potentially serviceable equipment being returned for repair. Other factors, such as a reduction in the availability of the system, compromising reliability of high value assets, and logistical factors, can all contribute to the cost of resolving an unknown fault. Here we apply the soft systems methodology to capture the critical cost drivers of NFF across the supply chain and build a framework to estimate the cost of NFF. We use a multi-method design including an online survey, workshops and semi-structured interviews to study NFF related cost practices based on information from 12 key participants across 7 UK organisations. The study identifies the major NFF cost drivers across the supply chain (e.g. transportation), the OEM (e.g. inventory) and the customer (e.g. lost man hours). An agent based model is used to evaluate the impact of these cost drivers on the overall NFF cost. The analysis shows how the most appropriate drivers can be selected to represent the cumulative costs due to NFF events and their impacts across the supply network. From the academic perspective, the generic framework for NFF cost estimation demonstrates how qualitative and quantitative information can be used together to achieve maintenance objectives. From a practical perspective, by applying the framework on one component, an organisation has the liberty to analyse the cost of NFF for that particular unit only.
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    Modernizing medical waste management: unleashing the power of Internet of Things (IoT)
    (MDPI, 2023-06-21) Mohamed, Nurul Hamizah; Khan, Samir; Jagtap, Sandeep
    The rapid technological advancements of modern times have brought about the need for an innovative and contemporary approach to medical waste management procedures. This arises from the inadequacy of conventional manual techniques in ensuring the safety of employees and the environment from infections. The increasing amount of waste produced each day can exacerbate the situation if no action is taken to address the current issue. This article presents a systematic review of the use of the Internet of Things (IoT) in medical waste management, utilizing the PRISMA approach. The adoption of the IoT in waste and medical waste monitoring is analyzed for its potential to enhance the overall waste monitoring procedure and contribute to achieving net-zero goals. Empirical evidence from studies conducted in the last five years has revealed the benefits of employing waste bin sensors as a digital surveillance tool for real-time waste status monitoring. While a few researchers have proposed the use of the IoT in medical waste monitoring, the application is currently limited to either monitoring storage facilities, waste transportation, or disposal processes, specifically. These limitations are discussed to understand the barriers that hinder further development. Among the selected analyzed studies are published articles and conference papers that offer solutions for addressing waste management issues and facilitating further development. This paper also aims to identify IoT technologies for monitoring waste and medical waste management. The digitalization of medical waste can ensure that the entire monitoring procedure is conducted directly and in real time. The collected data can be easily shared, and the condition of the waste can be updated periodically.
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    No Fault Found events in maintenance engineering Part 1: current trends, implications and organizational practices
    (Elsevier, 2013-12-01) Khan, Samir; Phillips, Paul; Jennions, Ian K.; Hockley, Chris
    This paper presents the first part of a state of the art review on the No Fault Found (NFF) phenomenon. The aim has been to compile a systematic reference point for burgeoning NFF literature, and to provide a comprehensive overview for gaining an understanding of NFF knowledge and concepts. Increasing systems complexities have seen a rise in the number of unknown failures that are being reported during operational service. Units tagged as ‘NFF’ are evidence that a serviceable component was removed, and attempts to troubleshoot the root cause have been unsuccessful. There are many reasons on how these failures manifest themselves and these papers describe the prominent issues that have persisted across a variety of industrial applications and processes for decades. This article, in particular, deals with the impact of NFF from an organizational culture and human factors point of view. It also highlights recent developments in NFF standards, its financial implications and safety concerns.
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    No Fault Found events in maintenance engineering Part 2: Root causes, technical developments and future research
    (Elsevier, 2013-12-01) Khan, Samir; Phillips, Paul; Hockley, Chris; Jennions, Ian K.
    This is the second half of a two paper series covering aspects of the no fault found (NFF) phenomenon, which is highly challenging and is becoming even more important due to increasing complexity and criticality of technical systems. Part 1 introduced the fundamental concept of unknown failures from an organizational, behavioral and cultural stand point. It also reported an industrial outlook to the problem, recent procedural standards, whilst discussing the financial implications and safety concerns. In this issue, the authors examine the technical aspects, reviewing the common causes of NFF failures in electronic, software and mechanical systems. This is followed by a survey on technological techniques actively being used to reduce the consequence of such instances. After discussing improvements in testability, the article identifies gaps in literature and points out the core areas that should be focused in the future. Special attention is paid to the recent trends on knowledge sharing and troubleshooting tools; with potential research on technical diagnosis being enumerated.
