## 3 Results
#### 3.1.2 Estimation of Maintainability Parameters  
The replacement times (in minutes) of the Komatsu WA470 front loader bucket knife, obtained from the analyzed database, can be represented by the statistical series consisting of n = 32 values (arranged in ascending order): 3 \(\times\) 30; 4 \(\times\) 40; 7 \(\times\) 50; 12 \(\times\) 60; 70; 2 \(\times\) 80; and 3 \(\times\) 100 (Figure 14a). From the analyzed database, the replacement times of the Komatsu HB365 excavator bucket tooth is obtained, represented by the statistical series consisting of n = 31 values (arranged in ascending order), in minutes: 22 \(\times\) 40; 45; 6 \(\times\) 50; and 2 \(\times\) 90 (Figure 14b). From the analyzed database, the change times of the sorting station trough are obtained, represented by the statistical series consisting of n=18 values (arranged in ascending order), in hours: 30; 4 \(\times\) 35;Analyzing the maintainability variation curve for the Komatsu WA470 front loader bucket knife, it is observed that the probability that the Komatsu WA470 front loader bucket knife will be replaced in 50 min is approximately 40% (Figure 15a), while to have an 80% certainty that this knife will be changed, 60 min are needed (Figure 15b). It can be observed from Figure 15b that for the maintainability parameter at 60 min, the confidence interval is narrower than in Figure 15a, indicating a lower variability in the knife replacement times. From the analysis of the maintainability graphs (Figure 15c,d), it is observed that the probability that the Komatsu HB365 excavator bucket tooth will be replaced in 40 min is only approximately 40%, while to have an 80% certainty that the tooth will be changed, 50 min are needed. Regarding the analysis of the maintainability graphs (Figure 15e,f), it is observed that the probability that the sorting station trough will be replaced in 40 h is only approximately 40%, while to have an 80% certainty that the trough will be changed, 45 h are needed. It is observed that the lognormal distribution law predominates regarding the reliability of the components (knife, tooth, trough), while for maintainability the exponential distribution law is more often used. Based on this information, decisions can be made regarding the scheduling of regular inspections, the implementation of preventive maintenance procedures, the evaluation of using more resistant materials considering the results obtained from their testing (e.g., Pucest), and the planning of trough replacement. The obtained reliability values can be used to optimize maintenance planning as follows:  
Figure 14: Maintainability, ranking of possible distribution laws of operating times of **(a)** Komatsu WA470 front loader bucket knife; **(b)** Komatsu HB365 excavator bucket tooth; **(c)** sorting station trough. 1. Establishing preventive maintenance intervals: Based on the reliability curves, optimal intervals for inspections, refurbishments, or replacements can be established. For example, if maintaining a reliability of 80% is desired, the preventive maintenance interval can be determined directly from the graph. 2. Resource allocation: Knowledge of the reliability of critical components allows the efficient allocation of maintenance resources (personnel, spare parts, equipment). Components with low reliability will receive higher priority in maintenance planning. 3. Cost evaluation: Reliability values can be used to evaluate the life cycle costs of components, considering acquisition, maintenance, and replacement costs. This allows the economic comparison of different materials or maintenance strategies. 4. Spare parts inventory planning: based on reliability, it can be determined how often a component needs to be replaced, and thus, spare parts inventory can be planned.