How operational data can improve asset reliability decisions in mining

Mining operations generate significant volumes of data every day. Sensors, control systems, data historians and maintenance platforms continuously record information about how assets are operating and performing. 

The challenge is turning this data into information that can support practical decisions.

For asset-intensive mining operations, this is particularly important when assessing reliability. Knowing when an asset failed is useful, but understanding the operating conditions surrounding the failure can provide further insight into its causes and operational significance. 

Adding context to asset reliability data 

Reliability, Availability and Maintainability (RAM) studies provide a structured way to examine how asset performance can affect the wider operation.

However, the quality of the analysis depends on the quality and context of the available information. Maintenance records may show that an asset experienced a failure, but operational data can provide additional context. This might include what was happening within the plant, which production conditions were present and how the wider system was performing at the time. 

Connecting these sources of information can help organisations move beyond recording individual failures towards understanding their operational significance. 

At a fundamental level, sensors and control systems convert physical activity into operational data. A data historian stores time-based information collected from equipment and control systems, allowing operating conditions to be examined over time. When data from these sources is structured and analysed alongside relevant maintenance information, it can support a more complete assessment of asset performance. 

Turning a raw signal into information that supports a decision may involve several stages of collection, validation and interpretation. Reliability analysis provides one example of how this additional context can influence the way asset performance is understood. 

Understanding reliability under different operating conditions 

A RAM study undertaken for a mining operator examined how asset reliability varied under different processing conditions. 

The study combined historical work-order information with operational data from data historians and operational technology (OT) systems to build and validate a RAM model for the operator’s processing environment. 

Rather than assessing asset performance using one set of operating assumptions, the analysis considered four throughput regimes: low, sustained, peak and campaign production. In this context, the regimes represented different production conditions under which the performance and importance of individual assets could be considered. 

This approach allowed the study team to examine how an asset’s importance to the overall system could vary as operating conditions changed. 

Equipment that represents relatively limited production risk under one throughput regime may have a different effect when a plant moves towards peak or campaign production. Considering these variations can provide additional context for reliability and maintenance decisions. 

From reliability data to maintenance priorities 

By analysing historical asset information alongside operating conditions, the study examined whether maintenance priorities reflected asset criticality under different production requirements. 

The analysis was also used to consider potential improvements in equipment redundancy, spare-parts strategies, workforce constraints, automation and operational monitoring. 

The process identified areas where the quality and governance of the underlying data could be strengthened. This is an important consideration because the usefulness of a reliability model, or a broader analytics initiative, depends on the information supporting it. 

Operational data does not remove the need for engineering judgement or maintenance expertise. Instead, it can provide additional evidence to help reliability and maintenance teams understand the circumstances surrounding an event and its possible effect on the wider operation. 

Building a stronger operational data foundation 

RAM analysis is one example of how operational data can support decision-making when it is analysed in the appropriate context. 

Across mining operations, information is generated by many assets and moves through operational and business systems. Connecting this information may involve both operational technology, which monitors or controls physical equipment and processes, and information technology (IT), which supports the storage, management and use of information across the organisation. 

How this data is structured, connected and governed affects the ways in which it can be used. A well-understood operational data foundation can support reliability analysis and may also provide a basis for other forms of operational analytics. 

Conclusion 

Operational data can add important context to conventional reliability and maintenance records. When information about asset events is considered alongside production conditions, it can help mining organisations examine not only when an asset failed, but also what was happening around the event and how the wider system may have been affected. 

The value of this approach depends on reliable source data, appropriate governance and a clear understanding of the operating environment. Bringing these elements together can support more informed reliability and maintenance decisions while helping organisations identify where their operational data practices could be strengthened.

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