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    A novel approach for No Fault Found decision-making
    (Elsevier, 2016-06-20) Khan, Samir; Farnsworth, Michael; Erkoyuncu, John Ahmet
    Within aerospace and defence sectors, organisations are adding value to their core corporate offerings through services. These services tend to emphasise the potential to maintain future revenue streams and improved profitability and hence require the establishment of cost effective strategies that can manage uncertainties within value led services e.g. maintenance activities. In large organisations, decision-making is often supported by information processing and decision aiding systems; it is not always apparent whose decision affects the outcome the most. Often, accountability moves away from the designated organisation personnel in unforeseen ways, and depending on the decisions of individual decision makers, the structure of the organisation, or unregulated operating procedures may change. This can have far more effect on the overall system reliability – leading to inadequate troubleshooting, repeated down-time, reduced availability and increased burden on Through-life Engineering Services. This paper focuses on outlining current industrial attitudes regarding the No Fault Found (NFF) phenomena and identifies the drivers that influence the NFF decision-making process. It articulates the contents of tacit knowledge and addresses a knowledge gap by developing NFF management policies. The paper further classifies the NFF phenomenon into five key processes that must be controlled by using the developed policies. In addition to the theoretical developments, a Petri net model is also outlined and discussed based on the captured information regarding NFF decision-making in organisations. Since NFF decision-making is influenced by several factors, Petri nets are sought as a powerful tool to realise a meta-model capability to understand the complexity of situations. Its potential managerial implications can help describe decision problems under conditions of uncertainty. Finally, the conclusions indicate that engineering processes, which allow decision-making at various maintenance echelons, can often obfuscate problems that then require a systems approach to illustrate the impact of the issue.
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    Olfactory-based augmented reality support for industrial maintenance
    (IEEE, 2020-01-29) Erkoyuncu, John Ahmet; Khan, Samir
    Augmented reality (AR) applications have opened innovative ways for performance improvement in the IoT industry. It can enhance user perception of the real-world by providing valuable information about an industrial environment and provide visual virtual information onto a head-mounted device (HMD). Such information is important for maintainers to quickly detect abnormalities, reduces nugatory routines and facilitate preventive maintenance.Since odors are made up of volatile compounds at low concentration, they can be used for olfactory-based identification.The prototype comprises of three modules: an electronic nose, a database and an AR application integrated with Microsoft HoloLens. After diagnosing an odor, the results are then sent wirelessly through a local network to the HMD worn by the user. To validate the technology, four odors have been used, including engine oil, sun lotion, medical alcohol and perfume, to record behaviors and demonstrate the repeatability of the process. The presented technology incorporates sampling methods, cleaning processes and statistical analysis that can be further scrutinized to allow correct smell identification.
