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Mr Owen C Freeman Gebler

Engineering Doctorate Student, Member Student

Owen Freeman Gebler

Mr Owen C Freeman Gebler

Engineering Doctorate Student, Member Student

Member of

Postgraduate research supervised by

Research interests

Throughout industry maintenance of systems is playing an increasingly central role in supporting operations, driven by increasing efficiency demands, with targets of >90% Overall Equipment Effectiveness (OEE) common. To support the realisation of such stringent targets the emergence of advance maintenance practices has been observed within industry, the most prominent of which being Reliability-centred Maintenance (RCM). RCM defines an approach to maintenance planning through which the most appropriate form of maintenance for each asset within a plant can be identified, whether this be Reactive, Preventative or Predictive [1].

Predictive or On-condition Maintenance (PM) describes an approach in which the condition of an asset is continuously monitored and maintenance only performed when failure onset is identified. This is in contrast to maintenance which is performed in response to failure (Reactive) or at pre-determined intervals (Preventative). The task of predicting the future condition of an asset is referred to as Prognostics and as part of a PM strategy can facilitate a range of benefits to operators, primary amongst which is an increase in operational efficiency through a reduction in process downtime when compared to Reactive and Preventative strategies.

As such a requirement has emerged for algorithms capable of providing accurate estimation of current component condition and prediction of remaining useful life (RUL) to enable the implementation of PM strategies. Research within the field of Prognostics has been driven primarily by the Aerospace sector within the scope of Integrated Vehicle Health Management (IVHM), with focus predominantly confined to prognostics of low volume, high value assets such as turbine engines [2]. This research project aims to translate such knowledge into less technologically sophisticated, general processing sectors such as Materials Handling and Wastewater Treatment and optimise the implementation of prognostics here based upon the specific requirements present.

Alongside development of technical theory specific focus will be given to the mitigation of potential barriers to industrial up-take of such technology which thus far have restricted implementation beyond low volume, high value assets. Primary barriers are centred around cost and ease of implementation therefore this research aims to address both complexity and generality of algorithms to produce an industry ready solution. Additionally current research has been confined to the development of component-level prognostics with little consideration given for the interpretability of prognostic data [3]. By taking a Systems approach to the problem this project aims to produce system-level prognostics data to facilitated simple interpretation and thus encourage utilisation by operators.

Research activities will be supported by the development of a test rig, using which the performance of state of the art algorithms can be evaluated as well as that of novel algorithms. Initial focus will be on prognostics for rotating components of the form employed within a typical conveyor system as operated within a Materials Handling Facility (MRF) to enable validation of system performance prior to industrial implementation. Research output is to ultimately be incorporated into proprietary maintenance software developed by the project industrial sponsor, Stirling Dynamics, and demonstrated within an industrial setting at industrial partner sites.


[1]     J. Moubray, Reliability-centered Maintenance 2.1 (2nd Edition), 2nd ed. Oxford: Butterworth-Heinemann, 1997.

[2]          I. K. Jennions, Integrated Vehicle Health Management: Perspectives on an Emerging Field. SAE International, 2011.

[3]      A. Heng, S. Zhang, A. C. C. Tan, and J. Mathew, “Rotating machinery prognostics: State of the art, challenges and opportunities,” Mech. Syst. Signal Process., vol. 23, no. 3, pp. 724–739, Apr. 2009.

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United Kingdom