Predict equipment failures before they happen. AI-powered analytics analyze patterns from maintenance logs, sensor data, and historical records to forecast issues and optimize maintenance schedules.
Stop reacting to failures. Start predicting them.
From data collection to automated work orders, our AI handles the entire predictive maintenance cycle.
Advanced AI analyzes patterns from maintenance logs, sensor readings, and historical data to predict equipment failures before they occur. Get early warnings with confidence scores.
Predict failures from maintenance logs.
Analyze trends across asset types.
Early warnings and risk evaluation.
Performance recommendations.
AI analyzes patterns to predict maintenance needs and operational insights
Based on maintenance log patterns, Pump P-204 may require bearing replacement within 30 days.
85% ConfidenceCompressor C-102 showing vibration anomalies. Schedule inspection within 14 days.
72% ConfidenceMonitor the health of every asset in real-time. Track MTBF, MTTR, availability, and reliability metrics. Get instant visibility into equipment condition across your entire operation.
0-100% asset health rating.
Mean Time Between Failures.
Mean Time To Repair metrics.
Equipment uptime percentage.
When AI predicts a failure, automatically generate work orders with the right priority, assigned technician, required parts, and estimated completion time. No manual intervention needed.
Set priority based on risk level.
Assign based on skills & MTTR.
Auto-reserve required inventory.
Set due date before predicted failure.
Preventive bearing replacement based on AI prediction. Failure probability: 87% within 12 days.
Visualize maintenance trends over time. Track failure patterns, identify recurring issues, and optimize maintenance schedules based on historical data and AI insights.
Track failure patterns over time.
Maintenance cost tracking.
Analyze downtime causes.
Measure predictive maintenance ROI.
Seamlessly connected with all OxMaint modules for complete maintenance management.
Organizations using OxMaint Predictive Maintenance see measurable improvements in equipment reliability.
Reduced Downtime
Cost Savings
Prediction Accuracy
"AI predicted our compressor failure 3 weeks in advance. Saved us $50,000 in emergency repairs."
PetroChem Refinery
Oil & Gas
"Downtime reduced by 45% in the first year. The ROI was visible within 3 months."
Manufacturing Plus
Manufacturing
"Auto work order creation means our technicians are always one step ahead. Game changer."
PowerGen Utilities
Utilities
"The trend analysis dashboard gives us insights we never had before."
Global Logistics
Transportation
Everything you need to know about OxMaint's Predictive Maintenance.
Predictive Maintenance uses AI and machine learning to analyze equipment data, maintenance logs, and sensor readings to predict when equipment is likely to fail. Unlike reactive maintenance (fixing after failure) or preventive maintenance (fixed schedules), predictive maintenance optimizes timing based on actual equipment condition, reducing unnecessary maintenance while preventing unexpected failures.
OxMaint's predictive maintenance achieves an average accuracy of 87% for failure predictions. Each prediction includes a confidence score (e.g., 85% confidence) so you can prioritize actions accordingly. The AI continuously learns from your equipment data, improving accuracy over time as it gathers more historical patterns.
The AI analyzes multiple data sources including: maintenance logs and work order history, inspection records and checklists, sensor data (vibration, temperature, pressure), runtime hours and cycle counts, historical failure patterns, and document intelligence from technical manuals and reports. The more data available, the more accurate the predictions become.
When the AI predicts a failure with high confidence, it can automatically generate a work order with: appropriate priority based on risk level, assigned technician based on skills and MTTR, required parts automatically reserved from inventory, due date set before the predicted failure date, and estimated duration based on historical repair times. You can configure thresholds for when auto-generation occurs.
MTBF (Mean Time Between Failures) measures the average time between equipment failures, indicating reliability. Higher MTBF means more reliable equipment. MTTR (Mean Time To Repair) measures the average time to repair equipment after a failure, indicating maintenance efficiency. Lower MTTR means faster repairs. OxMaint tracks both metrics automatically and uses them to optimize technician assignments and predict maintenance windows.
Stop reacting to equipment failures. Let AI predict problems before they happen and automatically create work orders to prevent downtime.
OxMaint Predictive Maintenance is available on web, iOS, and Android.