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    On the requirements of digital twin-driven autonomous maintenance
    (Elsevier, 2020-09-10) Khan, Samir; Farnsworth, Michael; McWilliam, Richard; Erkoyuncu, John Ahmet
    Autonomy has become a focal point for research and development in many industries. Whilst this was traditionally achieved by modelling self-engineering behaviours at the component-level, efforts are now being focused on the sub-system and system-level through advancements in artificial intelligence. Exploiting its benefits requires some innovative thinking to integrate overarching concepts from big data analysis, digitisation, sensing, optimisation, information technology, and systems engineering. With recent developments in Industry 4.0, machine learning and digital twin, there has been a growing interest in adapting these concepts to achieve autonomous maintenance; the automation of predictive maintenance scheduling directly from operational data and for in-built repair at the systems-level. However, there is still ambiguity whether state-of-the-art developments are truly autonomous or they simply automate a process. In light of this, it is important to present the current perspectives about where the technology stands today and indicate possible routes for the future. As a result, this effort focuses on recent trends in autonomous maintenance before moving on to discuss digital twin as a vehicle for decision making from the viewpoint of requirements, whilst the role of AI in assisting with this process is also explored. A suggested framework for integrating digital twin strategies within maintenance models is also discussed. Finally, the article looks towards future directions on the likely evolution and implications for its development as a sustainable technology
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    Perspectives on trading cost and availability for corrective maintenance at the equipment type level
    (Elsevier, 2017-05-29) Erkoyuncu, John Ahmet; Khan, Samir; Eiroa, Alexandre López; Butler, Nigel; Rushton, Keith R.; Brocklebank, Simon
    Characterising maintenance costs has always been challenging due to a lack of accurate prior cost data and the uncertainties around equipment usage and reliability. Since preventive maintenance does not completely prevent corrective repairs in demanding environments, any unscheduled maintenance can have a large impact on the overall maintenance costs. This introduces the requirement to set up support contracts with minimum baseline solutions that warrant the target demand within certain costs and risks. This article investigates a process that has been developed to estimate performance based support contract costs attributed to corrective maintenance. These can play a dominant role in the through-life support of high values assets. The case context for the paper is the UK Ministry of Defence. The developed approach allows benchmarking support contract solutions, and enabling efficient planning decisions. Emphasis is placed on learning from feedback, testing and validating current methodologies for estimating corrective maintenance costs and availability at the Equipment Type level. These are interacting sub-equipment's that have unique availability requirements and hence have a much larger impact on the capital maintenance expenditure. The presented case studies demonstrate the applicability of the approach towards adequate savings and improved availability estimates.
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    Study on intermittent faults and electrical continuity
    (Elsevier, 2014-10-31) Ahmada, Wakil Syed; Perinpanayagam, Suresh; Khan, Samir; Jennions, Ian K.
    connector, or incorrect installation during initial manufacture and assembly. Unless such issues are narrowed down to a specific root cause, any corrective actions or troubleshooting will be difficult to carry out, and hence its resolution may not make its way into future designs of the system. This leads to further susceptibility to NFF. Intermittent behaviour is often a clear sign of a partially damaged connector, or a connector undergoing a particular degradation mechanism, with the level of intermittency being further aggravated through process variation of harsh environments and parametric faults. In order to further our understanding of the relationship between degradation, operating conditions, intermittent behaviour within the subject, an experimental investigations have been carried out. This paper is a work in progress paper that illustrates a test regime that has been used to stimulate intermittence in electronic connectors whilst subjected to vibration, using both a traditional oscilloscope and bespoke intermittent fault detection equipment, in order to capture an intermittent signature. The results of these experiments provide an insight into the limitations of test equipment and requirements for future intermittent fault detection techniques.
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    Supporting data for "A framework to estimate the cost of No-Fault Found events"
    (Cranfield University, 2017-10-10 11:57) ahmet Erkoyuncu, John; Roy, Rajkumar; Khan, Samir; Mohammed Fazal Hussain, Syed
    The files cover: the questionnaire used, the input data requirements, and a presentation containing further data and supporting information that went into the modelling in the paper "A framework to estimate the cost of No-Fault Found events".
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    Towards digitalization of Malaysian medical facilities waste management
    (Cranfield University, 2022-11-08) Mohamed, Nurul Hamizah; Khan, Samir; Jagtap, Sandeep
    Medical waste is produced in huge quantities daily, and the increasing amount of it is a worldwide issue that makes managing medical waste more and more crucial. Leakage or improper use of medical waste can be harmful, risking the environment and human lives. The pandemic COVID-19 has challenged current practices with the increasing number of waste and the possibility of transferring the virus from one person to another. In Malaysia, waste management predominantly remains a manual endeavor. where data is usually keyed in either by waste generators, transport contractors, or process occupiers. Digitalization of this setup can be a way to manage waste management effectively as it could be tracked and monitored in real-time. This paper discusses the applicability of exponential technologies, such as Internet of Things and Blockchain, to communicate real-time data to all stakeholders. It presents a framework that can be used to improve the overall waste management process by improving tracking and tractability of waste. Such technology is expected to have an impact across the whole waste management cycle including segregation, storage, transport, and disposal process, and at the same time, help with documentation and administration arrangement.