Leszek Jurdziak

Conveyor belt systems are commonly treated as industrial equipment, although their operational value, degradation, risk, and renewal potential are distributed along the route and evolve through identifiable belt sections, splices, inspections, repairs, inserts, and refurbishment cycles. This article redefines conveyor belt loops as digital twin-ready linear assets and proposes a transferable asset management framework integrating three coupled layers: performance and physics, condition data and belt passport, and renewal decisions. The study is designed as a conceptual engineering article based on targeted literature synthesis, structured cross-sector analogy and framework development, rather than as a bibliometric review or a new optimization model. The proposed framework builds on previous work on structure-aware segment renewal by positioning it within a broader data and governance architecture. The article shows that a conveyor digital twin becomes operationally meaningful only when physics-based interpretation, spatially anchored diagnostics, intervention history, operating context, residual value, and auditable decision rules are connected through a persistent belt passport. The framework supports more transparent life-cycle decisions and positions conveyor belt loops as a reference case for infrastructure asset management, condition traceability, and digital twin governance.

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.

Conveyor belts constitute critical components of bulk material handling systems, and their reliable operation directly affects process continuity, operational safety, and maintenance costs in industrial environments. Increasing requirements regarding system reliability and predictive maintenance have stimulated the development of advanced diagnostic methods for conveyor belt condition monitoring. This review presents a comprehensive analysis of conveyor belt damage detection and diagnostic approaches, with particular emphasis on sensing technologies and signal-based methodologies. The paper discusses major conveyor belt degradation mechanisms and analyzes their representation in diagnostic data obtained using different sensing modalities. Current developments in machine vision systems, magnetic methods based on magnetic flux leakage, ultrasonic techniques, and X-ray imaging are critically reviewed together with signal preprocessing procedures, feature extraction strategies, and damage classification approaches. Particular attention is devoted to the transition from conventional signal processing techniques toward machine learning and deep learning methods enabling automated feature representation and fault identification. The analysis indicates that despite substantial progress in sensing technologies and artificial intelligence, most existing solutions remain strongly sensor-specific and limited to individual data modalities. Key research gaps include the lack of unified damage representation frameworks, limited benchmark datasets, and the insufficient integration of multimodal sensing information. Future progress will likely depend on the development of integrated diagnostic ecosystems combining heterogeneous sensing technologies, advanced feature representation methods, and intelligent decision-support systems.

Linear assets (LAs), such as conveyor systems, road networks, pipelines, and power transmission lines, are typically maintained through localized, segment-based interventions. While such approaches effectively address spatially heterogeneous degradation, they often neglect the system-level consequences of repeated local actions. In particular, improvements in segment condition may be accompanied by increased structural complexity, leading to reduced reliability and higher lifecycle costs. This paper proposes a unified engineering framework that integrates segment-level condition assessment with system-level structural effects. The framework is based on a dual representation of asset condition, distinguishing between material state (MS) and structural state (SS), which correspond to material aging (MA) and structural aging (SA), respectively. A key contribution is the introduction of the fragmentation penalty (FP), capturing the negative impact of increasing segmentation and interface density on system performance. The framework incorporates multi-threshold decision logic, enabling differentiation between operational, refurbishment, and replacement regimes, and interprets maintenance actions as transformations affecting both condition and structure. A formal model is developed to represent the asset as a dynamic system of segments and interfaces. It provides a basis for future empirical calibration and structure-aware optimization. Although the model is developed using conveyor belt loops as a reference case, its broader relevance is discussed for other classes of linear assets with repeated local intervention and evolving structural heterogeneity. A simple worked example is included to demonstrate the operational meaning of the proposed fragmentation-aware perspective. The results show that maintenance decisions may change when structural side effects are considered together with local condition improvement, and they provide a basis for future empirical calibration and structure-aware optimization of maintenance strategies.

Conveyor belts are among the most critical components of material transport systems across various industrial sectors, including mining, energy, cement production, metallurgy, and logistics. Their reliability directly affects the continuity and operational costs. Traditional methods for assessing belt condition often require downtime, are labor-intensive, and involve a degree of subjectivity. In recent years, there has been a growing interest in non-destructive and remote diagnostic techniques that enable continuous and automated condition monitoring. This paper provides a comprehensive review of current diagnostic solutions, including machine vision systems, infrared thermography, ultrasonic and acoustic techniques, magnetic inspection methods, vibration sensors, and modern approaches based on radar and hyperspectral imaging. Particular attention is paid to the integration of measurement systems with artificial intelligence algorithms for automated damage detection, classification, and failure prediction. The advantages and limitations of each method are discussed, along with the perspectives for future development, such as digital twin concepts and predictive maintenance. The review aims to present recent trends in non-invasive diagnostics of conveyor belts using remote and non-destructive testing techniques, and to identify research directions that can enhance the reliability and efficiency of industrial transport systems.

