# Analysis of Uncertainty in Conveyor Belt Condition Assessment Using Time-Based Indicators
## 2 Materials and Methods
Since the inspected belts operate at a comparable speed of  
Figure 3: Violin plot of belt age distribution by transported material. Figure 2: Violin plot of segment length distribution by transported material. approximately 6 m/s, the effective sampling interval along the belt is 15 mm. The sensor head consists of 90 coils spaced every 25 mm across the belt width, which is either 1.8 m or 2.25 m. This yields 72 or 90 transverse sampling channels, respectively. For every meter of belt length, approximately 67 samples are recorded per channel. Consequently, a single full scan of a 2 km long belt generates approximately 12 million data points, structured as a matrix of 134,000 longitudinal rows (each covering 15 mm) and 90 columns (one per coil), used to map and quantify internal damage signals. The collected data form a 2D map of core defects along the belt length and width. Figure 5 shows an example of such imaging at three different time points. Figure 4: Installation diagram of the diagnostic head on a conveyor belt. Figure 5: Two-dimensional representation of belt core-defect signals. The software automatically divides the belt into sections (between joints), counts detected defects per section, and computes their total area. Quantitative belt-condition metrics--such as defect density (defects per length) and total defect area per meter--are derived. Results are displayed as color-coded maps and defect profiles, enabling quick identification of high-damage regions. Figure 6 provides an example of a color-coded condition map for a scanned belt loop. Individual belt segments are represented as rectangles, with lengths proportional to the actual segment lengths, and colors indicating the severity of internal damage. The color scale is progressive--from green (low signal density, good technical condition), through yellow and orange (moderate damage levels), to red and dark brown, which mark significant core deterioration and a potential risk of failure. This visual representation enables rapid identification of belt sections that may require closer monitoring or maintenance intervention. DiagBelt+ was designed for high-throughput diagnostics of belt-conveyor loops in open-pit mining, where operational continuity is critical. The use of non-destructive diagnostics allows regular, rapid assessment of belt-core condition during operation, significantly reducing the risk of operational failures. Through advanced data analysis algorithms, the system automatically counts defects and provides clear quantitative condition scores. This information enables maintenance teams to effectively plan repairs, reconditioning, or segment replacement at the optimal time, minimizing downtime costs and preventing serious breakdowns [31]. The implementation of DiagBelt+ in mines (e.g., Belchatow) confirmed its practical utility--diagnostic data supported analysis of defect-growth trends over time and modeling of remaining error-free operational time. Such diagnostics greatly reduce unplanned downtime and streamline belt asset management, enhancing conveyor safety and continuity, and yielding substantial cost savings through extended belt service life compared to conventional visual inspection schedules [32]. In addition to absolute belt age, a time-based indicator known as Resurs (percentage of expected operating time) was used to represent the relative level of belt usage. Resurs is defined as the ratio of the belt segment's age (in months) to its expected service life, based on manufacturer specifications or operational experience from similar conveyor systems. Expressed as a percentage, it enables normalization of usage across belts of different lengths, installation dates, and operating conditions. While strongly correlated with age, Resurs provides a more contextualized view of expected wear, particularly useful in comparative studies of degradation rates.