Predictive Maintenance Implementation Strategies

In our previous “Predictive Maintenance: Benefits and Challenges” article, we explained predictive maintenance (PdM). Also we discussed difference between reactive, preventive and predictive maintenance, highlighted benefits and challenges of implementing this strategy. The predictive approach not only anticipates equipment faults and failures but also optimises maintenance schedules and resources. This article describes the best practices and strategies in predictive maintenance and its implementation in the mining industry, including examples from leading mining corporations.

For the successful implementation of PdM at production sites, it is important to follow a set of best practices and strategies that will help ensure the effective operation and the achievement of desired results:

 

1. Focus on Critical Machinery and Equipment: It is essential to start the PdM program by identifying and prioritising assets that have incurred the most substantial losses. This ensures that maintenance efforts are focused on critical areas first.

2. Gradual Implementation: Start with pilot projects on critical equipment and gradually extend the program, learning and adapting as the process evolves. This allows for a more manageable analysis of the predictive maintenance model and its impact before scaling up.

6. Integration into Daily Operations: Embed PdM insights into the daily operational workflow for timely decision-making.

 

7. Continuous Review and Improvement: Regularly review and update PdM strategies based on new data, insights, and technological advancements.

8. Collaboration with Tech Partners: If in-house expertise is lacking, consider collaborating with outside vendors, experts or consultants who specialise in predictive maintenance. They can offer valuable guidance throughout the implementation process and provide ongoing support.

9. Change Management: A systematic approach to managing the organisational change associated with the implementation of new technologies is vital for the successful adoption of predictive maintenance strategies.

Predictive maintenance is widely used in various industries, including manufacturing, energy, transportation, and mining. In the manufacturing sector, for example, predictive maintenance is used to monitor critical machinery such as turbines, compressors, and conveyor systems. In the energy sector, it helps maintain the reliability of power generation equipment. While in transportation, it ensures the smooth operation of vehicles and infrastructure.

In the mining industry, best practices and strategies in predictive maintenance play a crucial role in maintaining the reliability of heavy machinery, such as crushers, conveyor belts, and excavators. By preventing unexpected breakdowns, they help minimise costly downtime and maintain production targets.

Leading mining companies such as BHP, Rio Tinto, Newcrest, and Newmont are recognised leaders in adopting the best predictive maintenance practices and strategies. This is evidenced by numerous examples of successful implementations and tangible results within the industry.

• BHP: BHP has been utilising the best practices in predictive maintenance to monitor the health of their large mining equipment, significantly reducing downtime and maintenance costs. By leveraging data analytics, BHP has been able to predict equipment failures, schedule maintenance more effectively, and improve overall operational efficiency.
• Rio Tinto: Rio Tinto’s deployment of PdM technologies has enabled the company to optimise its maintenance schedules, leading to increased equipment uptime and reduced operational costs. Their advanced data analytics capabilities have played a pivotal role in predicting and preventing equipment failures.

Downtime for Rio Tinto’s enormous haul trucks, which haul iron ore around the clock, translates directly to lost revenue. A single day out of service costs a hefty $2 million, and that price tag jumps even higher for tow-away situations. To combat this expensive issue, Rio Tinto embraced the power of the Internet of Things (IoT) for predictive maintenance. The company outfitted its fleet of 900 dump trucks with a whopping 92 sensors each. These sensors constantly monitor engine, drivetrain, and wheel performance, generating a daily data stream of a staggering 4.9 terabytes. Using advanced computer algorithms, including deep learning, this data is analysed in near real-time. The system can, for example, pinpoint a 60% chance of a part failing within three weeks. With this foresight, Rio Tinto can proactively order parts and schedule maintenance, drastically reducing unplanned downtime and saving significant costs.

Predictive Conveyor Maintenance and Mill Liner Wear Monitoring Case Studies illustrate how best practices and strategies in predictive maintenance have been successfully applied to critical areas, delivering tangible benefits.

The implementation of predictive maintenance practices and strategies is transforming the mining industry by offering a more efficient, cost-effective, and safe approach to equipment maintenance, and reliable and sustainable operations. While the transition from reactive to predictive maintenance poses its challenges, the long-term benefits are clear and substantial. By focusing on critical machinery, leveraging IoT and data analytics, and integrating PdM into daily operations, mining companies can significantly reduce downtime, optimise resource allocation, and enhance overall safety. The successes of industry leaders like BHP, Rio Tinto, Newcrest, and Newmont underscore the transformative potential of predictive maintenance.  Embracing the best practices and strategies in predictive maintenance can lead to significant operational improvements and cost savings, setting a new standard in mining operations management. The future of mining maintenance lies in predictive strategies, and embracing this approach is essential for staying ahead in an increasingly competitive landscape.