New Method for Mining Downtime Analysis

By Dr Silpaja Chandrasekar, PhDReviewed by Laura ThomsonFeb 17 2025

A recent article in Scientific Reports analyzed the maintainability of ten rope shovels in an open-pit copper mine, examined downtime factors, and optimized maintenance strategies. Researchers found that 80% of repairs were completed within a single shift, most taking less than 45 hours.

A novel method was developed to categorize downtimes into pre-repair, repair, and post-repair actions, with repair activities accounting for 50% of the total downtime. Around 30% of delays resulted from transportation and operational hold-ups. The study provided insights on optimizing shovel maintenance and improving mine productivity.

Related Work

Previous works on maintainability analysis and downtime reduction classified downtime into four primary components: mean detection time, mean decision-making time, mean time to repair, and mean function test time. These studies aimed to optimize equipment availability by identifying key elements affecting repair efficiency. Many researchers focused on minimizing maintenance lead time by systematically analyzing failure patterns and repair processes. Some studies explored the role of personnel skills, technology, and infrastructure in improving maintainability. Others investigated the impact of downtime reduction strategies on overall mining productivity.

Equipment Reliability and Downtime Assessment

Maintainability is crucial in engineering, ensuring that systems remain functional with minimal downtime. It involves designing products for easy maintenance by reducing repair time, resource consumption, and environmental impact. A well-maintained system improves robustness, lowers costs, and boosts operational efficiency across various industries. Key elements affecting maintainability include accessibility, standardization, modularization, and human factors, all of which contribute to streamlined maintenance processes.

Downtime assessment is essential for improving system efficiency by identifying variables that lead to equipment malfunctions and disruptions. Various approaches, such as root cause evaluation, failure modes and effects analysis, and overall equipment effectiveness, help organizations refine maintenance strategies. These methods classify downtime into phases: anomaly detection, decision-making, restoration, and functionality validation. Addressing inefficiencies in these areas enhances system reliability and operational continuity.

Classifying downtime components provides a structured approach to diagnosing inefficiencies in maintenance workflows. Key metrics help industries assess and fine-tune their maintenance performance. Organizations can reduce unexpected malfunctions and operational disruptions by improving anomaly detection techniques and implementing predictive maintenance. Enhancing data-driven decision-making further optimizes resource allocation and repair prioritization. Integrating advanced analytics and automation can significantly boost equipment durability and operational efficiency.

Mathematical modeling plays a significant role in analyzing maintainability and downtime. Predictive analytics and real-time monitoring have emerged as practical tools for minimizing repair disruptions and optimizing resource allocation. By combining quantitative and qualitative approaches, industries can develop proactive maintenance strategies that advance equipment longevity, reduce costs, and ensure continuous system performance.

Shovel Fleet Performance

The Sarcheshmeh Copper Mine, Iran's largest open-pit mine, produces 31 million tons of ore annually and aims to increase production to 68 million tonnes within five years. The mine primarily relies on 10 rope shovels with 12 and 15 m³ bucket capacities for copper sulfide ore extraction. Researchers conducted a comprehensive maintainability analysis using a hybrid quantitative and qualitative approach to enhance fleet availability and operational productivity. A new downtime classification framework was introduced to systematically assess elements contributing to shovel downtime, ultimately improving maintenance practices and overall mine efficiency.

The study analyzed shovel failure records from March 1, 2022, to March 1, 2023, using Easyfit software with Weibull, lognormal, and exponential models. Shovels SH10 and SH08 had the highest maintainability, with 80% of repairs completed within 130 minutes, while SH03 and SH04 took 180 minutes. Maintainability curves showed that 80% of repairs were finished within one shift, optimizing maintenance schedules.

Downtime analysis classified malfunctions as mechanical or electrical, emphasizing key aspects such as pre-repair, repair, and post-repair activities. Only 54% of mechanical breakdown-related downtime was dedicated to actual repairs, while delays and logistical challenges consumed the remainder. Electrical anomalies required longer diagnosis times due to the lack of immediate visual or auditory cues, further extending downtime.

Statistical assessment showed that only 50% of downtime was spent on repairs, while the rest was due to access, diagnosis, and preparation delays. Logistical inefficiencies and communication gaps further affected maintainability. Mechanical failures stemmed from structural defects, while electrical issues arose from short circuits and power irregularities, requiring specialized troubleshooting.

The study recommended optimizing resources, upgrading the support fleet, and using advanced communication to reduce downtime. Enhancing expertise, minimizing disruptions, and implementing predictive maintenance could strengthen shovel dependability and increase production.

Conclusion

To summarize, a maintainability evaluation of 10 rope shovels at Sarcheshmeh Copper Mine revealed that most repairs were completed within 45 hours, with 80% finalized in a single shift. However, impediments in accessing failed machines and logistical inefficiencies extended downtime. Skilled maintenance crews managed repair operations efficiently, but only 50% of downtime was spent on actual repairs, with the rest attributed to logistical and procedural delays. Recommendations included mobile welding units, better resource allocation, time management training, and improved electrical fault diagnostics to improve fleet maintainability.

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