3 minute read
Asset reliability in mineral processing
Asset reliability is the bedrock of operational excellence in mineral processing. This article explores its benefits and essential metrics and equips you with practical strategies to build a robust program. By implementing these strategies, you, the on-site maintenance professional, can significantly boost production uptime and efficiency, inspiring your team with the results.
Why asset reliability matters
In the high-pressure world of mineral processing, reliable equipment is the backbone of smooth operations. Here’s how a strong asset reliability program directly impacts your team’s success:
Boosted uptime:
Minimise breakdowns and keep equipment running at peak performance through preventive and predictive maintenance techniques.
Reduced costs:
Costly repairs and production loss due to equipment failures become a thing of the past. Proactive maintenance identifies and addresses potential issues before they escalate.
Enhanced worker safety:
Unreliable equipment poses safety hazards. Asset reliability practices create a safer work environment for your crew.
Smarter asset management:
Move beyond reactive “fix-it-when-it-breaks” maintenance. Leverage technologies like predictive maintenance (PdM) to analyse sensor data and anticipate failures before they occur. This allows for targeted interventions, maximising equipment lifespan and optimising resource allocation.
Understanding key concepts
Asset reliability vs. asset availability:
Reliability refers to an asset’s ability to perform consistently without failure. Availability is a broader concept that considers reliability and the time it takes to repair a failed asset. High reliability contributes to high availability, but they are not the same.
Mean Time Between Failure (MTBF) and Mean Time to Repair (MTTR):
MTBF is a statistical measure of the average time between equipment failures. MTTR is the average time to repair a failed asset and get it back in operation. Both are crucial for understanding asset performance and planning maintenance activities.
Building a world-class asset reliability program
Here are essential practices to implement on-site:
Preventive Maintenance (PM):
Develop a customised PM program for each asset based on historical data and manufacturer recommendations. This significantly reduces equipment failures.
Predictive Maintenance (PdM):
Predictive Maintenance (PdM): Utilise techniques like vibration analysis, oil analysis, and thermography to identify equipment degradation before failures occur. Analyse data trends to schedule targeted maintenance activities.
This is where software solutions like Aspen Mtell® can be used to enhance asset reliability and performance with early warnings and real-time actionable insights, paving the way to operational excellence.
Reliability-Centered Maintenance (RCM):
This methodology analyses equipment functions, failure modes, and consequences to develop an optimised maintenance plan. Data on failure history and criticality is essential for RCM implementation.
Root Cause Analysis (RCA):
Analyse past equipment failures to identify underlying causes and prevent recurrence. Data on failure events, operating conditions, and maintenance history aids in identifying root causes.
Continuous improvement:
By continuously monitoring and improving your asset reliability program, you are fostering a culture of excellence and innovation. Data analysis helps identify trends, track KPIs, and measure the effectiveness of implemented strategies, inspiring your team to strive for continuous improvement.
Challenges and overcoming them
Budget constraints:
Focus on cost-effective practices like PM optimisation and data-driven maintenance decisions. Prioritise critical assets and implement PdM for early problem detection to avoid costly failures.
Skilled labour shortage:
Implement knowledge transfer programs and train existing personnel on PdM techniques and data analysis—partner with external service providers for specialised tasks.
Data overload:
Focus on collecting and analysing relevant data using a structured approach. Utilise CMMS (Computerised Maintenance Management System) software to manage and integrate data from various sources. This allows for data-driven decision-making without relying solely on expensive software solutions.
Aging infrastructure:
Older assets are prone to failures, requiring frequent maintenance, parts replacements, and total overhauls. Continued maintenance could work, but it would only be a band-aid solution. Modernising the infrastructure is the better path to choose and will require comprehensive resource planning for asset replacement or refurbishment.
Resistance to change
Shifting from reactive to proactive maintenance cultures is quite formidable because of entrenched practices and mindsets. Open communication, sincere engagement, and adequate training and support can help frontline workers accept the changes. These also facilitate smoother transitions.
The future of asset reliability
The future of asset reliability is bright, with emerging technologies offering even greater possibilities:
Advanced analytics and Machine Learning (ML):
Implementing these techniques can provide deeper insights from data, predict failures with higher accuracy, and optimise maintenance strategies.
Integration of IoT (Internet of Things):
Sensors embedded in equipment can provide real-time data for continuous monitoring and proactive maintenance.
Digital twins:
Creating digital models of plant assets can allow for the simulation and optimisation of maintenance strategies.
Summary
By focusing on these practical strategies and effectively leveraging data, even without the latest software, you and your team can significantly improve asset reliability, driving production excellence in your mineral processing operations. Now, it’s time to implement these insights and start reaping the benefits of a robust asset reliability program.
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