From reactive to predictive: the shift that changes everything
Most industrial plants still operate with a reactive maintenance model: intervene when the machine fails. Some have moved to preventive maintenance: scheduled interventions every X operating hours, regardless of the equipment's actual condition. Predictive maintenance takes the next step: intervene at exactly the right moment, when data indicates that failure is imminent — not before (wasted resources) or after (unplanned stoppage).
Until recently, predictive maintenance required expensive measurement equipment, specialist software and vibration analysis experts. IIoT has democratised this capability: low-cost wireless sensors, accessible cloud platforms and increasingly automated analysis algorithms put predictive maintenance within reach of medium and small plants.
What to monitor and why
Vibration
Vibration is the richest source of information about the mechanical condition of rotating equipment. An accelerometer on the bearing of a motor, pump or compressor captures the vibration signature. Frequency domain analysis (FFT) allows precise identification of anomaly sources: imbalance (at 1× rotation frequency), misalignment (2×, 3×), bearing faults (BPFO, BPFI, BSF, FTF frequencies), mechanical looseness (multiple harmonics) and pump cavitation (high-frequency components).
Temperature
Temperature increase in bearings, motor windings or electrical cabinets is an early indicator of deterioration. A 10°C increase in a motor's operating temperature can halve its service life.
Motor Current Signature Analysis (MCSA)
Analysing the motor current signature allows detection of mechanical and electrical faults without installing sensors on the machine itself — only a current transformer on the supply cable is needed. Bearing faults, broken rotor bars and misalignment issues generate characteristic components in the current spectrum.
Ultrasound
Ultrasound sensors detect high-frequency acoustic emission from gas leaks, cavitation, electrical arcing and early-stage bearing friction — weeks or months before they become detectable by conventional vibration analysis.
Analysis algorithms: from thresholds to AI
- Static thresholds: alarm when vibration or temperature exceeds a fixed limit. Simple but with high false alarm rates.
- Trend analysis: tracking value evolution over time. A sustained upward trend in bearing vibration is more meaningful than a single peak.
- Statistical anomaly detection: comparing current behaviour against the equipment's statistical baseline (mean, standard deviation, percentiles).
- Machine Learning: models trained on historical fault data to predict Remaining Useful Life (RUL). Requires sufficient historical fault data for training.
Real ROI of predictive maintenance
- Unplanned downtime reduction: 30% to 70%, depending on the starting point.
- Maintenance cost reduction: 10% to 30%, by eliminating unnecessary preventive interventions and reducing urgent corrective ones.
- Equipment life extension: 20-30% on average, by detecting and correcting problems before they cause secondary damage.
- Spare parts inventory reduction: better planning of spare parts management with real visibility of equipment condition.
Typical ROI for a well-implemented predictive maintenance project: 12 to 24 months.
Where to start
The practical recommendation is to start with critical equipment — those whose stoppage has the greatest production impact — and initially install vibration and temperature sensors. With 3-6 months of data, it is already possible to establish baselines and detect significant deviations.
At Bluemation we integrate condition monitoring systems with IIoT platforms: from sensor installation and gateway configuration to dashboard visualisation and alert setup. Contact our team to evaluate which equipment in your plant would benefit most from predictive maintenance.