Abstract
Predictive maintenance needs to forecast the numbers of rejections at any overhaul point before any failure occurs in order to accurately and proactively take adequate maintenance action. In healthcare, prediction has been applied to foretell when and how to administer medication to improve the health condition of the patient. The same is true for maintenance where the application of prognostics can help make better decisions. In this paper, an overview of prognostic maintenance strategies is presented. The proposed data-driven prognostics approach employs a statistical technique of (i) the parameter estimation methods of the time-to-failure data to predict the relevant statistical model parameters and (ii) prognostics modelling incorporating the reliability Weibull Cumulative Distribution Function to predict part rejection, replacement, and reuse. The analysis of the modelling uses synthetic data validated by industry domain experts. The outcome of the prediction can further proffer solution to designers, manufacturers and operators of industrial product-service systems. The novelty in this paper is the development of the through-life performance approach. The approach ascertains when the system needs to undergo maintenance, repair and overhaul before failure occurs.
Highlights
Through-life Engineering Services (TES) aligns MRO function with the operations strategy of an organisation
The most common prognostic approaches to demonstrate the effectiveness of the predictive maintenance modelling include physical model, data-driven, knowledge and hybrid [3]–[6]
In [3], a review of machinery diagnostics and prognostics, implementing condition-based maintenance shows that statistical, artificial intelligence and model-based prognostic approaches can be used to estimate life with improved accuracy
Summary
Through-life Engineering Services (TES) aligns MRO function with the operations strategy of an organisation. Application of prognostics approaches in the modelling and simulation of gas turbine engine mechanical components for an assembly can give a better understanding of the behaviour of a system. The contribution in this paper is applying data-driven model to develop a through-life performance approach that establishes when an asset should be ready for detailed maintenance, repair and overhaul before failure occurs. The most common prognostic approaches to demonstrate the effectiveness of the predictive maintenance modelling include physical model, data-driven, knowledge and hybrid [3]–[6]. The focus of this article is to develop a predictive maintenance strategy applicable to system reliability in the manufacturing, aerospace gas turbine, and other domains relative to concurrent system operations. The remainder of this paper includes maintenance strategies, data-driven prognostics maintenance strategy, case study, results and discussion, and conclusion
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
