Browsing by Author "Camci, Fatih"

Now showing 1 - 8 of 8
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    City blood: A visionary infrastructure solution for household energy provision through water distribution networks
    (Elsevier, 2013-11) Karaca, Ferhat; Camci, Fatih; Raven, Paul Graham
    This paper aims to expand current thinking about the future of energy and water utility provision by presenting a radical idea: it proposes a combined delivery system for household energy and water utilities, which is inspired by an analogy with the human body. It envisions a multi-functional infrastructure for cities of the future, modelled on the human circulatory system. Red blood cells play a crucial role as energy carriers in biological energy distribution; they are suspended in the blood, and distributed around the body to fuel the living cells. So why not use an analogous system – an urban circulatory system, or “city blood” – to deliver energy and water simultaneously via one dedicated pipeline system? This paper focuses on analysing the scientific, technological and economic feasibilities and hurdles which would need to be overcome in order to achieve this idea. We present a rationale for the requirement of an improved household utility delivery infrastructure, and discuss the inspirational analogy; the technological components required to realise the vignette are also discussed. We identify the most significant advance requirement for the proposal to succeed: the utilisation of solid or liquid substrate materials, delivered through water pipelines; their benefits and risks are discussed.
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    Filter clogging data collection for prognostics
    (2013-12) Eker, Ömer Faruk; Camci, Fatih; Jennions, Ian K.
    Filtration is a critical process in many industrial systems to obtain the desired level of purification for liquids or gas. Air, fuel, and oil filters are the most common examples in industrial systems. Filter clogging is the main failure mode leading to filter replacement or undesired outcomes such as reduced performance and efficiency or cascading failures. For example, contaminants in fuel (e.g. rust particles, paint chips, dirt involved into fuel while tank is filling, tank moisture rust) may lead to performance reduction in the engine and rapid wear in the pump. Prognostics has potential to avoid cost and increase safety when applied to filters. One of the main challenges of prognostics is the lack of failure degradation data obtained from industrial systems. This paper presents the process of design and building of an experimental rig to obtain prognostics data for filter clogging mechanism and data obtained from the rig. Two types of filters have been used during the accelerated filter clogging and 23 run-to-failure data have been collected. Flow rate and pressure sensors are used for condition monitoring purposes. The filter clogging has been recorded through a camera to evaluate the findings with pressure and flow sensors. The data collected is very promising for development of prognostics methodologies.
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    General support vector representation machine for one-class classification of non-stationary classes
    (Elsevier Science B.V., Amsterdam., 2008-10-31T00:00:00Z) Camci, Fatih; Chinnam, R. B.
    Novelty detection, also referred to as one-class classification, is the process of detecting 'abnormal' behavior in a system by learning the 'normal' behavior. Novelty detection has been of particular interest to researchers in domains where it is difficult or expensive to find examples of abnormal behavior (such as in medical/equipment diagnosis and IT network surveillance). Effective representation of normal data is of primary interest in pursuing one-class classification. While the literature offers several methods for one-class classification, very few methods can support representation of non-stationary classes without making stringent assumptions about the class distribution. This paper proposes a one-class classification method for non-stationary classes using a modified support vector machine and an efficient online version for reducing computational time. The presented method is applied to several simulated datasets and actual data from a drilling machine. In addition, we present comparison results with other methods that demonstrate its superior performance. (C) 2008 Elsevier Ltd. All rights reserved.
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    Health-state estimation and prognostics in machining processes
    (IEEE, 2010-07-02T00:00:00Z) Camci, Fatih; Chinnam, R. B.
    Failure mechanisms of electromechanical systems usually involve several degraded health-states. Tracking and forecasting the evolution of health-states and impending failures, in the form of remaining-useful-life (RUL), is a critical challenge and regarded as the Achilles' heel of condition-based-maintenance (CBM). This paper demonstrates how this difficult problem can be addressed through Hidden Markov models (HMMs) that are able to estimate unobservable health-states using observable sensor signals. In particular, implementation of HMM based models as dynamic Bayesian networks (DBNs) facilitates compact representation as well as additional flexibility with regard to model structure. Both regular HMM pools and hierarchical HMMs are employed here to estimate online the health-state of drill-bits as they deteriorate with use on a CNC drilling machine. Hierarchical HMM is composed of sub-HMMs in a pyramid structure, providing functionality beyond an HMM for modeling complex systems. In the case of regular HMMs, each HMM within the pool competes to represent a distinct health-state and adapts through competitive learning. In the case of hierarchical HMMs, health-states are represented as distinct nodes at the top of the hierarchy. Monte Carlo simulation, with state transition probabilities derived from a hierarchical HMM, is employed for RUL estimation. Detailed results on health-state and RUL estimation are very promising and are reported in this paper. Hierarchical HMMs seem to be particularly effective and efficient and outperform other HMM methods from literature.
