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How predictive analytics is the future of asset optimisation in mining

The concept of predictive maintenance (PdM) has been around for a long time, but like the Internet of Things (IoT), it has only recently come to fruition in high-end industries thanks to digital transformation. PdM is defined as a group of emerging scientific technologies that are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted.

In a shifting market due to fluctuating commodity prices, mining companies are continually looking for new ways to operate more effectively and efficiently. Preventive maintenance schedules alone are not effective enough in helping organisations to avoid asset failures and the associated cost. Companies recognise that to maintain profitability, they need to avoid downtime, unexpected or otherwise, while managing risk and maintaining healthy assets.

Mining companies are most often operating reactively or at most at a basic preventive level. Investment for modernising equipment is limited so best-of-breed organisations are looking to get more from their existing infrastructure. This is where predictive analytics and an overall PdM approach come into play. At the most basic level, predictive maintenance techniques using smart sensors can predict equipment failure before it actually happens. But in order for a PdM approach to be effective, your systems must be able to process the right data and leverage the right analytics to predict degradation in performance asset health and prioritise maintenance activity accordingly.

Organisations that implement emerging sensor technology in conjunction with a strong enterprise asset management solution complete with advanced analytics will be able to operate more efficiently and cost effectively while providing safer environments for their employees and better reliability of supply for their customers.

A new approach to handling maintenance

Most mining companies currently operate using a calendar-based preventive maintenance model. By prescribing maintenance work on fixed time schedules or based on basic operational statistics or best practices, organisations look to limit asset failures.

An even more basic approach to maintenance is reactive maintenance. This strategy involves letting an asset run until it fails. This approach only works for non-critical assets that have low replacement costs and do not disrupt overall safety and reliability of operations.

In order to combat current challenges such as aging assets, unpredictable environmental elements, and rising fuel costs, the approach to maintaining assets must be more strategic and optimised. A more strategic approach begins with condition-based maintenance. This strategy focuses on maintenance that is initiated on the basis of an asset’s condition, or in laymen’s terms “if it isn’t broke, don’t fix it.” The benefit of condition-based maintenance (CBM) is it eliminates any unexpected downtime by reducing or entirely preventing equipment failures that lead to health, safety and environmental risks. By eliminating unneeded maintenance activity (unlike preventive maintenance) CBM balances the cost of maintenance with the cost of equipment performance.

Implementing a successful CBM strategy isn’t without its challenges though. An organisation needs to conduct a thorough cost/benefit analysis and carry out an equipment audit first in order to ensure it is the right strategy for the organisation culturally and the right fit to help meet KPIs. The real-time monitoring aspect of this approach adds randomness and unpredictability to your maintenance organisation, which can become disruptive. Sometimes it is hard to turn data from sensors and asset monitoring into actionable outcomes.

By far the most proactive and optimised way to manage maintenance of equipment and other assets is through predictive maintenance. A predictive maintenance (PdM) approach requires not only the ability to continuously monitor asset performance through sensors such as vibration monitoring, but also a predictive engine that can process input and provide intelligent responses automatically. For the strategy to be effective as well, the collected data and resulting responses must all be captured and processed in a streamlined enterprise asset management system to ensure compliance, effective business intelligence, and customer satisfaction.

According to an ARC Advisory Group research report in 2015, 82% of assets have a random failure pattern, which renders preventive maintenance strategies essentially ineffective in managing equipment downtime and maximising equipment lifetime. In a predictive maintenance scenario, a sensor is put in place to monitor the performance of a key piece of equipment. After comparing historical data with real-time operating data, an alert triggers a utility organisation’s enterprise asset management software to automatically schedule a technician with the right skill set and the right parts to fix the failing asset. With an integrated enterprise asset management system working uniformly with a predictive analytics solution, downtime is eliminated, efficiency is maximised, and a chain of events is triggered that unify and optimise the entire process from inventory management to human resources.

Three ways predictive analytics optimises asset management

Predictive analytics has both immediate and long-term benefits for organisations when it comes to reliability and cost savings. When combined with monitoring and asset management systems, PdM can give mining companies better visibility over their assets, greatly reducing previously uncontrollable challenges such as distance and environment. By far the three most attractive benefits of a predictive maintenance strategy are:


  1. Improved customer satisfaction


With reliability comes increased customer satisfaction. Customers demand reliable supplies without the associated costs. By reducing operational costs while increasing performance, companies not only stay compliant with regulatory authorities but they also keep customers happy by ensuring reliable supplies.


  1. Reduced total cost of ownership


By prioritising maintenance activities and using cost/benefit analysis and historical data to help employees make intelligent decisions, unexpected costs are reduced or in best cases, eliminated. Organisations save significant amounts of money by avoiding equipment failure, a completely opposite approach to reactive maintenance. Not only do companies eliminate the cost of replacing assets, but they also eliminate the costs and penalties associated with equipment downtime, employee utilisation, environmental impact, and more. Using predictive monitoring also ensures real-time visibility into the health of your assets, allowing miners to get the most out of their asset investment.


  1. Increased safety and compliance


Predictive analytics provides visibility into assets that were previously hard to manage due to distance, size, age etc. By analysing data from multiple sources and comparing historic values with real-time input, organisations get a clear view as to how their assets are running. This allows for proactive management of risk thanks to the ability to easily prioritise potential problems, automate responses and prevent failures.

Meeting PdM challenges head on

Embracing predictive analytics involves dealing with a variety of issues that are best mitigated with careful consideration and organisational buy-in. The biggest of those is data itself. Data can be every company’s best friend and worst nightmare. It is imperative to practice good data management to ensure that the shift to a predictive analytics strategy goes smoothly.

Data quality and integrity are the foundation for success when it comes to predictive analytics that rely on accurate historical and real-time data to provide the best analysis and recommendations. Having one internal system like a strong integrated enterprise asset management solution eliminates data silos and ensures your data is managed, collected, and collated effectively.

With a fully extensible asset management system in place that includes maintenance scheduling, work orders and other functionality necessary for reliability-centred maintenance, a predictive analytics solution can effectively data mine the right information to inform operations of key cost-saving, proactive actions.


What’s next for PdM adoption

Digital transformation has made the impossible possible, creating efficiencies by reducing human intervention and error with complete automation and optimisation. Mining companies that embrace these changes will be better able to control the uncontrollable aspects of the industry cost-effectively, propelling them into a more profitable, sustainable future.

Monolithic legacy systems no longer have the capability to embrace new technologies and platforms without extensive, costly measures. Best-in-class organisations look to run more leanly with agile solutions that are built for easy integration and scalability. The future of PdM success begins with internal change management, a solid IT infrastructure as a foundation, and software that has the ability to process and apply new strategies as the world and expectations continue to change.

About the Author

Colin Beaney is the Global Industry Director for Asset Intensive Industries within IFS, where he has worked for nearly 18 years. Colin has been involved in implementing and project managing IFS software into many project and asset-intensive organisations in Europe and worldwide. Prior to this Colin worked as a management consultant specialising in maintenance continuous improvement philosophies such as TPM and RCM.


About IFS

IFS develops and delivers enterprise software for customers around the world who manufacture and distribute goods, maintain assets, and manage service-focused operations. The industry expertise of our people and solutions, together with a commitment to our customers, has made us a recognised leader and the most recommended supplier in our sector. Our team of 3,300 employees supports more than one million users worldwide from a network of local offices and through our growing ecosystem of partners. For more information, visit: IFSworld.com


By Colin Beaney, Global Industry Director for Asset Intensive Industries, IFS

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