Comparison of Computational Prognostic Methods for Complex Systems Under Dynamic Regimes: A Review of Perspectives
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ORIGINAL PAPER
Comparison of Computational Prognostic Methods for Complex Systems Under Dynamic Regimes: A Review of Perspectives Oguz Bektas1,2 · Jane Marshall1 · Jeffrey A. Jones1 Received: 14 December 2018 / Accepted: 24 April 2019 © The Author(s) 2019
Abstract Complex systems are expected to play a key role in the progress of Prognostics Health Management but the breadth of technologies that will highlight gaps in the dynamic regimes are expected to become more prominent and likely more challenging in the future. The design and implementation of sophisticated computational algorithms have become a critical aspect to solve problems in many prognostic applications for multiple regimes. In addition to a wide variety of conventional computational and cognitive paradigms such as machine learning and data mining fields, specific applications in prognostics have led to a wealth of newly proposed methods and techniques. This paper reviews practices for modeling prognostics and remaining useful life applications in complex systems working under multiple operational regimes. An analysis is provided to compare and combine the findings of previously published studies in the literature, and it assesses the effectiveness of techniques for different stages of prognostic development. The paper concludes with some speculations on the likely advances in fusion of advanced methods for case specific modeling.
1 Introduction In recent years, machinery systems are becoming ever more complex and require new expertise in terms of safety and performance. A complex system is composed of many components that are interrelated and interacting elements forming a complex whole [1]. Condition monitoring of such advanced systems can be used to recognise any potential problems at an early stage and therefore reduce the risk of failures. However, the data are monitored from various sensors of components and their interpretation is often challenging because of the complicated inter-dependencies between monitored information and actual system conditions [2]. Moreover, the operational conditions and regimes force some systems to operate under dynamic operational
* Oguz Bektas [email protected]; [email protected] Jane Marshall [email protected] Jeffrey A. Jones [email protected] 1
Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UK
Ministry of National Education, Ankara, Turkey
2
margins. The health degradation of such systems is not always deterministic, and commonly multi-dimensional [3]. The data streams are monitored from various channels [4] and advanced decision-making processes are required to model the multidimensional health degradation phenomena [5]. The challenges brought up by the complexity of realworld applications should be considered during the development and test of a new prognostic model [6]. The literature has recognised the importance of complexity by proposing different algorithms for the multiple axes of information and multidimensional data. However, there is
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