ORCID Profile
0000-0001-7278-4623
Current Organisation
University of the Arts London
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Publisher: SAGE Publications
Date: 02-2008
Abstract: Managing asset integrity is crucial for the cost-effective asset management of processing plants. Consequently, new techniques for detection and classification of incipient faults are continuously being sought by industry and developed by research and development professionals, both corporate and academic. Although acoustic emission (AE) testing for faults in static equipment has been used since the 1970s, its use as a monitoring technology for various machinery conditions has been poorly adopted by industry, despite a significant volume of work having been published over the past twenty years describing success in detecting numerous rotating and reciprocating machinery faults. Anecdotal evidence from industry suggests that many ‘tried and failed’. The authors believe that this is because applying AE monitoring to industrial plant is fraught with poorly documented challenges, obstacles, and limitations that must be well understood and overcome before any reported results can be replicated. Thus, to enable potential users to approach AE monitoring with more realistic expectations, the current paper discusses several such problems and suggests some available techniques for their management. After an initial introduction on the basics of AE specifically applied to machinery monitoring, issues are ided into generic problems and application-specific challenges.
Publisher: Elsevier BV
Date: 03-2024
Publisher: MDPI AG
Date: 15-07-2019
DOI: 10.3390/EN12142705
Abstract: Components of rotating machines, such as shafts, bearings and gears are subject to performance degradation, which if left unattended could lead to failure or breakdown of the entire system. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes a combination of two supervised machine learning techniques namely, the regression model and multilayer artificial neural network model, to predict the remaining useful life of rolling element bearings. Root mean square and Kurtosis were analyzed to define the bearing failure stages. The proposed methodology was validated through two case studies involving vibration measurements of an operational wind turbine gearbox and a split cylindrical roller bearing in a test rig.
Publisher: Elsevier BV
Date: 11-2022
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for David Mba.