Reliability, Maintainability, and Availability of Mining Drilling Equipment

Abstract

Drilling represents the initial and most crucial phase in open pit mining operations that utilize the drilling and blasting method. The mobility of all drilling machineries, facilitated by the undercarriage moving between drilling sites, is essential. Any malfunction in this system can hinder movement and, ultimately, halt drilling activities. The reliability, maintainability, and availability of such equipment are vital for continuous production and pose significant challenges for mining companies aiming to enhance system utilization. This research focuses on evaluating the reliability, maintainability, and availability of rotary drilling machines’ transmission systems through a case study conducted in Iran. The study involved collecting failure data from four different drilling machines and proposing preventive maintenance schedules for each. The findings reveal that the transmission system’s reliability significantly decreases to zero after 1000–3500 hours of operation. From a maintainability perspective, most machine failures were repaired within 10–45 hours. The study underscores the importance of enhancing the transmission system’s maintainability to boost the overall reliability of drilling operations in mining environments.

1. Introduction

Mining equipment and machinery are pivotal in boosting production rates and minimizing operational expenses in mines. Thus, assessing machine performance and enhancing their efficiency are key strategies for elevating productivity and substantially lowering production costs. Any malfunction in these machines can interrupt mining operations, leading to a complete halt. It is therefore imperative to assess the reliability of these machines and to calculate the likelihood of their continuous operation without failures. In recent times, the reliability analysis of various types of mining equipment, including mining trucks [1, 2], Load-Haul-Dump (LHD) units [3, 4], shovels [5, 6], draglines [7, 8], and drilling machines [9, 10], has been extensively explored.

The initial step in blasting operations is drilling, aimed at creating holes of specific geometric shapes and arrangements. To date, various drilling techniques such as mechanical, thermal, hydraulic, acoustic, chemical, electrical, and microwave drilling have been employed for rock drilling. Among these, mechanical drilling systems have become the standard in mining and construction activities. Currently, rotary blasthole drilling machines are widely used in surface mines and quarries to drill through rock formations ranging from soft to medium hardness. A malfunction in the drilling equipment can halt production-related activities and eventually bring all mining operations to a standstill, resulting in significant financial losses for the mine. Implementing a well-planned preventive maintenance schedule is crucial for mine managers and engineers to enhance the mine’s economic performance by preventing unnecessary damage and prolonged downtimes. Evaluating the reliability of mechanical systems and planning for their maintenance and repair is a robust strategy for anticipating breakdowns and ensuring optimal performance over specific time periods. Nowadays, these metrics serve as crucial technical and managerial indicators across industries, aiding in maintaining production continuity and bolstering the economic viability of businesses.

The reliability, maintainability, and failure patterns of mining equipment and machinery have been extensively explored by researchers in recent times. Yet, drilling machines have received comparatively less attention in scholarly research. Gaurav et al. [11] conducted a study analyzing the reliability and maintainability of a universal drilling machine at the Saoner underground mine in India, collecting failure and repair data over a 44-month period from maintenance staff, daily logs, and maintenance records. They employed serial correlation and trend statistical tests to determine the most appropriate methods for reliability and maintainability modeling. Their findings highlighted a critical issue related to the overheating of a specific part of the drilling machine, suggesting a revision of the maintenance program to enhance reliability levels. Rahimdel et al. [9] adopted a modeling and simulation approach to assess the reliability and availability of rotary-type drilling machine systems, dividing the drilling system into three independent components: the drill string, rotary head, and mast. They utilized statistical modeling and Monte Carlo simulations for their analysis, concluding that the mast was the most dependable subsystem. Their importance analysis recommended prioritizing preventive maintenance on the drill string and rotary head subsystems. Ugurlu and Kumral [12] focused on the reliability of drilling machines in open pit mines, using Markov chain Monte Carlo stochastic processes and reverting process modeling techniques to study the reliability of 10 blasthole drilling machines. They analyzed the time between failures (TBFs) and repair time data to model reliability and calculated drilling length to explore the relationship between reliability and drilling efficiency. Their results provided insights into the behavior of drilling machines, simulated the availability of drills, and used regression analysis to correlate reliability levels with drilling performance.

