OVERVIEW
A paper manufacturing company implemented AI-powered vibration analysis to predict mechanical failures on critical equipment including dryers and cylinders. The solution reduced unplanned downtime by 25% while improving energy efficiency by 8%, transforming maintenance from reactive to predictive across pulp and paper production lines.
INITIAL SITUATION
Paper mills operate continuous production lines where dryers, cylinders, and motors run 24/7 under demanding conditions. The client faced frequent unexpected failures on critical components, resulting in costly emergency repairs and production losses. Traditional time-based maintenance schedules were inefficient, components were either replaced too early (wasting capital) or failed unexpectedly (causing downtime).
The maintenance team lacked real-time visibility into equipment health. When motors or bearings began degrading, subtle warning signs went undetected until catastrophic failure occurred. This reactive approach meant keeping excessive spare parts inventory while still experiencing unplanned stoppages that disrupted production schedules and customer commitments.
THE CHALLENGE
The primary objective was to predict mechanical failures before they occurred, specifically targeting motors, bearings, and rollers on dryers and cylinders, the most critical assets in the paper manufacturing process. The solution needed to distinguish between different failure modes (bearing wear, misalignment, imbalance) and provide maintenance teams with sufficient lead time to plan interventions.
Technical constraints included integrating with existing PLC systems without disrupting operations and achieving high-frequency data collection (10-25 kHz for vibrations) across multiple machines. The artificial intelligence system also needed to learn the specific behavior patterns of each asset, as paper industry equipment varies significantly based on age, configuration, and operating conditions.
THE SOLUTION
Mipu implemented a comprehensive predictive maintenance solution using artificial intelligence and vibration analysis. The project followed a structured approach:
Discovery Phase (2 months): Engineers collected 6 months of historical data including vibrations, motor current, temperature, and RPM readings. They also documented past failures and maintenance logs to train the AI models on real failure patterns specific to the paper industry.
Sensor Installation: High-frequency vibration sensors (sampling at 10-25 kHz) were installed on critical motors and rotating equipment. Current sensors (1-5 kHz) and temperature monitors were integrated with existing PLCs to create a comprehensive monitoring system.
AI Model Development: Machine learning algorithms analyzed vibration spectra using Fast Fourier Transform (FFT) analysis to recognize signatures of specific defects. The data-driven approach learned the unique behavior of each machine rather than relying on generic manufacturer specifications—critical for paper manufacturing equipment that rarely behaves in standard ways.
Human-in-the-Loop Integration: The system was designed with maintenance experts validating AI predictions, creating a feedback loop that continuously improved model accuracy while building trust with the operations team.
RESULTS
The predictive maintenance solution delivered measurable improvements across multiple KPIs:
- 25% reduction in unplanned downtime from early detection of bearing wear, misalignment, and motor degradation
- 8% improvement in energy efficiency as better-maintained equipment operated more smoothly with less friction and resistance
- 40% reduction in emergency repair costs by converting urgent failures into planned maintenance interventions
- Improved spare parts management with data-driven inventory decisions replacing guesswork
The artificial intelligence system successfully distinguished between different failure modes, providing maintenance teams with specific diagnoses rather than generic alerts. This enabled targeted interventions, ordering the correct spare parts and scheduling appropriately skilled technicians. The paper mill gained predictive visibility into equipment health across dryers, cylinders, and motors, transforming maintenance strategy from reactive firefighting to proactive asset management.
