Sensor-Based Channel-Wise Remaining Useful Life Estimation and Multi-Threshold Maintenance Policy for Steel-Cord Conveyor Belts.

Authors

Ryszard Błażej, Leszek Jurdziak, Aleksandra Rzeszowska

Published in

Sensors (Basel, Switzerland). Volume 26. Issue 15. Aug 05, 2026. Epub Aug 05, 2026.

Abstract

Steel-cord conveyor belts are usually assessed using average damage indicators calculated for entire belt sections. Although such indicators are useful for general condition assessment, they do not adequately represent spatially non-uniform degradation across the belt width. This paper proposes a sensor-based, channel-wise framework for condition assessment, remaining useful life (RUL) estimation, and maintenance decision support for steel-cord conveyor belts. The approach uses data from consecutive non-destructive diagnostic scans, in which the belt cross-section is represented as a set of active measurement channels. For each channel, local damage density is treated as a health indicator evolving over time. A simple accelerating degradation model is then used to estimate local degradation intensity and channel-wise RUL. Three decision thresholds are introduced to represent entry into the refurbishment window, the end of economically justified refurbishment, and the critical removal condition. In addition to channel-wise RUL, the framework includes cross-sectional indicators such as the fraction of channels exceeding each threshold, the width of compact critical zones, and life-uniformity measures describing the spatial distribution of degradation. A cost-oriented interpretation is also introduced by linking non-uniform cross-sectional degradation with reduced life utilization and increased cost per unit operating time. The applicability of the framework is illustrated using consecutive diagnostic scans from an industrial conveyor belt loop. The results show that localized degradation may govern belt replacement earlier than average indicators suggest. The proposed approach provides a practical basis for condition-based maintenance, refurbishment planning, and future integration with loading-condition analysis and digital-twin-oriented diagnostics.

PMID:
42590719
Bibliographic data and abstract were imported from PubMed on 13 Aug 2026.

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