A review of existing literature reveals that the study of drilling machine reliability has not been a primary focus for many researchers. From the reviewed literature, it is apparent that most previous studies have concentrated on the reliability of mechanical and electrical subsystems of drilling machines within the context of open pit mining. However, there has been scant research on the crucial aspect of ensuring the reliable movement of drilling machines, either from one drilling site to another or between different work sites. The undercarriage, which forms the foundation supporting the machine’s upper structure and mast, is pivotal for facilitating movement. The crawler undercarriage, consisting of two tracks, is a prevalent type in rotary drilling machines, and any malfunction in this system can impede machine mobility, ultimately halting operations. This study focuses on examining the failure behavior of the transmission system in such machines. Through a detailed investigation of the operation and reliability of the transmission system of rotary drilling machines, in this chapter, the Sarcheshmeh copper mine in Iran is applied as a case study for data gathering. Reliability analysis is conducted using statistical modeling techniques, aiming to explore the reliability characteristics of the system and identify an optimal maintenance strategy. The findings of this study present the operational safety probability of the transmission system in each drilling machine, calculate the system’s maintainability, and determine appropriate intervals for inspection and maintenance.

The findings from this research offer valuable insights for mine directors and engineers, guiding them to adopt the recommended maintenance practices for efficient and cost-effective operation of blast hole drilling in open pit mines. The structure of this chapter is organized in the following manner: Section 2 outlines the statistical modeling approach used for analyzing failure and repair data of the repairable system. Section 3 introduces the transmission system specific to crawler-type drilling machines. The chapter concludes with a detailed discussion on modeling the reliability, maintainability, and availability of the transmission system in drilling machines at the Sarcheshmeh copper mine in Iran.

2. Method

The efficiency of a system’s performance hinges on its reliability, maintainability, and availability metrics. Enhancing these aspects necessitates an upgrade in maintenance and inspection protocols, moving away from traditional maintenance strategies like corrective and preventive maintenance [1]. This section is dedicated to outlining the research methodology used in this study. Initially, it introduces the approach taken for analyzing reliability, maintainability, and availability. Subsequently, it delves into the concept of reliability-based preventive maintenance, explaining its principles and application.

2.1 Reliability, maintainability, and availability analysis

Reliability describes the likelihood that an item or system will fulfill its intended function under specified conditions over a defined interval [13, 14]. Qualitatively, reliability refers to the item’s capacity to stay operational. In quantitative terms, it measures the chance that the item will operate without any disruptions within a given timeframe. The fundamental definition of the reliability function is established by:

$$R\left(t\right)=1-{\int }_0^{t}f\left(x\right)\mathit{dx}$$E1

In this context, R(t) represents the reliability at time t, while f(x) denotes the failure probability density function. Maintainability, on the other hand, is defined as the likelihood that an item can be maintained or repaired within a specified time frame, given certain procedures and resources [13]. This has become increasingly significant due to the rising costs associated with maintenance. The maintainability function calculates the probability of successfully completing a repair within a designated period and is expressed by:

$$m\left(x\right)={\int }_0^t{f}_r\left(x\right)\mathit{dx}$$E2

Here, m(x) represents the maintainability function, and f_r(x) is the probability density function for repair time, with t being the variable representing repair time.

This part of the section aims to outline the fundamental methods used for conducting reliability and maintainability analysis on repairable systems. Typically, three approaches are utilized for this analysis: the renewal process (RP), the homogeneous Poisson process (HPP), and the non-homogeneous Poisson process (NHPP). In the context of RP, the analysis often assumes that TBFs are independent and identically distributed (iid). To verify this assumption [10, 15], trend tests and serial correlation tests are applied. The Military Handbook test, which calculates the test statistic U, is a commonly used method to identify current trends in the data [15, 16]:

$$U=2\sum _{i=1}^{n-1}\mathit{ln}\left(T_n/T_i\right)$$E3

Here, n represents the total number of failures, T_n refers to the time at which the n^th failure occurs, and T_i is the time at which the i^th failure occurs.

If the null hypothesis is rejected with a 5% level of significance, it indicates that the data exhibit a trend, implying that the failures are not identically distributed. Under these circumstances, the power law process (PLP) serves as a widely recognized model for examining the reliability of repairable systems. In the PLP approach, the failure rate and consequently the reliability function are determined as follows [17, 18]:

$$\lambda \left(t\right)=\frac{\beta }{\theta }{\left(\frac{t}{\theta }\right)}^{\beta -1}$$E4

$$R\left(t\right)=\mathit{exp}\left[-{\left(\frac{t}{\eta }\right)}^{\beta }\right]$$E5

Here, θ represents the scale parameter and β the shape parameter, with t denoting time. If β is less than 1, it indicates a decreasing failure intensity; if β is greater than 1, failure intensity is on the rise; and when β equals 1, the power law process (PLP) simplifies to a homogeneous Poisson process (HPP), resulting in a constant failure intensity. The parameters θ and β can be calculated using the following formulas [19]:

$$\beta =\frac{n}{{\sum }_{i=1}^n\mathit{Ln}\left(t_n/t_i\right)}$$E6

$$\eta =\frac{t_n}{n^{1/\beta }}$$E7

Here, t_n represents the cumulative time up to the i^th event, and n is the total number of failures.