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    A Hybrid Prognostic Methodology and its Application to Well-Controlled Engineering Systems
    (Cranfield University, 2015-01) Eker, Ömer Faruk; Camci, Fatih; Jennions, Ian K.
    This thesis presents a novel hybrid prognostic methodology, integrating physics-based and data-driven prognostic models, to enhance the prognostic accuracy, robustness, and applicability. The presented prognostic methodology integrates the short-term predictions of a physics-based model with the longer term projection of a similarity-based data-driven model, to obtain remaining useful life estimations. The hybrid prognostic methodology has been applied on specific components of two different engineering systems, one which represents accelerated, and the other a nominal degradation process. Clogged filter and fatigue crack propagation failure cases are selected as case studies. An experimental rig has been developed to investigate the accelerated clogging phenomena whereas the publicly available Virkler fatigue crack propagation dataset is chosen after an extensive literature search and dataset analysis. The filter clogging experimental rig is designed to obtain reproducible filter clogging data under different operational profiles. This data is thought to be a good benchmark dataset for prognostic models. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. This comparison has been based on the most recent prognostic evaluation metrics. The results show that the presented methodology improves accuracy, robustness and applicability. The work contained herein is therefore expected to contribute to scientific knowledge as well as industrial technology development.
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    A new hybrid prognostic methodology
    (Prognostics and Health Management Society, 2019-03-08) Eker, Ömer Faruk; Camci, Fatih; Jennions, Ian K.
    Methodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications this is very rarely the case, and hence the chosen method’s performance can deteriorate quite markedly from optimal. For this reason a hybrid methodology, merging physics-based and data-driven approaches, has been developed and is reported here. Most, if not all, hybrid methods apply physics-based and data-driven approaches in different steps of the prognostics process (i.e. state estimation and state forecasting). The presented technique combines both methods in forecasting, and integrates the short-term prediction of a physics-based model with the longer term projection of a similarity-based data-driven model, to obtain remaining useful life estimation. The proposed hybrid prognostic methodology has been tested on two engineering datasets, one for crack growth and the other for filter clogging. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability, especially in the case of minimal data being available.
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    Robust kernel distance multivariate control chart using support vector principles
    (Taylor & Francis, 2012-01-24) Camci, Fatih; Chinnam, R. B.; Ellis, R. D.
    It is important to monitor manufacturing processes in order to improve product quality and reduce production cost. Statistical Process Control (SPC) is the most commonly used method for process monitoring, in particular making distinctions between variations attributed to normal process variability to those caused by ‘special causes’. Most SPC and multivariate SPC (MSPC) methods are parametric in that they make assumptions about the distributional properties and autocorrelation structure of in-control process parameters, and, if satisfied, are effective in managing false alarms/-positives and false- negatives. However, when processes do not satisfy these assumptions, the effectiveness of SPC methods is compromised. Several non-parametric control charts based on sequential ranks of data depth measures have been proposed in the literature, but their development and implementation have been rather slow in industrial process control. Several non-parametric control charts based on machine learning principles have also been proposed in the literature to overcome some of these limitations. However, unlike conventional SPC methods, these non-parametric methods require event data from each out-of-control process state for effective model building. The paper presents a new non-parametric multivariate control chart based on kernel distance that overcomes these limitations by employing the notion of one-class classification based on support vector principles. The chart is non-parametric in that it makes no assumptions regarding the data probability density and only requires ‘normal’ or in-control data for effective representation of an in-control process. It does, however, make an explicit provision to incorporate any available data from out-of-control process states. Experimental evaluation on a variety of benchmarking datasets suggests that the proposed chart is effective for process mon
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    A simple state-based prognostic model for railway turnout systems
    (IEEE Institute of Electrical and Electronics, 2010-05-31T00:00:00Z) Eker, Ömer Faruk; Camci, Fatih; Guclu, Adem; Yilboga, Halis; Sevkli, Mehmet; Baskan, Saim
    The importance of railway transportation has been increasing in the world. Considering the current and future estimates of high cargo and passenger transportation volume in railways, prevention or reduction of delays due to any failure is becoming ever more crucial. Railway turnout systems are one of the most critical pieces of equipment in railway infrastructure. When incipient failures occur, they mostly progress slowly from the fault free to the failure state. Although studies focusing on the identification of possible failures in railway turnout systems exist in the literature, neither the detection nor forecasting of failure progression has been reported. This paper presents a simple state-based prognostic method that aims to detect and forecast failure progression in electro-mechanical systems. The method is compared with Hidden Markov Model based methods on real data collected from a railway turnout system. Obtaining statistically sufficient failure progression samples is difficult considering that the natural progression of failures in electro-mechanical systems may take years. In addition, validating the classification model is difficult when the degradation is not observable. Data collection and model validation strategies for failure progression are also presented.