CASE STUDY
Detecting Bearing Degradation Before Failure
Predictive Health Monitoring for Critical Motors
The Challenge
Motor bearing failures were the leading cause of unplanned downtime at the plant:
Sudden bearing seizure causing immediate line stoppage
Excessive reliance on periodic manual vibration checks
No early warning between inspections
Over-maintenance of healthy motors
High emergency repair and production loss costs
A single bearing failure could halt a production line for 2–4 hours, with ripple effects across downstream operations.
Why BayaSense
BayaSense was chosen as the predictive maintenance intelligence layer because it provided:
Continuous vibration and temperature monitoring
Edge-based analytics for early fault detection
Correlation of mechanical and thermal behavior
Scalable deployment across a large motor population
Simple dashboards consumable by maintenance teams
Most importantly, BayaSense detected degradation trends, not just threshold violations.
Business Impact
70% reduction in bearing-related downtime
₹36 lakh annual savings from avoided breakdowns
Reduced spare inventory holding
Improved maintenance manpower utilization
Higher line availability and throughput
Deployment Scope
Architecture Highlights
- RMS vibration levels
- Bearing defect frequencies (BPFO, BPFI, BSF, FTF)
- Temperature rise and thermal gradients
- Load-dependent vibration patterns
- Compact edge sensors mounted on motors
- Continuous data capture with local preprocessing
- Secure data flow to BayaSense platform
- Centralized dashboards by line and motor criticality
Key Paramaters
Bearing-related failures
Detection method
Maintenance strategy
Emergency breakdowns
MTBF
Before BayaSense
Frequent
Periodic manual checks
Reactive / time-based
High
Low
After BayaSense
Rare
Continuous monitoring
Condition-based
Minimal
Improved by 2–3×
Beyond Bearings: The Motor Health Roadmap
With BayaSense in place, the plant is now positioned to:
Monitor electrical parameters (current, imbalance)
Detect misalignment and looseness
Extend monitoring to gearboxes and fans
Integrate alerts with CMMS workflows
Implement fleet-level motor health benchmarking
Client Snapshot
Industry: Automotive components manufacturing
Application: Critical motors driving machining centers and conveyors
Motor Population: 40+ motors (5.5 kW to 55 kW)
Operating Mode: Continuous and intermittent duty
Failure Impact: Line stoppage, missed deliveries, quality risk
Digital Maturity: Basic condition checks, no online monitoring
Key Takeaway
Bearings don’t fail suddenly—they deteriorate predictably.
BayaSense made that deterioration visible, actionable, and manageable—turning motor maintenance into a predictive, data-driven discipline.