To assess the fit of the model, a straightforward graphical technique involves plotting the logarithm of N(t) against the logarithm of t on graph paper with a square grid. If the plot yields a straight line, this indicates that the power law process model is an appropriate choice for conducting reliability analysis [20].

Availability refers to the likelihood that a system will operate as intended under specified conditions (including factors like the accessibility of spare parts and personnel) and perform reliably across all time periods.

This encompasses the total duration of operation, including operational, logistical, active repair, and administrative phases. Furthermore, availability is described as the chance that a system is operational and not in a state of failure or repair when it is required for use [21]. This metric enables the identification of system vulnerabilities that necessitate thorough servicing and inspection. The formula for calculating availability is as follows:

$$\mathit{Av}=\frac{\mathit{\text{MTBF}}}{\mathit{\text{MTBF}}+\mathit{\text{MTTR}}}$$E8

Here, Av represents the availability, MTBF stands for the mean time between failures, and MTTR denotes the mean time to repair.

2.2 Preventive maintenance and improvement of the system reliability

Maintaining a system’s normal operation necessitates regular maintenance activities, which fall into two primary categories: corrective maintenance (CM) and preventive maintenance (PM). CM refers to repairs conducted to fix a failed item and restore it to working condition. In contrast, PM is carried out while the system is operational and encompasses inspection, detection, and prevention activities to avert initial failures and maintain the item in a specified condition [22]. The primary goal of PM is to enhance reliability by either eliminating or mitigating the impact of equipment failures. PM extends equipment lifespan, prevents unexpected failures, and reduces the need for unplanned maintenance, making it more effective than CM. To assess PM’s impact on reliability, one must first determine the critical reliability level, ensuring the system operates without dipping below this threshold. Subsequent to establishing this critical level, PM schedules are devised. Figure 1 illustrates the reliability enhancement observed immediately following the initial PM intervention [10, 23].

Figure 1.

Impact of preventive maintenance on reliability functions across maintenance intervals T_PM [10].

The reliability function following preventive maintenance (R_PM(t)) is determined using the following formula [10]:

$$R_{\mathrm{PM}}\left(t\right)=\left\{\begin{array}{l}R\left(t\right),0<t<T_{\mathrm{PM}}\\ ,\\ R^n\left(T_{\mathrm{PM}}\right).R\left(t-n{T}_{\mathrm{PM}}\right),n{T}_{\mathrm{PM}}<t<\left(n+1\right)T_{\mathrm{PM}},n\ge 1\end{array}\right.$$E9

Here, R(t) represents the reliability over the failure-free period t; R_PM(t) denotes the reliability function subsequent to preventive maintenance; T_PM refers to the intervals between preventive maintenance tasks, and n is the count of preventive maintenance activities that have been executed.

3. Transmission system of rotary drilling machines

Rotary drilling machines are comprised of various components, each consisting of parts that require careful design to ensure longevity. The key components of a rotary drilling machine encompass the undercarriage, main frame, leveling jack, main mover, compressor, operator cab, driver cab, mast, rotary head, auxiliary winch, hydraulic system, dust control equipment, and the machinery house. According to Gokhale [24], the parts of a drilling machine can be organized into three primary groups: assemblies located in the lower, upper, and mast sections. Rahimdel et al. [9] further divided these components into five main subsystems: hydraulic, electrical, pneumatic, drilling, and transmission (including crawler assemblies). The transmission subsystem, in particular, comprises elements that support the upper structure and mast, facilitating the movement of the blasthole drill. A malfunction within this subsystem can halt the machine’s operation, leading to a complete cessation of drilling activities.

