Predictive Maintenance: How AI Can Prevent Crusher Failures

In a crushing operation, unplanned downtime isn’t just inconvenient — it’s expensive. A sudden bearing failure, a jammed crusher chamber, or an overheated motor can stall an entire operation for hours or even days. Traditional preventive maintenance helps, but it often relies on fixed schedules, which may miss early signs of failure. This is where AI-powered predictive maintenance is changing the game.

By analyzing real-time machine data and historical performance trends, AI can anticipate failures before they happen, keeping plants running longer, safer, and more efficiently.

From Reactive to Predictive

Most crushing plants today still follow a reactive or calendar-based maintenance model:

• Reactive: Fix it when it breaks
• Preventive: Replace parts after X hours or tons

But neither approach guarantees the part won’t fail early — or be replaced too soon.

Predictive maintenance, on the other hand, uses AI and machine learning to analyze data from:

• Vibration sensors on motors, gearboxes, and shafts
• Temperature probes on bearings and oil reservoirs
• Power draw patterns from crushers and screens
• Moisture and particle size sensors from screening units

This data is used to model normal operating conditions and flag deviations that point to potential issues — often days or weeks in advance.

How It Works in Practice

Taurian’s upcoming predictive maintenance system is designed to plug into our automated plants, especially on key equipment like:

• Jaw and cone crushers
• Vibrating screens
• Motorized feeders
• Belt drives and pulleys

The system uses cloud-based analytics to identify early warning signs, such as:

• Rising vibration levels in a cone crusher’s eccentric
• Abnormal temperature spikes in screen deck bearings
• Gradual increase in power draw indicating feed blockages

Once identified, the system generates a maintenance alert with specific actionable recommendations — from tightening a loose drive coupling to pre-ordering a replacement part before failure.

Case Example: Iron Ore Crushing in Chhattisgarh

At a 400 TPH iron ore site, Taurian’s beta predictive system flagged abnormal vibration trends in a jaw crusher. Within 72 hours, a crack in the flywheel hub was visually confirmed — something that would’ve otherwise gone unnoticed until total failure. Because of early intervention, only 3 hours of downtime were needed, avoiding what could have been a 2-day shutdown and ₹12–15 lakh in lost production.

More Than Just Uptime

Predictive maintenance not only reduces breakdowns but also:

• Lowers inventory costs (parts are only stocked when needed)
• Increases component life through optimized usage
• Improves safety by minimizing emergency interventions
• Offers better audit trails for compliance and reporting

The Future Is Self-Aware Plants

With AI models continuously learning and improving, crushing plants are becoming self-aware systems — not only reacting to problems but learning from them. Over time, this reduces operating costs, improves output quality, and ensures plant managers can make smarter, data-driven decisions.

In the competitive world of crushing and screening, plants that learn are plants that lead.