Mining Meets AI: How Razor Labs is Revolutionizing Equipment Reliability with AI Sensor Fusion

Mining meets AI in a powerful way. In mining, an hour of downtime can cost companies hundreds of thousands of dollars. But what if that failure could be detected weeks before it happens? That’s where AI sensor fusion comes into play, and Razor Labs is leading the way in applying it to the mining industry.

In this article, Michael Zolotov, Co-Founder and CTO of Razor Labs, discusses how AI technology, designed to detect failures early, is changing the game for mining operations. Michael and his team have created an “automated doctor” for mining equipment, using AI to fuse sensor data and predict failures—helping clients like Glencore save millions and significantly reduce downtime.

Of course, downtime events will still happen. Which is where our EasyRCA comes into play. With our AI assistance and streamlined platform, we help companies get to root causes faster. Watch the full podcast episode on YouTube!

Quick Overview (TL;DR)

• AI sensor fusion technology helps mining teams detect failures weeks before they would typically notice.
• Razor Labs’ AI system fuses data from multiple sensors, providing real-time insights into equipment health.
• The system detects both failure modes and operator abuse, allowing for proactive maintenance.
• Major clients like Glencore have already saved millions by integrating this technology.
• AI has revolutionized failure detection, reducing downtime and maintenance costs.

Table of Contents

• Michael’s Background and Razor Labs’ Mission
• How AI Sensor Fusion Works in Mining
• The Importance of Proactive Maintenance
• AI vs Traditional Mining Systems: Why AI Wins
• Key Success Stories: Glencore and Other Clients
• Razor Labs’ Vision for the Future of Mining AI
• How EasyRCA Supports Proactive RCA in Mining

Michael’s Background and Razor Labs’ Mission

Michael Zolotov’s journey into mining AI began with a background in artificial intelligence. His team at Razor Labs recognized early on that while traditional tools in the mining sector helped monitor equipment, they still missed a huge chunk of potential failures. That’s when the idea for AI sensor fusion was born.

“We wanted to build an automated doctor for equipment,” Michael explains. “Just like a doctor combines X-rays, blood work, and ultrasound to diagnose patients, we combine different sensor data to diagnose mining equipment issues.”

The result? An AI system that detects failures weeks ahead of traditional monitoring systems, helping mining teams act fast to prevent costly downtime.

How AI Sensor Fusion Works in Mining

AI sensor fusion refers to the combination of data from multiple sensors—such as temperature, vibration, and pressure—to provide a holistic view of equipment health. The key innovation here is that AI algorithms are used to synthesize this data and detect patterns that would be invisible to traditional monitoring systems.

Key Features of AI Sensor Fusion:

• Multiple sensor integration: Combines diverse data points from vibration, temperature, and more.
• Failure prediction: Detects potential failures weeks before traditional systems.
• Real-time insights: Provides immediate actionable data for maintenance teams.
• Automated diagnosis: AI provides a root cause diagnosis along with recommended actions.

By learning the interdependencies of equipment performance, this system can predict failures with a level of accuracy and speed that traditional methods can’t match.

The Importance of Proactive Maintenance

Proactive maintenance isn’t just a buzzword—it’s a game-changer. With Razor Labs’ AI, mining teams can catch small issues before they escalate into major breakdowns.

For example, Michael shares a simple yet powerful example:

“If a gearbox is running low on lubrication, you could fix it in 20 minutes. But if you wait too long, you’ll need a complete replacement, costing thousands in parts and downtime.”

This approach—acting on issues early—has the potential to prolong equipment life and dramatically reduce maintenance costs. The system also helps optimize equipment use, improving efficiency across the board.

AI vs Traditional Mining Systems: Why AI Wins

Unlike traditional systems that set high failure thresholds to avoid constant false alarms, AI systems can fine-tune those thresholds based on real-time operating conditions. Michael explains:

“Imagine a hauler truck where operational conditions change all the time—engine load, payload, RPM. Traditional systems just can’t keep up with that level of data. AI can, and it does—detecting failures long before they hit the traditional thresholds.”

By reducing false positives and detecting early signs of failure, AI ensures that problems are caught before they grow into expensive repairs or shutdowns.

Key Success Stories: Glencore and Other Clients

Razor Labs’ AI technology has already had measurable success with clients like Glencore, one of the world’s largest mining companies. Michael highlights the impact:

“For Glencore, we reduced failure detection time by 3-5 weeks, which means millions saved in downtime and maintenance costs.”

This example is just one of many where AI sensor fusion is proving to be a valuable asset in proactive mining operations. From detecting early signs of wear in mobile fleets to reducing unexpected failures in fixed assets, Razor Labs’ technology is setting new standards for reliability in mining.

Razor Labs’ Vision for the Future of Mining AI

Looking to the future, Razor Labs is expanding its reach. Currently, they’re focusing on providing AI-powered solutions for both fixed assets and mobile fleets, including haul trucks and excavators. Michael explains that their goal is to offer a holistic view of equipment health across entire mining sites.

“We’ve completed coverage for all fixed assets, and now we’re expanding to more complex equipment like drag lines and bucket wheel reclaimers—systems that require specialized AI analysis.”

In the coming years, Razor Labs plans to expand further into adjacent industries like oil refineries and power plants, where equipment uptime is equally critical.

How EasyRCA Supports Proactive RCA in Mining

At EasyRCA, we recognize that even with the best preventitaive plans and tool, downtime events will happen. When they do, great AI tools need to be complemented with effective root cause analysis (RCA) to ensure long-term reliability. Our RCA Turbo with AI feature accelerates RCA investigations by auto-suggesting probable causes based on historical data and past investigations, streamlining the process for faster decision-making.

EasyRCA’s intuitive platform is designed to help teams not only identify problems but also take actionable steps to prevent them from recurring. Whether you’re tracking small faults or large-scale failures, our customizable RCA templates ensure that your team stays on top of problems before they affect your bottom line.

Ready to unlock the potential of proactive RCA in your mining operations? Start your trial with EasyRCA.

Want to learn more about how AI and RCA can work together to improve equipment reliability? Watch the full podcast episode on YouTube, or listen on Spotify and Apple Podcasts.