The undercarriage, also known as the transmission system, forms the foundation of the entire drilling machine and is situated beneath the machine’s body. The vast majority of rotary drilling machines, over 98%, are of the crawler type. This system is designed to exert low ground pressure with its widely spaced, long tracks, enabling it to navigate rough terrains with remarkable stability, even when the mast is raised. Crawler-type undercarriages are known for their low maintenance costs, with many of the machine’s parts being capable of automatic lubrication. A crawler system includes two tracks, each comprising several components such as the track beam, idler and load rollers, sprocket, idler tumbler, and trackpads, as depicted in Figures 2 and 3 [24]. The track beam is typically constructed from a welded box section, which may be filled with a specific type of concrete that includes scrap steel pieces for corrosion resistance, enhancing both weight and rigidity. The top of the track beam houses several idler rollers that support the crawler pads from below. These tracks are connected by a rigid cross frame, at the center of which sits a large diameter circular roller bearing. This bearing supports the machine’s upper frame, allowing it to rotate 360° relative to the lower crawler tracks [24, 25].

Figure 2.

The studied drilling machine and its crawler track.

Figure 3.

A crawler-type undercarriage and its components [24].

The sprocket, also referred to as a drive tumbler, is affixed to one end of the track beam. As the sprocket turns, it propels the trackpads into motion. This rotational movement of the sprockets is generated by a high-torque hydraulic motor connected through a gear train, as illustrated in Figure 4. At the opposite end of the track beam, an idler tumbler is installed, functioning similar to the load rollers and sprocket by supporting the track beam. Moreover, this tumbler is equipped with a spring mechanism that exerts a continuous force on the tumbler, ensuring the crawler track maintains its necessary tension [24].

Figure 4.

Hydraulic motor and gear train [24].

Based on the components and figures referenced earlier, the transmission system of drilling machines can be categorized into six main subsystems. These subsystems are arranged in a series configuration, as depicted in Figure 5.

Figure 5.

Decomposition of main components of a drilling machine’s transmission system.

4. Results and discussions

This section is dedicated to examining the reliability, maintainability, and availability of the transmission systems in rotary drilling machines. Failure data were gathered over a two-year period from four rotary drilling machines (labeled A, B, C, and D) operating in the Sarcheshmeh copper mine. For this analysis, daily operational and production reports from shift supervisors and maintenance personnel were utilized. Initially, the time between failures (TBFs) and time to repairs (TTRs) were determined. The mean and standard deviation for the failure and repair data of the machines under study are presented in Table 1.

Data set	Machine	Mean	Standard deviation
TBF	A	324.75	241.27
	B	424.37	342.43
	C	421.87	364.10
	D	411.46	500.23
TTR	A	10.26	29.63
	B	10.70	23.70
	C	4.99	8.94
	D	10.41	17.11

Table 1.

Mean and standard deviation of failure and repair dataset (hour).

4.1 Reliability and maintainability analysis of drilling equipment

The datasets for time between failures (TBFs) and time to repair (TTR) were subjected to trend analysis using the calculated test statistic value (from Eq. 3) and the Chi-squared (Chi²) test. The outcomes are detailed in Table 2. As indicated in Table 1, the TBF data for all machines and the TTR data for all machines, except machine D, exhibit no trend and are correlation-free, thus qualifying as independent and identically distributed (iid). Consequently, the renewal process method is suitable for modeling their reliability. However, the TTR data for machine D show an increasing trend, rendering the iid assumption inapplicable for this particular machine. As a result, the nonhomogeneous Poisson process (NHPP) is employed to model its reliability function. In accordance with the research methodology described, the power law process (PLP), a specific variant of the NHPP, is utilized in this analysis.

Machine	Data set	U statistic	Decision on trend	Lower Chi^2 value	Upper Chi^2 value	Modeling method
A	TBF	28.86	no trend	15.38	38.88	RP
	TTR	20.82	no trend	16.93	41.34	RP
B	TBF	38.42	no trend	15.38	38.88	RP
	TTR	28.97	no trend	16.93	41.34	RP
C	TBF	27.01	no trend	18.49	43.77	RP
	TTR	27	no trend	20.07	46.19	RP
D	TBF	16.41	increasing trend	25.51	55.76	PLP
	TTR	50.38	no trend	28.14	58.12	RP

Table 2.

Computed values of the test statistic U for TBF data.

Following the trend analysis, the next phase involved identifying the best-fitting distributions for datasets that meet the iid assumption criteria. The Kolmogorov–Smirnov test was applied to determine the most appropriate distributions. The outcomes of this data analysis, along with the best-fitting distributions, are outlined in Table 3.

