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Analysis of institutional authors

Sanz, RicardoAuthor

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June 9, 2019
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Data-driven prognostics using a combination of constrained K-means clustering, fuzzy modeling and LOF-based score

Publicated to: NEUROCOMPUTING. 241 97-107 - 2017-06-07 241(), DOI: 10.1016/j.neucom.2017.02.024

Authors:

Diez-Olivan, A; Pagan, JA; Sanz, R; Sierra, B
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Affiliations

Diesel Engine Factory Diagnose Engn & Product Dev - Author
Tecnalia Res & Innovat Ind Syst Unit - Author
Univ Basque Country, Dept Comp Sci & Artificial Intelligence - Author
Univ Politecn Madrid, Autonomous Syst Lab - Author
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Abstract

Today, failure modes characterization and early detection is a key issue in complex assets. This is due to the negative impact of corrective operations and the conservative strategies usually put in practice, focused on preventive maintenance. In this paper anomaly detection issue is addressed in new monitoring sensor data by characterizing and modeling operational behaviors. The learning framework is performed on the basis of a machine learning approach that combines constrained K-means clustering for outlier detection and fuzzy modeling of distances to normality. A final score is also calculated over time, considering the membership degree to resulting fuzzy sets and a local outlier factor. Proposed solution is deployed in a CBM+ platform for online monitoring of the assets. In order to show the validity of the approach, experiments have been conducted on real operational faults in an auxiliary marine diesel engine. Experimental results show a fully comprehensive yet accurate prognostics approach, improving detection capabilities and knowledge management. The performance achieved is quite high (precision, sensitivity and specificity above 93% and K = 0.93), even more so given that a very small percentage of real faults are present in data. (C) 2017 Elsevier B.V. All rights reserved.
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Keywords

Behavior characterizationCondition monitoringConstrained k-means clusteringFault-diagnosisFuzzy modelingLocal outlier factorMachine learning

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal NEUROCOMPUTING due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2017, it was in position 27/132, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.32. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 1.48 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-24, the following number of citations:

  • WoS: 44
  • Scopus: 54
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-24:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 114.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 111 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 3.

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/46796/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 489
  • Downloads: 651
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