Nanoprecise Helps Automotive Plant Prevent Blower Failures With Blower Vibration Analysis
Summary
Nanoprecise partnered with an automotive manufacturer in India to monitor and maintain the health of 21 industrial blowers used in paint handling through vibration and acoustic analysis.
Using real-time vibration analysis and acoustic emission sensors, the system detected 8 critical faults (two of which occurred on the same blower). These early detections enabled timely maintenance interventions, preventing over $9,600 in production losses, achieving daily energy savings of $537, and reducing CO₂ emissions by 2.1 metric tons per day.
The result: extended asset life, reduced downtime, and a data-driven maintenance strategy.
Customer Profile
A leading passenger vehicle manufacturer in India (serving both domestic and international markets). relies on precise airflow during the painting process to ensure high-quality finishes across doors, panels, and full body frames.
The plant’s paint handling system includes 21 industrial blowers, varying in motor power from 22 kW to 90 kW. Despite a weekly cleaning routine, the manufacturer faced challenges maintaining consistent performance and preventing unexpected blower degradation caused by paint particle accumulation and mechanical imbalance.
The Challenge
The blowers were aging under varied loads and operating conditions. Paint buildup on impellers contributed to imbalance and vibration faults, leading to potential bearing failures, inefficient energy use, and the threat of unplanned downtime.
Without a continuous monitoring system, or proper blower vibration analysis, issues often went unnoticed until they escalated into performance loss or mechanical breakdowns, resulting in production delays and excessive energy consumption.
Solution Deployed
To improve reliability and energy efficiency, the plant deployed Nanoprecise’s Energy-Centric Predictive Maintenance solution across all 21 blowers.
This solution included:
- Wireless sensors with 6-parameter machine condition monitoring (including blower vibration analysis and energy consumption monitoring)
- Real-time vibration and FFT analytics
- Remaining Useful Life (RUL) prediction
- Automated fault detection using vibration and acoustic data
- Cloud-based dashboard for maintenance visibility
- Acoustic emission sensor support for early-stage degradation monitoring
Over four months, eight faults were detected across seven blowers—most notably on the CF-208 blower, which experienced two separate critical fault events. CF-208 became a standout example of how Nanoprecise technology can move maintenance from reactive to proactive.
Observation & Analysis
First Detection on the CF-208 Blower
- RMS Velocity values were in the critical zone at blower bearings.
- Fast Fourier Transform (FFT) Spectrum revealed a dominant frequency of 15.55 Hz, indicating unbalance at Blower Drive End (DE) and Non Drive End (NDE).
- Stage-4 severity notification for unbalance generated by system and sent to maintenance team.
- Corrective Action taken: Maintenance team cleaned the impeller. Afterwards, the vibration levels reduced from:
- 40.1 mm/s → 7.8 mm/s (DE)
- 34.7 mm/s → 9.7 mm/s (NDE)
Second Detection on the CF-208 Blower (4 months later)
- Abnormal vibrations returned with rising RMS Velocity & Acceleration values.
- FFT Spectrum indicated 12.61 Hz frequency (with blower running at 1x speed) and its harmonics are indicated at Blower DE & NDE bearings, which suggested potential base looseness/unbalance in the fan impeller.
- Envelope FFT Spectrum also revealed running speed harmonics, suggesting bearing looseness.
- Post-cleaning, while vibrations reduced, they were still in the ‘alert zone’. Further inspection revealed impeller damage.
- Corrective Action Taken: Impeller replaced; vibration returned to normal levels.
- Want to see the dashboard? View our video walkthrough.
Outcomes & Impact
Avoided Unplanned Downtime and Costs: Early fault detection helped prevent $9,600 in lost production, with each critical fault potentially costing $1,200/hour.
Energy Optimization: By correcting imbalance and degradation, Nanoprecise reduced energy waste by $537/day or $196,005/ year across monitored blowers.
CO₂ Reduction: Estimated 2.1 metric tons/day in carbon emissions avoided through energy-efficient operation.
Asset Longevity: Early fault detection helped avoid secondary damage to shafts, housings, and bearings, extending remaining useful life of blower and other equipment.
Conclusion
Nanoprecise’s predictive maintenance platform provided this automotive leader with the insights needed to transition from routine cleaning schedules to data-driven, condition-based maintenance.
By enabling early detection of blower failures, mechanical unbalance, and energy inefficiencies, Nanoprecise helped the plant optimize energy use, reduce CO₂ emissions, and avoid thousands in downtime losses.
In fast-paced automotive production lines, even minor mechanical faults can ripple into major delays. Nanoprecise empowers maintenance teams to predict with precision, take targeted corrective action, and maintain high equipment reliability in mission-critical systems. Want to see it work for you? Get your demo!