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Predictive Maintenance & IoT Impact on Mining

The mining industry is one of the oldest & most hazardous commercial sectors where the use and implementation of modern technology are very gradual. Mining…

Predictive Maintenance & IoT Impact on Mining

Predictive-Maintenance-IoT-Impact-on-Mining

The mining industry is one of the oldest & most hazardous commercial sectors where the use and implementation of modern technology are very gradual. Mining companies utilize a plethora of expensive equipment in a high stakes & cost environment. In these cases, asset health is critical to the safety & profitability of the mine.

This is where IoT-driven Predictive maintenance can be a gamechanger. It has the potential to collect and analyze environmental and equipment data instantaneously and conduct real-time risk and area evaluation. It reduces the risk of downtime & loss due to machine failure and reduces overall maintenance & spare part costs of high capital-intensive machinery. The application of IoT in the mining industry is quintessential because of its advantages for large-scale operations in mining, where the operating environment is constantly changing & workforce operates in a compact, adapting, and potentially hazardous environment.

 

Let’s first try to understand the what makes maintenance for the mining industry difficult:

Challenges in the mining industry

Disruptive & exorbitant impact of equipment failure in mines Equipment failure is the worst nightmare for mines. A standard mining operation spends 35-50 percent of its yearly operations budget on just asset maintenance & repairs. Unpredictable equipment failure can disrupt production & a considerable dent in the bottom line.
Remote monitoring of equipment at far-off locations Mines are typically located far away from civilization. So in case of unplanned downtime, it takes time to get expert maintenance personnel and spare parts to reach, diagnose and repair the equipment. These transportation delays & costs impact the budget as well as profitability.
Workforce safety depending on asset health Worker health & safety remains a big concern in the mining industry due to the difficult working conditions. Furthermore, as mines get deeper, the likelihood of a collapse & danger increases. While safety in mines has improved dramatically over the years, the fatalities caused by asset malfunction are a big reason for on-site hazards.
Unreliable connectivity options Additionally, because more mines are constructed in off-grid locations, providing stable electrical infrastructure to power mining operations and appropriate water supply becomes increasingly tricky. Connectivity is limited or unreliable, particularly in underground mines, and the 3G/4G signals may be difficult to pick up in remote regions.

Types of machine maintenance in mining

The different types of machine maintenance are:
  1. Reactive Maintenance/ Run-to-Failure Maintenance: This refers to repairs performed after a machine has already failed and it is unexpected and thus leads to emergency rushed repairs.
  2. Preventive Maintenance: This refers to any planned or scheduled machine maintenance that aims to identify and repair problems before they cause failure. It can be annual/bi-annual. But it cannot prevent asset failure between two schedules or unnecessary downtime.
  3. Condition-based Maintenance: It focuses on monitoring the current status of assets to undertake maintenance when evidence of decreasing performance or approaching breakdown is detected.
  4. Predictive Maintenance: It expands on condition-based maintenance by utilizing instruments and sensors to continuously evaluate machinery performance & flagging off any anomaly and its root cause before it results in a full-blown asset failure.

Predictive Maintenance in mining can cause many benefits – direct & indirect.

Some of the benefits of Predictive Maintenance are:

  • Reduced Downtime: Utilizing predictive maintenance, you can anticipate troubles ahead of time, decrease machine downtime, increase uptime by 15-20%, schedule maintenance as needed, and thus extend the life of an old machine by up to 20%.
  • Increasing Productivity: It ensures that both planned and unplanned downtime is kept to a minimum, resulting in fewer interruptions to production and a significant increase in overall productivity.
  • Higher Production Capacity: Asset availability of high performing & critical assets in mines helps plan and optimize production capacity, which is crucial for effective management & production planning and staying on schedule.
  • Lowered Maintenance & Spare Part Costs: Maintenance and spare part costs are significantly lower for preventative maintenance since all machines in the manufacturing process are continuously monitored and repaired before a problem becomes severe.
  • Enhancing Workplace Safety: Predictive maintenance can reduce the risk of work-related accidents by identifying any discrepancies that could lead to an accident on-site. Predictive maintenance ensures a sanitary and healthy environment in the plant while reducing safety risks by up to 14%.
  • Proactive Decision Making: The implementation of IoT enables mining maintenance managers to detect when there is a breakdown or a drop in performance, enabling them to react quickly and effectively. In addition, monitoring, obtaining, and analyzing data from particular mining equipment over a period may help them understand how the overall efficiency of the process itself can be improved.

Conclusion

The mining industry has been a critical sector globally for centuries. With the right Predictive Maintenance solution, mine maintenance managers can ensure that the production continues without impacting commercial efficiency while ensuring worker safety. A sound & functioning asset also ensures a greener footprint and fewer hazards, proving to be less dangerous for the environment.

Want to know more about how a competent Predictive Maintenance solution by Infinite Uptime is helping some of the largest mining companies improve asset & operational efficiencies?
FAQs
The mining industry grapples with the high costs of equipment failure, spending up to 50% of operational budgets on maintenance. Remote locations exacerbate downtime as getting personnel and parts to sites is time-consuming and costly. Safety concerns due to equipment health also pose significant risks in hazardous environments.
Predictive Maintenance uses IoT and AI to monitor equipment in real-time, predicting failures before they occur. This proactive approach reduces downtime, extends equipment life, and lowers maintenance costs compared to reactive (fixing after failure) and preventive (scheduled maintenance) strategies.
Predictive Maintenance reduces downtime by 15-20%, enhances productivity by minimizing interruptions, and optimizes production capacity. It lowers maintenance and spare part costs by monitoring equipment continuously and prevents costly breakdowns, thus improving overall operational efficiency.
Mining operations employ Reactive Maintenance (fixing after failure), Preventive Maintenance (scheduled check-ups), Condition-based Maintenance (monitoring performance for signs of wear), and Predictive Maintenance (AI-driven real-time monitoring) to ensure equipment operates efficiently and safely.
IoT enables real-time data collection from mining equipment, allowing for predictive analytics and condition monitoring. This data-driven approach facilitates proactive decision-making, improves operational efficiency, and enhances safety by identifying potential hazards before they escalate.

It’s crucial to select a solution that integrates seamlessly with diverse equipment types and can operate in remote, off-grid locations with limited connectivity. Deployment speed and scalability are also critical to ensure minimal disruption and rapid ROI across large-scale mining operations.

By preemptively identifying equipment issues, Predictive Maintenance helps create safer working conditions in mines, reducing the risk of accidents and environmental hazards. It also supports sustainable practices by optimizing resource use and minimizing operational disruptions.