Prevent equipment failures, minimize downtime, and optimize maintenance costs with IoT-enabled predictive maintenance powered by machine learning.
Unplanned equipment failures cause production stops, missed deliveries, and revenue loss. Average cost of downtime in manufacturing: €260,000 per hour.
Time-based preventive maintenance replaces parts too early, wasting money. Reactive maintenance leads to emergency repairs at premium costs.
Equipment failures can endanger workers, cause accidents, and result in regulatory violations, fines, and reputational damage.
Running equipment to failure accelerates wear, causes secondary damage, and shortens overall equipment life, requiring premature capital replacement.
Our machine learning platform analyzes sensor data from industrial equipment in real-time to predict failures weeks in advance, enabling proactive maintenance and minimizing downtime.
We deploy IoT sensors to monitor critical equipment parameters: vibration, temperature, pressure, acoustics, power consumption, and oil quality. Our edge computing platform processes this data in real-time, detecting anomalies and degradation patterns that signal impending failures.
Machine learning models trained on historical failure data predict the probability and time-to-failure for each asset. Models continuously learn from new data, improving accuracy over time and adapting to changing operating conditions.
Predictive insights feed into maintenance scheduling systems that optimize work orders, spare parts inventory, and technician allocation. This ensures maintenance happens at the right time - before failures occur but not unnecessarily early.
Ready to eliminate unplanned downtime? Our predictive maintenance platform reduces maintenance costs by 25% while improving equipment uptime.
Industrial maintenance has evolved through four generations: (1) Reactive maintenance - fix it when it breaks, resulting in high downtime and emergency costs. (2) Preventive maintenance - time-based schedules that replace parts before expected failure, reducing downtime but wasting parts with remaining useful life. (3) Condition-based maintenance - monitor equipment health and trigger maintenance when thresholds are exceeded. (4) Predictive maintenance - use ML to predict failures weeks in advance, optimizing both reliability and costs.
Research shows predictive maintenance reduces maintenance costs by 25-30%, eliminates breakdowns by 70-75%, reduces downtime by 35-45%, and increases equipment life by 20-40%. For a manufacturing facility spending €5M annually on maintenance, this translates to €1.25-1.5M in savings plus substantial gains from improved production uptime.
Accelerometers mounted on rotating equipment (motors, pumps, compressors, turbines) detect vibration patterns that indicate bearing wear, misalignment, imbalance, or looseness. FFT analysis converts time-domain vibration signals to frequency domain, revealing specific failure modes. ML models learn normal vibration signatures and flag deviations. Most common and effective sensor type for rotating machinery.
Temperature sensors and thermal imaging detect hotspots, overheating, and thermal inefficiency in electrical systems, motors, and process equipment. Rising temperatures often precede failures by weeks. IR cameras enable non-contact scanning of large equipment installations. Thermal monitoring is especially critical for electrical equipment where overheating causes 30-40% of failures.
Ultrasonic sensors detect high-frequency sounds produced by compressed air leaks, steam leaks, electrical arcing, and bearing defects. Acoustic emission monitoring on pressure vessels and tanks identifies crack growth and material degradation. These sensors detect problems invisible to other methods, especially in early stages when intervention is easiest.
Oil condition sensors monitor lubrication systems for contamination, viscosity changes, and metal particles. Wear debris indicates internal component damage. Automated oil analysis provides early warning of gear, bearing, and hydraulic system failures. Critical for hydraulic systems, gearboxes, and engines where oil degradation precedes mechanical failure.
Current and voltage sensors monitor electrical equipment and motor-driven systems. Motor current signature analysis (MCSA) detects rotor bar defects, air gap issues, and load problems. Power quality monitoring identifies supply issues affecting equipment. Non-invasive electrical monitoring provides rich data about mechanical condition through electrical signals.
Unsupervised learning algorithms (Isolation Forest, One-Class SVM, Autoencoders) learn normal equipment behavior and flag deviations. Crucial for rare failure modes where you don't have many failure examples. We train on normal operating data, then detect when sensor readings deviate from expected patterns. Autoencoders compress sensor data to low-dimensional representation and flag reconstruction errors as anomalies.
