How PPRM Cut Unplanned Downtime by 47% in 12 Months

30 Apr, 2026

TMT Long Products Mill | 400,000 TPA | PPRM Mill Stands C1 – C16

The Problem

A 400,000 TPA TMT rebar mill was operating on a time – based maintenance (TBM) approach replacing components at fixed intervals and reacting only after failures occurred.

The outcome: –

• 5 – 7 unplanned breakdowns per month
• 4 – 6 hours lost per event
• Hundreds of rolling hours lost annually

The primary failure points:

• Main reduction gearboxes
• Cardan shaft assemblies
• Spindle support bearings

All critical drivetrain components were being run to failure, with no early warning system in place.

The Shift: From TBM to Condition-Based Monitoring (CBM)

PPRM implemented a focused, signal-driven CBM strategy – not by adding more sensors, but by monitoring the right components, in the right way.

Measured Impact (12 – Month Stabilized Period)

• 47% reduction in unplanned downtime
• ~169 hours of rolling time recovered annually
• ~13,500 tonnes incremental production

Where PPRM Monitored – And Why

1. Main Reduction Gearboxes

Sensors: Vibration (velocity & acceleration)
Parameters:

• RMS velocity (overall condition)
• Envelope acceleration (bearing defects)
• Gear mesh frequency + sidebands

Why:
Gearboxes provide high signal clarity, enabling early and reliable fault detection.

2. Cardan Shaft Assemblies

1)Balance cardan shaft having dynamic balancing grade .
2)Torque rating of Cardan shaft and rigidity etc.

Designed to operate under high torque and critical load conditions, ensuring stability and precision during operation. Engineered with a defined breaking torque to prevent damage in case of overload. Delivers reliable torque transmission while maintaining alignment and overall system integrity. Developed through in-house R&D to combine precision, safety, and protection of the most valuable equipment in rolling mill applications.

Detection Scope:

• Imbalance (1× rotational frequency)
• Misalignment (2× frequency components)
• Mechanical looseness

Insight:
Failure signatures appeared 2 – 5 weeks before functional degradation, allowing planned intervention.

3. Spindle Support Bearings

Sensors:

• Localized vibration
• Temperature at chock level

Failure Modes Captured:

• Lubrication breakdown
• Progressive wear
• Thermal instability under load variation

4. Housingless Stand Bearings (Design – Specific Strategy)

Instead of over-instrumentation, PPRM adopted a design-aware monitoring approach.

Monitoring Focus:

• Temperature trending (50 – 70°C operating band)
• Lubrication flow (shift-wise checks)
• Periodic inspection + condition – triggered action

Why vibration was excluded:

• High structural noise
• Poor repeatability
• Weak correlation with early – stage defects

Result: Better diagnostics by measuring less – but measuring right

Implementation Methodology

Phase 1 (Months 1 – 2): Baseline Development

• 18 sensors deployed across drivetrain
• Data captured across speeds, sections, and grades

Outcome:
Established statistical baselines, not rigid thresholds

Phase 2 (Month 3): Alert Logic Definition

Alert Level	Condition	Action Window
Yellow	+20–30% over baseline	Plan within 1–2 weeks
Red	+40–50% OR fault frequency emergence	Act within 48–72 hrs

• Temperature >70°C = immediate action trigger

Phase 3 (Months 4 – 6): Maintenance Integration

• Alerts linked to weekly shutdown planning
• Shift from reactive maintenance → planned intervention

Phase 4 (Months 7 – 12): Stabilization

• Trend validation
• Reduction in false positives
• Stronger correlation between data and physical inspection

Representative Case (Validated)

Location: C5 Roughing Stand
Component: Gearbox input shaft bearing

Observed:

• ~30% increase in RMS vibration
• Emerging sidebands around gear mesh frequency

Action:

• Day 1: Yellow alert triggered
• Day 3: Trend confirmed
• Within 1 week: Planned shutdown scheduled

Execution:

• Bearing replaced under controlled conditions

Inspection Findings:

• Early – stage raceway pitting
• No secondary gear damage

Estimated remaining life: 2 – 3 weeks

Operational Impact

• Avoided catastrophic gearbox failure
• Eliminated emergency stoppage
• Reduced spares escalation cost
• Maintained production continuity

Key Technical Insight

Effective CBM in long product rolling mills is not about maximizing sensors – it’s about maximizing signal relevance:

• Monitor components with clear fault signatures
• Avoid measurement where signal-to-noise ratio is poor
• Use relative baselining, not fixed OEM thresholds
• Integrate alerts into maintenance decision workflows, not just dashboards

Closing Thought

Downtime reduction isn’t just a maintenance win – it’s a production multiplier.

In this case, the shift from reactive to predictive didn’t just prevent failures – it unlocked 13,500 tonnes of additional output without adding a single new stand.
Connect with us: sales@pprm.in
Note: This case study is depend on PPRM’S internal research.