Mining operations already collect large volumes of equipment information. Oil analysis, telemetry, inspections, vibration, thermography, work orders, and component history can all reveal changes in asset condition. The harder problem is bringing those signals together, determining which issues matter most, and making sure the right maintenance action is completed before a potential failure becomes a functional failure.
Recent conversations with mining maintenance, reliability, planning, engineering, and asset-management teams reinforced a consistent point: more data is not automatically better decision-making. Teams need a clear view of asset and component health, prioritized issues, technically credible recommendations, and a workflow that connects insight to execution.
This FAQ explains the relationship between condition monitoring, predictive maintenance, condition-based maintenance, and Condition Intelligence, with a practical focus on how mining organizations can convert fragmented equipment data into better-supported maintenance decisions.
- What is condition monitoring in mining?
Condition monitoring in mining is the process of collecting and evaluating information about equipment condition to identify abnormal change, deterioration, or emerging risk.
Common sources include oil analysis, equipment telemetry, vibration, thermography, non-destructive testing, visual inspections, maintenance history, operating behaviors, and work-order records. Depending on the operation, these sources may cover mobile equipment such as haul trucks, loaders, and drills as well as fixed plant assets such as crushers, conveyors, mills, and processing systems.
Condition monitoring creates the evidence needed to understand equipment health. Its value depends on whether that evidence can be interpreted, prioritized, and connected to maintenance action.
- How is condition monitoring different from predictive maintenance?
Condition monitoring describes the observation and assessment of equipment condition. Predictive maintenance uses condition information and analysis to identify emerging problems early enough to plan an intervention before an unexpected failure.
The terms overlap, but they are not identical. A mining operation can collect condition-monitoring data without having a mature predictive-maintenance process. Data may still sit in separate systems, be reviewed inconsistently, or produce alerts that do not lead to timely action.
A practical predictive-maintenance workflow therefore requires more than detection. It needs prioritization, a recommended response, ownership, execution, and verification.
- What is condition-based maintenance?
Condition-based maintenance is a maintenance approach in which decisions are influenced by the observed condition of an asset or component rather than only by a fixed calendar or operating-hour interval.
This does not mean scheduled maintenance is eliminated. It means actual condition provides additional evidence for deciding when to inspect, repair, monitor, or replace a component.
For mining organizations, condition-based maintenance can support both breakdown avoidance and optimized component management.
- What is Condition Intelligence?
Condition Intelligence is the process of turning multiple equipment-condition signals into clear, prioritized, and technically reviewed maintenance actions.
At Dingo, this combines a consolidated view of condition and maintenance information, advanced analytics, mining-specific historical context, and review by Condition Intelligence analysts.
The purpose is not to add another stream of alerts. It is to help maintenance teams understand what matters most, what action is recommended, and whether that action has been completed and validated.
- Why is collecting more maintenance data not enough?
More data does not automatically create a complete or decision-ready view of asset health.
A mining operation may have functional laboratory systems, OEM portals, telemetry platforms, inspection processes, spreadsheets, and an ERP or CMMS. The challenge is that each source may show only part of the equipment story.
When information is fragmented, reliability teams spend time locating, combining, and interpreting records before they can decide what requires attention. Important signals can also be missed when each source is reviewed in isolation.
- What equipment data can be brought together for asset-health analysis?
The relevant data depends on the site, asset class, systems, and available history, but it can include structured and unstructured condition and maintenance information.
Examples include oil analysis, telemetry or onboard monitoring data, vibration, thermography, inspections, non-destructive testing, maintenance records, component history, and ERP/CMMS work-order information.
A phased implementation may begin with one established data source, such as oil analysis, and add other sources as the workflow matures.
- How can mining teams identify which equipment issues need attention first?
Issues should be classified and prioritized according to condition, criticality, operational context, and required time to action.
The objective is to separate routine or normal information from the smaller subset that warrants technical review or maintenance action. This reduces the analysis burden on site teams and directs attention toward higher-risk issues.
Prioritization should remain explainable. Users need to understand the source data, the reason an issue was classified, and the basis for the recommended action.
- What makes a maintenance recommendation actionable?
An actionable recommendation explains the issue, the proposed response, its priority, and the expected timing clearly enough for the maintenance organization to decide and act.
A useful recommendation should answer practical questions: What was observed? Why does it matter? What should be checked or completed? How urgent is it? What evidence should be collected afterward?
The recommendation also needs an owner and a path into planning or work management. Without execution and follow-up, even a technically correct finding may not create operational value.
- How can mining operations avoid introducing another disconnected dashboard?
The solution should process information on behalf of the maintenance team and fit into existing decision and work-management processes rather than requiring users to monitor another standalone screen.
A useful condition-management workflow should show how data becomes a reviewed recommendation, how the site accepts or rejects it, how accepted work is planned, and how the outcome is verified.
Dashboards can support visibility, but they should not be the final product. The real outcome is a technically reviewed maintenance recommendation that is acted on, completed, and validated.
- What does an end-to-end condition-based maintenance workflow look like?
A complete workflow moves from data collection to classification, expert review, prioritization, site decision, work execution, and validation.
Dingo’s workflow is: condition and maintenance data enters the platform; routine information is filtered; flagged issues receive further analysis; a Condition Intelligence expert reviews the evidence; a recommendation is issued; the customer accepts or rejects it; accepted work is planned through the site’s process; and the result is followed through to completion and verification.
The exact configuration varies by customer, data availability, and integration scope.
- Can condition recommendations connect to an ERP or CMMS?
Yes, Dingo can support integration with ERP/CMMS workflows, but the exact connection and automation depend on the customer’s systems and implementation scope.
Within Dingo, accepted condition recommendations can be converted into work-order notifications or work orders, helping maintenance teams move from recommendation to execution within their existing maintenance workflow.
This should be assessed early in an evaluation. Key questions include which system owns the work order, what information moves between systems, whether integration is required in the first phase, and how completion status returns to the condition workflow.
- Why is human review important in mining maintenance analysis?
Human review adds operational context, technical judgment, and accountability to analytics-generated findings.
Mining equipment operates in different environments, duty cycles, maintenance regimes, and production contexts. A signal that is significant at one site may require different interpretation at another.
Dingo uses a human-in-the-loop approach in which experienced Condition Intelligence analysts validate and refine recommendations. The intent is to combine analytical scale with mining and maintenance expertise, not to replace site professionals.