Supervised models (Random Forest, XGBoost, Neural Networks) trained on labeled failure events predict probability of failure in next N days. Features include sensor statistics (mean, variance, trend), frequency domain features from vibration, and temporal patterns. Requires historical failure data but provides actionable predictions. We handle class imbalance (far more normal operation than failures) using SMOTE, class weights, or focal loss.
Survival analysis and regression models estimate how many days/hours until failure. LSTM networks excel at RUL prediction from sensor time series. This enables optimal maintenance timing - schedule work when RUL drops below safety threshold but maximize part utilization. NASA's turbofan degradation dataset is a common benchmark. Real-world RUL models require run-to-failure data from similar assets.
Beyond predicting "will it fail", classify specific failure modes (bearing failure, imbalance, misalignment, lubrication issue, electrical fault). This guides technicians to the right repair. CNNs can classify failure types from vibration spectrograms. Multi-class models require labeled data for each failure type but provide higher value by enabling precise interventions.
A European automotive parts manufacturer with 250 CNC machines and assembly lines was experiencing 12-15 unplanned downtime events monthly, each costing €40-120K in lost production. Their time-based preventive maintenance program replaced parts every 2,000 operating hours regardless of condition, resulting in €4.2M annual maintenance spend.
We deployed vibration sensors on 85 critical machines, temperature sensors on 60 motors, and current sensors on all major equipment. An edge computing platform analyzes sensor data in real-time using anomaly detection models (Isolation Forest) and failure prediction models (XGBoost) trained on 3 years of maintenance history. Alerts integrate with their CMMS for automated work order generation.
Results after 12 months: Unplanned downtime reduced from 12-15 to 7 events per month (42% reduction), adding 680 hours of production time worth €2.6M. Maintenance costs decreased 28% (€1.2M) through optimized part replacement timing. Total annual benefit: €3.8M. Predictive accuracy: 78% of predicted failures occurred within the forecasted 14-day window, 92% within 30 days.
Ideal scenario: 2+ years of maintenance records with 20+ failure events per asset type for supervised learning. However, we can start with less: anomaly detection works with just normal operation data (no failures required), transfer learning leverages models trained on similar equipment from other sites, and physics-based models can be calibrated with limited data. We recommend starting even with 6-12 months of data - the system improves continuously as it observes failures.
Typical implementation: 3-6 months for pilot (sensors on 10-20 critical assets, initial models, integration), then 6-12 months for full deployment. ROI usually appears within 6-9 months of pilot start as you prevent first major failures. Payback period: 12-24 months depending on equipment criticality and current downtime costs. Long-term benefits compound as models improve with more data. For high-value assets (where single failure costs €100K+), ROI can be achieved after preventing just one failure.
Yes, but each equipment type requires specific sensors and models. Rotating machinery (motors, pumps, compressors) uses vibration and temperature - well-established approach with high accuracy. Electrical systems use thermal imaging and current analysis. Process equipment (heat exchangers, reactors) monitors pressure, temperature, flow. We typically start with one equipment class (e.g., all motors), prove value, then expand to others. Some equipment is more amenable to PdM than others - assets with gradual degradation patterns work better than those with random sudden failures.
False positives are the biggest challenge in predictive maintenance adoption. Our approach: (1) Start with conservative thresholds - prefer missing some failures to excessive false alarms, (2) Multi-level alerts: "Monitor" (investigate), "Caution" (plan maintenance), "Critical" (immediate action), (3) Combine multiple signals - only alert when vibration AND temperature AND trend analysis all agree, (4) Continuous threshold tuning based on maintenance team feedback, (5) Track alert precision (what % of alerts lead to actual issues) and optimize. Typical mature systems: 70-80% precision, meaning 7-8 out of 10 alerts are valid.
Two types of errors: false positives (predict failure that doesn't happen) and false negatives (miss a real failure). False positives waste maintenance resources but don't cause downtime - we minimize these through tuning. False negatives are more costly but still better than reactive maintenance - even 70% detection rate prevents 7 out of 10 failures. When models miss failures, we perform root cause analysis: was sensor data missing? Was it a novel failure mode? We add these cases to training data and retrain. The key is viewing PdM as decision support for maintenance teams, not autopilot - human oversight remains critical.
Eliminate unplanned downtime and optimize maintenance costs with IoT-enabled predictive maintenance. Our team will assess your equipment, identify monitoring opportunities, and design a custom solution.
Based in Lund, Sweden • Serving businesses globally