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The power of smart PdM in metals and mining

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Global Mining Review,

Alexander Hill, Chief Global Strategist and Co-Founder, Senseye, outlines how automation and digital transformation at scale can help drive more efficient and sustainable operations.

The power of smart PdM in metals and mining

Companies worldwide understand the need to haul their ageing maintenance processes into the era of automation and achieve digital transformation at scale to drive more efficient and sustainable operations.

The impact of digital investment on maintenance costs, downtime, and OEE is already evident:

  • Collectively, the top 50 firms spent an estimated US$62.5 billion on plant and equipment maintenance in their last reported financial years.
  • Maintenance costs typically represent 1 – 3% of these firms’ total annual revenues.
  • A leading steel firm reported a 2.5% reduction in annual maintenance costs after implementing real time monitoring across key assets.
  • An Australian organisation currently trialling smart PdM solutions at an ore operation has cut lost production time on ore crushers by 94%.
  • One aggregates producer has improved operational efficiency by between 15% and 20% by rolling out a digital transformation scheme including PdM as part of its smart factory initiative.

The power of smart PdM in metals and mining

Senseye’s analysis of the 50 largest metals and mining companies shows that smart predictive maintenance (PdM) strategies are now achieving mainstream adoption.

More than half (58%) of the organisations studied have identified PdM as a strategic initiative and are trialling in at least one business area.

PdM is not just driving more efficient industrial operations. It is also delivering:

  • Lower maintenance costs: Maintenance costs in the mining industry regularly account for between 30% and 50% of total site operating costs. Studies show that condition monitoring (a key first step for predictive maintenance) can improve overall equipment effectiveness, on average, by 6% – reducing company spend on parts and machinery.
  • Less downtime: Previous research from Senseye has shown that – across the Fortune Global 500 – heavy industrial companies lose US$225 billion collectively per year due to unplanned downtime. A predictive approach to maintenance automates the analysis of machine health, identifies early warning signs of deterioration and directs engineers to where they are needed most. It leads to a more robust, informed asset maintenance strategy where more successful interventions are made and fewer assets run to fail.
  • Improved health and safety: The Center for Disease Control and Prevention (CDC) reports that 30% of all injuries and fatalities from underground mining in the US are due to machinery maintenance accidents.
  • PdM means interventions can be better planned and completed first-time. But a PdM-empowered schedule can also reduce the need for manual inspections by over 50% without sacrificing operational quality. This leads to less over-maintenance and reduces the time employees are at risk.
  • A scalable solution: Automating asset health analysis is critical for large firms looking for more comprehensive monitoring across their entire operations.

Smart PdM uses advanced machine learning algorithms to learn the characteristics of each monitored asset automatically, enabling the capability to be rolled out easily at scale. The system automatically provides the critical data needed for engineers to make decisions and gives them more time to focus on maintenance.

Nearly all leading firms have got their operational data into a usable format, collected centrally within their organisation. And the majority are taking the next step to make better use of that data for driving effective, cost-efficient daily operations.

It is crucial that those who are not yet at this stage take advantage of a powerful, developing field of AI-enabled tools that integrate quickly and provide immediate value in their operations.

For the top 50 metals and mining companies, which spent a collective US$62.5 billion on maintenance costs in 2020 – 2021, a 40% reduction in maintenance costs would save a total US$25 billion a year in maintenance efficiencies.

Case Study: Cameco – The power of bringing your data together

Cameco is a Canadian energy firm that started its digital transformation in earnest in 2019. Its longest-running initiatives are in operations management, where it seeks to improve operational decision-making, safety management, and sustainability.

A key element in these initiatives is asset maintenance, representing approximately 25% of Cameco’s overall operating costs at its various mining operations. Improving asset management began with automating data collection.

Cameco had struggled to analyse multiple streams of condition monitoring data from assets in the past. As a result, the company found it hard to understand what went wrong in the event of asset failure.

Senseye PdM, using data collected by existing sensors, now gives Cameco a continuous stream of information on the condition and predicted future condition of its machines. This will lead to improved sustainability, efficiency and a reduction in downtime.

Using the technique of multivariate analysis, Cameco is poised to make better operational decisions across the business:

  • Better data collection means a clearer understanding of what went wrong and stronger insight to avoid repeated issues in the future.
  • Importantly, it gives a clearer understanding of where and what you should be monitoring to find the best insights on asset health.
  • Continuous data collection reveals which forms of condition data are relevant for measuring asset health – and how the relationship between data points can show an upcoming fault.
  • It provides an opportunity to change operating regimes before they lead to equipment failure. The sooner you know, the sooner you can plan interventions and take advantage of windows in your schedule to apply a maintenance intervention without compromising production.

The results

It is crucial that those who are not yet at this stage take advantage of a powerful, developing field of AI-enabled tools that integrate quickly and provide immediate value in their operations.

Implementations of more sophisticated predictive maintenance has demonstrated its ability to deliver:

  • 40% reduction in maintenance costs.
  • 85% improvement in downtime forecasting accuracy.
  • 50% reduction in unplanned machine downtime.
  • 55% increase in maintenance-staff productivity.

To download a copy of Senseye’s Connecting Asset Data at Scale report, please click here.

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Canadian mining news Mining equipment news North American mining news