Reducing energy consumption in belt conveyor systems is critical to improving the overall energy efficiency of lignite mining operations. This study presents a theoretical and empirical analysis of energy use in overburden and coal conveyors, with a focus on balancing the relationship between belt speed and load. Building on the theory of conveyor motion resistance, the energy consumption index (WskZE)—previously introduced by the authors—is revisited as a function of two key variables: belt speed (v) and real-time material flow rate (Q_r). Empirical validation was conducted using operational data from variable-speed conveyors in the Konin lignite mine and compared to similar-length conveyors in the Bełchatów mine. Energy consumption measurements allowed for the analysis of energy consumption for two different scenarios: (i) in the Bełchatów mine the belt speed was constant and the excavator capacity was variable and (ii) in the Konin mine the excavator capacity was kept constant and the conveyor belt speed was varied. The results confirm that WskZE is linearly dependent on belt speed and inversely proportional to throughput, as predicted by theoretical models. However, findings also show that lowering belt speed—while effective in reducing energy use—results in a higher proportion of power being consumed to move the belt and heavy idlers, especially when these components are sized for peak loads. This study suggests a revised conveyor design philosophy (a new paradigm) that emphasizes maximizing the mass ratio of transported material to moving components. Additionally, it recommends integrating real-time monitoring of energy performance indicators into mine control systems to enable energy-aware operational decisions.

Reducing energy consumption in belt conveyor systems is critical to improving the overall energy efficiency of lignite mining operations. This study presents a theoretical and empirical analysis of energy use in overburden and coal conveyors, with a focus on balancing the relationship between belt speed and load. Building on the theory of conveyor motion resistance, the Energy Consumption Index (WskZE)—previously introduced by the authors—is revisited as a function of two key variables: belt speed (v) and real-time material flow rate (Qr). Empirical validation was conducted using operational data from variable-speed conveyors in the Konin lignite mine and compared to similar-length conveyors in the Belchatów mine. In Konin, tests were performed under two operational scenarios: (i) constant belt speed with varying excavator output, and (ii) constant output with varying belt speed. The results confirm that WskZE is linearly dependent on belt speed and inversely proportional to throughput, as predicted by theoretical models. However, findings also show that lowering belt speed—while effective in reducing energy use—results in a higher proportion of power being consumed to move the belt and heavy idlers, especially when these components are sized for peak loads. The study suggests a revised conveyor design philosophy (a new paradigm) that emphasizes maximizing the mass ratio of transported material to moving components. Additionally, it recommends integrating real-time monitoring of energy performance indicators into mine control systems to enable energy-aware operational decisions.

Non-invasive methods for diagnosing conveyor belts enable effective detection of damage, significantly reducing the costs associated with belt replacement. Additionally, they allow for continuous monitoring of the belts’ technical condition and degree of wear over extended periods of operation. Such solutions also enhance safety in environments where conveyor systems are used. While belt wear is an inevitable process, its rate can vary depending on specific operational conditions, such as the conveyor’s location, its length, the type of material being transported, and the belt’s operating speed. This article discusses an artificial intelligence-based approach to classifying conveyor belt damage. A two-layer neural network was implemented in the MATLAB environment using the Deep Learning Toolbox. By optimizing the network, a high level of operational efficiency was achieved, reaching an accuracy range of 80–90%. This solution opens new possibilities for precise diagnostics and monitoring of conveyor belts’ technical state, contributing to improved durability and reduced operational costs.

This study analyzes the impact of the type of transported material (overburden, lignite, mixture) on the rate of core damage accumulation in Type St conveyor belts in open-pit mines. The research was conducted using the DiagBelt+ diagnostic system, which enables the assessment of belt core condition without dismantling the belt. Data were collected from over 100 conveyor belt loops, covering segments of varying lengths, ages, and operational histories. Damage density and area were assessed, and differences were analyzed depending on the material type. The results indicate that belt age and damage density vary significantly with material type, while the Resurs indicator (percentage of expected operating time) shows no clear dependence on the material type. A multiple regression analysis was also performed to predict failure density based on operational variables, such as Age, Resurs results, Loop Length, and Segment Length. The regression model explains approximately 46% of the variability in damage density, indicating the need for further research to improve predictive accuracy. The study emphasizes the importance of using non-destructive diagnostic systems to optimize maintenance planning and enhance conveyor belt reliability.