Research Article Health Indicator for Predictive Maintenance Based on Fuzzy CognitiveMaps,GreyWolf,andK-NearestNeighborsAlgorithms G.Mazzuto ,S.Antomarioni ,F.E.Ciarapica ,andM.Bevilacqua Dipartimento Ingegneria Industriale e Scienze Matematiche, Universit` a Politecnica Delle Marche, Via Brecce Bianche, Ancona 60131, Italy Correspondence should be addressed to G. Mazzuto; g.mazzuto@univpm.it Received 22 September 2020; Revised 8 October 2020; Accepted 20 January 2021; Published 17 February 2021 Academic Editor: Filippo De Carlo Copyright©2021G.Mazzutoetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An essential step in the implementation of predictive maintenance involves the health state analysis of productive equipment in ordertoprovidecompanymanagerswithperformanceanddegradationindicatorswhichhelptopredictcomponentcondition.In this paper, a supervised approach for health indicator calculation is provided combining the Grey Wolf Optimisation method, Swarm Intelligence algorithm, and Fuzzy Cognitive Maps. e k-neighbors algorithms is used to predict the Remaining Useful Lifeofanitem,since,inadditiontoitssimplicity,theyproducegoodresultsinalargenumberofdomains.eapproachaimsto solvetheproblemthatfrequentlyoccursininterpolationprocedures:theapproximationoffunctionsbelongingtoachosenclass of functions of which we have no knowledge. e proposed algorithm allows maintenance managers to distinguish different degradationprofilesindepthwithaconsequentlymorepreciseestimateoftheRemainingUsefulLifeofanitemand,inaddition, an in-depth understanding of the degradation process. Specifically, in order to show its suitability for predictive maintenance, a dataset on NASA aircraft engines has been used and results have been compared to those obtained with a neural network approach. Results highlight how all of the degradation profiles, obtained using the proposed approach, are modelled in a more detailedmanner,allowingonetosignificantlydistinguishdifferentsituations.Moreover,thephysicalcorespeedandthecorrected fan speed have been identified as the main critical factors to the engine degradation. 1.Introduction Although predictive maintenance practices have existed for many years, only recently, thanks to the emerging Industry 4.0 technologies with increasingly reliable and affordable smart systems, it has become widely accessible [1]. It has severaladvantages,includingmachinelifeincreaseby3–5%, reduced maintenance costs by up to 40%, and returns on investment up to 10 times [2]. Oneofthemostrelevantstepsinthepredictionprocess is the choice of the best approach for the item behaviour assessment, such as data-driven or model-driven approach [3]. In particular, according to the platform developed by Pateletal.[4]fortheapplicationofIndustry4.0principlesto the industrial reality, the data-analytic layer is crucial to understand a plant functioning. Moreover, if properly designed,itallowsuserstoidentifythepresenceofinvisible relationsamongdataprovidedbytheapplicationlayer[5].It isalsotruethat,accordingtothe“nofreelunch”theorems,a standard procedure for predictive maintenance does not exist. Still, it must be chosen among those that best suit the reality under analysis [6]. In any case, regardless of the adoptedprocess,foramoreaccurateandoptimalprediction, it is necessary to gather and analyse appropriately large amountsofdatawithinatimeframe[7,8]withconsequent problems deriving from the identification of the most ac- curate health indicators. e health of a system can be definedasthedeviationordegradationofanitembehaviour from its regular operating performance [9]. e calculation of a suitable health indicator (HI) is fundamental to establish a link between the deviation or degradationofanitemanditsRemainingUsefulLife(RUL). us, an accurate HI is a key for a more precise prediction tool, guaranteeing its reproducibility [10, 11]. is Hindawi Mathematical Problems in Engineering Volume 2021, Article ID 8832011, 21 pages https://doi.org/10.1155/2021/8832011