Conceptual Design of Hybrid Algorithms for Early Detection of Mechanical Failures in Conveyor Belt Drive Systems in Mining
DOI:
https://doi.org/10.65093/aci.v16.n3.2025.37Keywords:
early fault detection, conveyor belts, hybrid algorithms, data fusionAbstract
This thesis addresses the conceptual design of a hybrid methodology for the early detection of mechanical failures in the transmission system of a mining conveyor belt. The main objective is to mitigate unscheduled shutdowns and the high operating costs associated. The methodology integrates the analysis of two data sources: vibration and temperature. Through signal preprocessing, the Fast Fourier Transform (FFT) is applied for spectral analysis, and data fusion is performed by extracting the Root Mean Square (RMS) value per frequency band and aligning it with the thermal data. Subsequently, a segmented analysis of the progression of these features is implemented to establish rigorous and quantitative alarm thresholds. These criteria allow for monitoring instability and vibratory energy over time, identifying the incipient state of critical failures (bearings, misalignment). This approach lays the groundwork for a robust predictive system, contributing to maintenance optimization within the context of Mining 4.0.
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