# Analysis of Uncertainty in Conveyor Belt Condition Assessment Using Time-Based Indicators
## 4 Conclusions  
This study analyzed the condition of conveyor belt segments across different material types using statistical and regression analyses. The key conclusions include:  
1. Material Type Impact: \(\circ\) Age and Damage Density vary significantly with material type, while Resurs shows limited sensitivity. \(\circ\) The mixture of brown coal and overburden behaves similarly to both parent materials in terms of Resurs, suggesting it can be treated flexibly in modeling. 2. Predictive Modeling: \(\circ\) A multiple linear regression model was developed to estimate Damage Density based on measurable variables: Resurs, Loop Length, and Section Length. 3. The final model (Model 2) explains approximately 46% of the variability in damage density and includes only statistically significant predictors. The variable Age was excluded due to its strong correlation with Loop Length (PPMC = 0.79), which helped reduce multicollinearity. Model Limitations and Residual Analysis:  
Figure 14: Plot of Density with predicted values. Figure 13: Component Effects Plot—a portion of the estimated regression model for Density with the residuals. The Durbin-Watson statistic for Model 2 was 0.570, indicating positive auto-correlation in residuals. This effect is likely caused by the data structure, in which observations were grouped by conveyor loop and belt segment. A group of seven outliers with Studentized residuals \(\geq\) 3 was identified. These segments exhibited very high wear (Resurs > 0.8) and were located in long loops (1880 m) used in overburden transport. Although these observations affect model fit, they represent real and critical wear conditions. Operational Implications:  
Predictive tools based on Resurs and geometry parameters could assist in maintenance planning and reliability assessment. However, due to moderate predictive power, further enhancement through non-linear modeling or inclusion of categorical variables (e.g., conveyor type or operational intensity) could be beneficial. Future Work:  
Investigate advanced regression methods (e.g., ridge, LASSO, or non-linear models). Incorporate categorical or operational variables for improved accuracy. Validate models using independent datasets or real-time monitoring systems. Main Conclusions  
The study demonstrates that predicting the condition of conveyor belt segments based solely on operational data--such as Resurs, Age, Loop Length, and Section Length--explains only approximately 46% of the variability in Damage Density. This leaves 54% of the variability unexplained, attributed to factors not captured in standard operational databases (such as material fatigue, micro-damages, or local stress concentrations). The past (before the implementation of the DiagBelt diagnostic system), which relied primarily on visual inspection and calculations based on Resurs, was limited in accuracy and could result in up to 56% uncertainty when assessing the real condition of belt segments. While Resurs is a useful indicator, it cannot fully account for the complex and heterogeneous wear patterns occurring across different conveyor systems. Therefore, the most effective and reliable approach is the application of a non-invasive diagnostic tool, such as belt scanning technology. These systems can directly measure belt condition and detect early signs of wear or damage that are invisible to the eye or not reflected in calculated indicators like Resurs. In fact, the residual structure of the regression model revealed clear clusters of segments with excessive wear that would not have been detected using Age or Resurs alone. Such diagnostic tools offer a substantial improvement in precision, reduce the risk of unexpected failures, and support more informed maintenance decisions. In conclusion, while data-driven modeling provides some predictive value, direct condition monitoring through non-invasive diagnostics is essential for achieving accurate, real-time assessment of conveyor belt health and ensuring operational reliability.