How AI Predictive Maintenance Cuts Mining Downtime

Planned downtime [scheduled servicing] or [equipment] maintenance done at a specific interval cost a mining operation significant costs each and every time it is done. So, does every unplanned downtime [equipment failure] happen? Safety incidents also cost money. Part of the Appeal of predictive maintenance for a mining situation is the ability to provide a tool to personnel. A tool that changes the firefighting mentality to one that is more data driven with requests and decisions held. There is also the appeal that the technology has the ability to replace what could be the real time decision with one that relies more heavily on the traditional failure maintenance practices for. Think for a moment about the ability to accurately predict failure for each and every piece of equipment [fit for failure].

So what is predictive maintenance for mining?

Predictive maintenance is an incorrect instrument of taking care of a piece of equipment such that it will allow for the monitoring of the equipment in the immediate future for the maintenance of equipment. i.e. for the purpose of maintenance.

Predictive maintenance is a maintenance that is truly reactive. Unlike traditional maintenance which is scheduled on a schedule, predictive maintenance is essentially the maintaining of equipment that is to be used for maintenance in the near future for maintenance. Maintenance, for the purpose of measurement or more to the point, of value.

Predictive maintenance is essentially the shift from time maintenance to condition maintenance. In harsh, remote locations for preservation and protection of the greatest value for the equipment in an operation.

This is the belief that maintenance will fall short for the greatest value. The greatest value, the least short, is protection and preservation of the equipment for the greatest value. Mining is the industry. In mining, there is a single regard to the trade of a single cost that could be as high as the cost of the trade itself. $5 million for a single haul truck.

High-Value Mining Equipment and Maintenance Challenges

A ball mill or SAG mill costs tens of millions. Their unexpected failure comes with a lengthy list of costly consequences that include lost production, emergency parts of procurement, additional unplanned labor, and potential safety incidents.

Conventional maintenance has two major issues.

One is maintenance by failure, which is the practice of waiting for a failure to occur and then reacting. By the time an abnormality due to a failing asset surfaces by means of noisy operation, excessive heat, or vibration, the failure has occurred, and the cost of the repair, the parts needed for repair, and the labor needed to repair the asset are all lost production. The asset is brought back online only after the repair is complete, contributing additional lost production.

The second issue is the “tick the box” preventive maintenance, which places assets at a greater risk than waiting for the failure. Assets are taken offline, and labor, parts and time are lost for maintenance that is not needed. Because maintenance is done on a schedule as opposed to a failure, random asset failures are overlooked and contribute to the unplanned production loss.

Conventional asset maintenance fails to provide the needed foresight to mining operations. AI helps to close that gap. AI drives the need for predictive maintenance. AI helps to bridge that gap.

How AI Predictive Maintenance Works

Predictive maintenance uses real-time sensor data to predict an impending asset failure. AI helps with mining operations by predicting maintenance scenarios before an asset failure occurs. Continuous monitoring, using IoT sensors that are mounted on critical equipment, detects changes in vibration, temperature, pressure, current draw, oil quality, and speed of the equipment.

Pulse embeds AI models for process control. Predictive maintenance uses deep learning and AI, and Pulse incorporates that as well. Each equipment asset can process time series data along with 20 years of historical data. Predictive maintenance uses an anomaly detection framework to drive both operational and maintenance returns.

Any deviation from consistent sensor readings, even slight, will be detected by the system. These are normally undetectable by the operators, yet the system can detect these trends up to weeks before a system failure.

3. Failure Prediction and Risk Scoring

Anomalies are detected by the advanced AI, and the system will rank the probability of failure and provide time frame estimates. This helps the maintenance team to understand what the most important and urgent asset to intervene is and helps keep the production on schedule.

4. Automated Maintenance Alerts

Once a risk limit has been breached, the system will produce a maintenance work order, and include the reason maintenance is required, the suggested course of action, and how urgent the maintenance is. This helps the maintenance crew arrive with the correct equipment to avoid on-site systems failures.

5. Continuous Model Improvement

The AI continues to improve the model as it feeds on maintenance events, regardless of if the maintenance was a failure or if it was a required maintenance. The system gets better with understanding the asset and how it degrades and is unique to each asset.

Key Benefits of AI Predictive Maintenance

The primary financial and operational case for predictive maintenance has been established for the mining industry. These are the most notable advantages:

AI predictive maintenance helps with a reduction in the amount of time equipment is down, in some cases, up to 70% (Mining Technology, 2024). In a large open-pit mining operation, this can have a significant financial impact of $10 million saved per year with a 20 to 30 percent reduction in downtime.

Operations can maintain spend in addition to performing maintenance when the system indicates the asset is in a condition critical for maintenance (Oracle, 2024).

Extended Equipment Lifespan

Small damage can turn into significant breakdowns, but with proactive steps, this can be avoided. Equipment that is maintained according to its condition, rather than according to schedule or with breaks, can have longer service lives (Rojas et al., 2025).

