August 5, 2025Open Access

The Development of a Predictive Maintenance System for Gearboxes Through a Statistical Diagnostic Analysis of Lubricating Oil and Artificial Intelligence

DRDiego RigolliRoma Tre University LPLorenzo PompeiUniversity of Calabria MMMassimo ManfrediniUniversity of Calabria

Key Points

The integration of artificial intelligence enhances predictive maintenance in gearboxes through oil diagnostics.
Statistical analysis methods, including FT-IR and viscosity measurements, inform condition assessments.
The approach tested on gearbox data aligns AI results with expert evaluations and reduces human error.
Implementation of this system promotes consistent reporting and improves overall maintenance practices.

Abstract

This paper addressed the problem of oil diagnostics lubricants applied to the predictive maintenance of industrial gearboxes, proposing the integration of an artificial intelligence (AI) system into the process analysis. The main objective was to overcome the critical issues of the traditional method, characterized by long analysis times and a marked dependence on the subjective interpretation of operators. The method includes a detailed statistical analysis of the common ways to assess the condition of lubricants, such as optical emission spectroscopy, particle counting, measuring viscosity and density, and Fourier-transform infrared spectroscopy (FT-IR). These methods are then combined with an artificial intelligence model. Tested on commercial gearbox data, the proposed approach demonstrates agreement between IA and expert evaluation. The application has shown that it can effectively support diagnoses, reduce processing time by 60%, and minimize human errors. It also improves knowledge sharing through an increase in the stability and repetitiveness of diagnoses and promotes consistency and clarity in reporting.

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