Predictive Maintenance That Works: From First Signal to Closed Work Order - GRX26 Workshop
June 23, 2026
At GRX26, Tomer Srulevich, Chief Business Officer at Razor Labs, and Andrew Kaushal, VP Sales APAC, led a workshop exploring why predictive maintenance initiatives continue to struggle despite the growing number of monitoring systems deployed across mining fleets.
Using real mine fleet data and operational scenarios, the session followed the journey from the first anomaly to a closed work order, highlighting the operational, technical, and organizational challenges that often prevent maintenance teams from turning data into action.
Why Predictive Maintenance Still Falls Short
Mining operations generate more equipment data than ever before. Sensors, OEM systems, condition monitoring platforms, and maintenance software continuously collect information intended to improve reliability and reduce downtime.
Yet many operations continue to face familiar challenges:
- False alarms
- Missed failures
- Reactive maintenance
- Delayed diagnosis
- Difficulty scaling expertise across sites
- Complex mixed-fleet environments
The reality is that most predictive maintenance initiatives do not fail because of a lack of data. They fail because organizations struggle to transform data into reliable maintenance decisions and actionable work orders.
The Expertise Gap
One of the workshop’s central themes was the growing challenge of diagnostic expertise in mining operations.
Remote sites often struggle to access experienced specialists who can translate equipment data into accurate diagnoses and maintenance actions. OEM support is not always immediately available, and mixed-fleet environments frequently require expertise across multiple manufacturers and equipment types.
As a result, maintenance organizations face a difficult challenge: scaling expert-level diagnostics across large fleets, multiple sites, and diverse equipment populations.
Beyond Threshold-Based Monitoring
Many monitoring systems still rely on simple threshold-based alerts. While straightforward to implement, this approach often creates unnecessary noise and can miss developing failures.
Temporary operating conditions may trigger alarms even when there is no actual deterioration, resulting in large volumes of false positives. At the same time, many failures do not appear as simple threshold violations but as subtle changes in relationships between sensors, operating conditions, and equipment behavior.
The result is a familiar challenge for maintenance teams: too many alarms and not enough actionable insight.
Why Mining Requires Specialized AI
As AI adoption accelerates across industries, many organizations are exploring how generic AI models can support maintenance operations.
However, mining equipment generates highly specialized operational data that generic AI systems were never designed to understand.
The workshop demonstrated why mining operations require AI models trained on real equipment behavior, operational context, and failure mechanisms. These specialized models can identify complex failure patterns, compare equipment performance across fleets, and detect developing issues earlier than traditional monitoring approaches.
Most importantly, they help bridge the gap between anomaly detection and maintenance action.
Extending Component Life Through Condition-Based Maintenance
The workshop also explored how condition-based maintenance can help operations maximize component life.
Many sites continue to replace major components according to fixed OEM schedules regardless of their actual condition. However, when oil analysis, subsystem health indicators, alarm history, and trend data all indicate healthy operation, assets may continue operating safely beyond planned replacement intervals.
In qualifying cases, this approach can deliver:
- Up to 25% longer component life
- Fewer expensive component replacements
- Better capital efficiency
- More value generated before asset retirement
Building Trust in AI
Trust remains one of the most important factors in AI adoption.
For maintenance and reliability teams to act on AI recommendations, they need to understand why decisions are being made. The workshop demonstrated how explainable AI provides supporting evidence behind every diagnosis, allowing users to review the underlying data, validate conclusions, and build confidence in the system over time.
This transparency helps bridge the gap between advanced analytics and human decision-making, enabling organizations to scale AI adoption more effectively.
From First Signal to Closed Work Order
The central message of the workshop was simple: successful predictive maintenance is not determined by the amount of data available. It is determined by an organization’s ability to transform data into timely, accurate, and actionable maintenance decisions.
Watch the full workshop recording above to learn how mining operations can move from first signal to closed work order while reducing false alarms, detecting failures earlier, and improving maintenance outcomes.
Interested in Learning More?
If you’re looking to reduce false alarms, detect failures earlier, and improve maintenance decision-making across your mining operation, we’d be happy to discuss how AI-driven predictive maintenance can support your goals.
Contact the Razor Labs team to learn more.
