CASE STUDY
Preventing Unplanned Pump Failures
Multi-Sensor Health Monitoring for Critical Centrifugal Pumps
The Challenge
The plant faced recurring, unplanned pump failures that disrupted production and increased maintenance cost:
Bearing and seal failures occurring without early warning
Cavitation damage going undetected until catastrophic failure
Maintenance based on fixed schedules rather than condition
High spare inventory and emergency repair costs
No correlation between vibration, pressure, flow, and temperature
Each pump failure resulted in 4–6 hours of downtime and cascading process losses.
Why BayaSense
BayaSense was selected as the pump health intelligence platform because it enabled:
Multi-sensor data capture at the edge
High-frequency vibration and process parameter monitoring
Correlation of mechanical and hydraulic behavior
Real-time alerts with historical context
Scalable deployment across pump fleets
Most importantly, BayaSense allowed the maintenance team to detect failure modes early, not just record events.
Business Impact
60% reduction in pump-related downtime
₹42 lakh annual savings from avoided failures and lost production
Improved process stability and safety
Better maintenance planning and resource utilization
Higher confidence in continuous operations
Deployment Scope
Architecture Highlights
RMS vibration & frequency spectrum
Temperature rise trends
Differential pressure & pulsations
Flow deviation from design curve
- Edge devices installed near pump skids
- Continuous data capture and local analytics
- Secure transmission to BayaSense platform
- Role-based dashboards for maintenance and operations
Key Paramaters
Pump failures
Maintenance approach
Mean Time Between Failures
Emergency repairs
Spare inventory
Before BayaSense
6–8 per year
Reactive
Low & unpredictable
Frequent
High
After BayaSense
1–2 per year
Predictive
Increased by 2.5×
Rare
Optimized
Beyond Monitoring: The Reliability Roadmap
With BayaSense in place, the plant is now positioned to:
Expand monitoring to compressors and agitators
Introduce Remaining Useful Life (RUL) models
Track pump efficiency vs Best Efficiency Point (BEP)
Integrate maintenance actions into CMMS
Move toward asset-level digital twins
Client Snapshot
Industry: Chemical Processing
Application: Process fluid transfer using centrifugal pumps
Pump Population: 18 critical pumps
Operating Mode: Continuous (24×7)
Failure Impact: Line stoppage, quality risk, safety exposure
Digital Maturity: Basic SCADA, no predictive maintenance system
Key Takeaway
Pump failures rarely occur suddenly—they are preceded by signals.
BayaSense helped this manufacturer listen to those signals, correlate them across sensors, and act early—transforming pump maintenance from reactive to predictive.