Machine	Data set	Best-fit distribution	Estimated parameters
A	TBF	Gamma	α = 1.68, β = 193
	TTR	Lognormal	σ = 1.27, μ = 1.39
B	TBF	Lognormal	σ = 0.83, μ = 6.19
	TTR	Weibull (3P)	α = 0.41, β = 5.23, γ = 0.166
C	TBF	Weibull (3P)	α = 0.90, β = 557.77
	TTR	Lognormal	σ = 1.09, μ = 0.80, γ = 24.67
D	TTR	Lognormal	σ = 1.63, μ = 1.06

Table 3.

Results of TBF and TTR data analysis and the best statistical distribution.

As previously discussed, the assumption that the time between failures (TBFs) data for the transmission system of machine D are independent and identically distributed (iid) does not hold true. Consequently, the power law process (PLP) should be employed to analyze its reliability. The results from the graphical goodness-of-fit test for the PLP model are depicted in Figure 6. The figure demonstrates a reasonably good linear fit with the data, indicating that the PLP is an appropriate method for the reliability modeling of this system. Based on the analysis, the PLP parameters were determined to be β = 2.33 and η = 1680.31. Figure 7 presents the reliability plots for the transmission systems of all the drilling machines studied.

Figure 6.

Goodness-of-fit test on TBF data of machine D for PLP model.

Figure 7.

The reliability plots of transmission systems of studied drilling machines.

From the analysis presented in Figure 7, it is observed that the reliability plots for machines C and B exhibit similarities, yet machine C demonstrates consistently lower reliability throughout the operational period compared to machine B. The transmission system of machine D outperforms all others in terms of reliability, with its reliability nearing zero after 3500 hours of operation. On the other hand, machine A, identified as the least reliable, sees its reliability drop to zero after just 1200 hours. Currently, the reliability of the transmission systems for machines B, C, and D stands at approximately 8, 9, and 64%, respectively. This variance in reliability across the machines can be attributed to differences in their ages and operating conditions. Machines A and B were both produced by Bucyrus, while machines C and D are models from Ingersoll-Rand. Machines A and B have been in service for about 21 years, whereas machines C and D are around their 15th year of operation, which aligns with the higher reliability observed in machine D and the lower reliability in machine A due to age discrepancies.

The situation between machine B and machine C is more complex, with machine C being newer yet exhibiting lower transmission system reliability than machine B during the observation period. A thorough review of operation reports and related documents for machine C was conducted to pinpoint the cause of this discrepancy. The review revealed that machine C has been deployed in harder rock fields more than machine B, implying that the harsher working conditions and the intense strain endured during drilling operations have compromised its reliability and expedited wear and tear.

Based on the parameters determined for the best-fitting distributions of time to repair (TTR) data, maintainability plots are illustrated in Figure 8. These plots reveal that machine C possesses the greatest maintainability compared to the others, with all its failures being rectified in under 30 hours. For machine C’s drilling system, there is an 80% likelihood that repairs will be completed within 5.5 hours. Conversely, machine B exhibits the longest repair times of all the machines analyzed, indicating it has the lowest maintainability.

Figure 8.

Maintainability plots of transmission system for studied drilling machines.

Figure 8 indicates that there is an 80% chance that any corrective maintenance on the transmission systems of the drilling machines at the Sarcheshmeh copper mine will be concluded within an average of 13.12 hours. Additionally, the plots demonstrate that machines A and B exhibit similar maintainability characteristics, and machine D displays a comparable pattern. This similarity suggests that the maintenance scheduling and operations for these machines can be efficiently coordinated. The reduced maintainability observed in these machines can be attributed to their age and the comprehensive need for parts to be either significantly repaired or replaced.

4.2 Maintenance scheduling and reliability improvement

Maintenance management stands as a pivotal objective in any reliability analysis. Through examining the operational and failure patterns of machinery, reliability and maintainability analyses provide extensive insights into the machinery’s requirements. Given the critical importance of reliability within open pit mining systems, adopting a reliability-based preventive maintenance strategy is deemed the most effective approach to maintaining system reliability at a satisfactory level. This strategy utilizes preventive maintenance schedules derived from the reliability model to ensure optimal performance and reliability. In this study, a target reliability level of 90% was set for each subsystem of the drilling machine to guide the planning of preventive maintenance activities. As such, it is recommended that all subsystems undergo regular checks and maintenance at predetermined intervals to ensure efficient and reliable operation.

