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Analysis of institutional authors

Antonino-Daviu, JaCorresponding Author

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October 28, 2024
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Gear wear detection based on statistic features and heuristic scheme by using data fusion of current and vibration signals

Publicated to: Energies. 16 (2): 948- - 2023-01-01 16(2), DOI: 10.3390/en16020948

Authors:

Jaen-Cuellar, Arturo Yosimar ; Trejo-Hernández, Miguel ; Osornio-Rios, Roque Alfredo; Antonino-Daviu, J.
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Affiliations

Univ Autonoma Queretaro, Fac Ingn, HSPdigital CA Mecatron, Campus San Juan del Rio,Rio Moctezuma 249,Col San - Author
Univ Politecn Valencia UPV, Inst Tecnol Energia, Camino Vera S-N - Author

Abstract

Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because this machine is the core that provides the power and generates the motion of the industrial process. However, also, it is possible to diagnose other types of faults that occur in other elements in the kinematic chain, which are reflected as problems in the motor operation. With this purpose, the coupling between the motor and the final load in the chain requires, in many situations, the use of a gearbox that balances the torque-velocity relationship. Thus, the gear wear in this component is addressed in many works, but the study of gradual wear has not been completely covered yet at different operating frequencies. Therefore, in this work, a methodology is proposed based on statistical features and genetic algorithms to find out those features that can best be used for detecting the gradual gear wear of a gearbox by using the signals, measured directly in the motor, from current and vibration sensors at different frequencies. The methodology also makes use of linear discriminant analysis to generate a bidimensional representation of the system conditions that are fed to a neural network with a simple structure for performing the classification of the condition. Four uniform gear wear conditions were tested, including the healthy state and three gradual conditions: 25%, 50%, and 75% wear in the gear teeth. Because of the sampling frequency, the number of sensors, the time for data acquisition, the different operation frequencies analyzed, and the computation of the different statistical features, meant that a large amount of data were generated that needed to be fused and reduced. Therefore, the proposed methodology provides an excellent generalized solution for data fusion and for minimizing the computational burden required. The obtained results demonstrate the effectiveness of fault gradualism detection for the proposed approach.
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Keywords

Artificial intelligenceConditionData acquisitionData fusionDiagnosisDiscriminant analysisElectrical machineFault detectionFault-diagnosisFaults detectionFeature extractionGear teethGear wearGenetic algorithmGenetic algorithmsInduction motorsInduction-motorsInductions motorsIndustrial motorIndustrial motorsIndustrial processsKinematic chainKinematicsSamplingStatistical featuresVibrations (mechanical)Wear detectionWear of materials

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Energies due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering (Miscellaneous).

From a relative perspective, and based on the normalized impact indicator calculated from World Citations from Scopus Elsevier, it yields a value for the Field-Weighted Citation Impact from the Scopus agency: 1.32, which 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)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-03, the following number of citations:

  • WoS: 9
  • Scopus: 12
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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 2026-04-03:

  • 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: 24 (PlumX).

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

    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.
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    Leadership analysis of institutional authors

    This work has been carried out with international collaboration, specifically with researchers from: Mexico; Switzerland.

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (Antonino Daviu, José Alfonso).

    the author responsible for correspondence tasks has been Antonino Daviu, José Alfonso.

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