Improved Safety

From structural collapses to hydraulic failures to electrical faults, many mining equipment failures can become safety problems. Safety problems can be avoided along with accidents by spotting issues earlier rather than later, which protects workers in high-stress environments.

Better Production Planning

With the ability to predict when an asset is due for maintenance, maintenance teams can communicate with production schedulers about optimized ways to work around the planned maintenance. Also, planned maintenance is less disruptive than unplanned maintenance, which helps the mine maintain its desired production levels.

Real-World Example: AI Predictive Maintenance on a Ball Mill

One of the most critical and expensive assets in any mineral processing circuit is the ball mill motor. A mining company in South Africa is an example of the use of AI predictive maintenance in their ball mill motor. The system was able to detect abnormal vibrations that happened in the days prior to maintenance. This allows the system to take planned maintenance rather than emergency maintenance, and self-repairing breakdowns help to prevent potentially hazardous and costly downtimes (Razor Labs, 2024).

This example is one of the many seen in the mining industry across the globe. Predictive maintenance based on AI shows the industry the importance of maintaining equipment to prevent failures rather than the importance of maintaining equipment to resolve failures.

Implementing AI Predictive Maintenance: Where to Start

Audit Your Critical Assets

Some equipment is more dangerous and disruptive to business than others. Start by identifying those that present the highest safety, or production risk should they fail, generally the primary crushers, mills, major pumps, and high-tonnage haul trucks.

Establish Your Sensor Infrastructure

Predictive maintenance is only as good as the data providing input. Ensure you have adequate IoT sensors on your critical assets, and that data is captured on a regular basis.

Choose an AI Platform Built for Mining

Most generic industrial AI solutions will not offer the domain-specific models necessary for predicting equipment failure modes specific to the mining industry. Select platforms that offer models that have been trained in data from mining equipment.

Integrate with Your Maintenance Management System

AI-generated alerts should be automatically integrated into your maintenance processes. Working with your existing CMMS (Computerized Maintenance Management System), the system goals are fulfilled without the need for intervention or looping back and forth for control on your part.

Start with a Pilot and Then Scale

Begin with just a few high-priority assets, assess the model forecasts against the actual results over a 60- to 90-day timeframe, and use that data and evidence to build the business case justification for a broader implementation.

How NTWIST Provides Predictive Maintenance for the Mining Sector

Predictive maintenance features are offered as part of our AI Mining Application.

Our tools proactively assess the health of your operation's devices. They identify early warning signs of potential device failures and not only suggest preventive maintenance but also provide the information your team needs to take the right course of action.

NTWIST eliminates the need for standalone point solutions bolted onto your existing systems. We let you conveniently integrate predictive maintenance intelligence with ore grading control, mill throughput optimization, and mine planning systems. Our predictive maintenance tools enable your systems to integrate asset health with production performance seamlessly.

The mining operations that leverage NTWIST's systems have not only minimized production interruptions but have also maximized device reliability. NTWIST has also enabled its customers to develop maintenance plans that align with production needs.

Learn how NTWIST's predictive maintenance tools are leveraging advanced technologies to provide interruption-free services.

The Ultimate Guide to Downtime Prevention and Advanced Technologies

Predictive Maintenance in Mining

Predictive maintenance in mining is using advanced technologies, such as IoT, machine learning, and artificial intelligence, to ensure continuous monitoring of device status, predict failures, and perform maintenance interventions.

How Is Predictive Maintenance Enhanced with Artificial Intelligence?

AI is used to assess manufacturing equipment for anomalies that deviate from the norm. For maintenance needs, the AI system assigns a maintenance priority, and within minutes, it will provide maintenance recommendations.

How Is Predictive Maintenance Leveraging AI Technologies to Ensure Safe Mining?

NTWIST systems predict nearly 60 to 70 substantiations of device manufacturing, and if the device type and model have a standard predictive maintenance coverage, the system will provide the maximum coverage.

Which Assets Benefit Most from Predictive Maintenance in Mining?

Assets that serve a high-value, high-criticality function, such as SAG and ball mills, primary crushers, large pump systems, and monohull-driven conveyance systems, are most likely to benefit from predictive maintenance.

Is predictive maintenance costly?

We usually see payback on the initial outlay for sensors and AI software in under 12 months. This is largely due to mining operations being able to extend the lifespan of their equipment while lowering operational costs and maintenance costs during the first year to 18 months.

Conclusion

London Mining and Orestone Mining have transformed the management of their assets with AI predictive maintenance. Transitioning from old paradigms employing routine maintenance balancers and emergency repairs to predictive maintenance simplifies the repair process and reduces the time laborers are on site and the duration of the repairs due to the reduction in emergency repairs. Mine management and maintenance costs are all significantly improved.

Those who employ predictive maintenance technology as opposed to the old paradigms are seeing tangible results worldwide while those who have not embraced this predictive maintenance technology are witnessing the costs of command and repair increasing with every broken piece of equipment.