Utilizing the reliability plots presented in Figure 7, the intervals for reliability-based preventive maintenance to maintain a 90% reliability level for the transmission systems have been determined as 70, 125, 60, and 640 hours for machines A, B, C, and D, respectively. These findings provide a precise framework for the service and maintenance schedule of the current fleet of drilling machines. Given these calculated intervals, halting mining operations to conduct preventive maintenance on each machine individually is nearly impractical. Therefore, it is crucial to consolidate preventive maintenance tasks to streamline the maintenance process and minimize downtime.

To streamline the maintenance schedule, it is advisable to consolidate similar maintenance intervals into a single period. Considering the obtained maintenance time intervals for 90% reliability level and amalgamating the maintenance tasks for machines A and C, preventive maintenance for these machines can be scheduled for every 65 hours. This approach aims to achieve smoother operations while minimizing maintenance costs and operational interruptions. Consequently, the maintenance schedule for the transmission systems of the drilling machines should be structured around intervals that are multiples of 65 hours. According to this method, preventive maintenance for machine B would occur every 130 hours, whereas, for machine D, it would be scheduled every 650 hours. A graphical representation of this consolidated maintenance schedule is illustrated in Figure 9.

Figure 9.

PM plan for transmission system of drilling machines.

While the refined maintenance schedule deviates from the initial reliability-based maintenance framework, these adjustments and simplifications are crucial for crafting a practical, operationally flexible plan rather than one that is purely theoretical. The revised schedule offers a more feasible and manageable approach, readily implementable in real-world settings. As per the maintenance intervals illustrated in Figure 9, the impact of the adjusted preventive maintenance (PM) schedule on the transmission systems’ reliability was assessed and depicted in Figure 10. The results from Figure 10 reveal that implementing the suggested PM program significantly enhances the reliability of the transmission systems in machines A, B, and D. Specifically, the first PM cycle sees a reliability increase of 4.74, 5.62, and 6.03% for machines A, B, and D, respectively. It is important to note, however, that while machine C’s reliability shows an improvement ranging from 2.69 to 11.71%, the PM program does not markedly affect its reliability, with the reliability curves “with PM” and “without PM” closely aligning. This suggests that the preventive services provided are insufficient to offset the system’s failures, indicating a need for comprehensive servicing and maintenance of machine C’s transmission system. Nevertheless, the availability analysis reveals that the transmission systems of machines A, B, C, and D have availabilities of 97.30, 97.68, 99.34, and 99.27%, respectively, indicating high levels of availability across all machines. This suggests that the transmission systems significantly contribute to the machines’ operational uptime, ensuring a high likelihood of their availability for service.

Figure 10.

Reliability plots with and without considering the suggested PM intervals.

5. Conclusions

Drilling machines play a crucial role in open pit mining, directly impacting mine production. Historically, rotary drilling machines have been employed in surface mines to penetrate soft and medium-hard rock formations. This study focused on the reliability and maintainability of the transmission systems in the drilling machines at the Sarcheshmeh copper mine in Iran, yielding several key findings:

• The failure data for the transmission subsystems of all machines, except for machine D, were found to be trend-free and correlation-free, indicating that the data for these machines are independent and identically distributed.

• The analysis determined that the time between failures (TBFs) for machines A, B, and C adhere to gamma, lognormal, and exponential distributions, respectively. The power law process model emerged as the most suitable for analyzing the failure data of the transmission system in machine D.

• The reliability curves for machines C and B displayed similarities. The transmission system of machine D exhibited the highest reliability among all machines, with its reliability declining to zero after approximately 3500 hours.

• Variations in the reliability of the transmission systems across the machines were primarily attributed to differences in machine ages and operational conditions. Notably, machine C experienced harsher working conditions in hard rock fields compared to machine B, which adversely affected its reliability.

• The analysis of time to repair (TTR) data indicated that there is an 80% chance that corrective maintenance on the transmission systems will be concluded within an average of 13.12 hours.

• The study sets a target reliability level of 90% for each subsystem within the drilling machines to guide preventive maintenance scheduling. To minimize maintenance costs and downtime, the maintenance schedule for the transmission systems was organized around intervals that are multiples of 65 hours. Accordingly, preventive maintenance for machine B is scheduled every 130 hours, and for machine D, every 650 hours.

• The availability analysis estimated an average 98.4% probability that the transmission systems would be available for service.

These insights provide a comprehensive overview of the operational reliability and maintenance strategies for drilling machinery in open pit mining, highlighting the importance of tailored maintenance schedules to enhance system availability and performance.