Predictive Maintenance
Predict equipment failures before they happen. AI-powered analytics analyse patterns from maintenance logs, sensor data, and historical records to forecast issues and optimise maintenance schedules.
Predictive Analytics Dashboard
AI-powered failure predictionWhy Predictive?
Predictive vs Reactive Maintenance
Stop reacting to failures. Start predicting them.
Reactive Maintenance
Predictive Maintenance
How It Works
5-Step Predictive Maintenance Workflow
From data collection to automated work orders, our AI handles the entire predictive maintenance cycle.
Collect
Sensor DataAnalyse
AI ProcessingPredict
Failure ForecastAlert
NotificationsExecute
Auto Work OrderAI Analytics
AI-Powered Failure Prediction
Advanced AI analyses patterns from maintenance logs, sensor readings, and historical data to predict equipment failures before they occur. Get early warnings with confidence scores.
Failure Prediction
Predict failures from maintenance logs.
Trend Analysis
Analyse trends across asset types.
Risk Assessment
Early warnings and risk evaluation.
Optimisation Tips
Performance recommendations.
Predictive Analytics
AI analyses patterns to predict maintenance needs and operational insights
Based on maintenance log patterns, Pump P-204 may require bearing replacement within 30 days. · Birmingham
85% ConfidenceCompressor C-102 showing vibration anomalies. Schedule inspection within 14 days. · Leeds
72% ConfidenceHealth Monitoring
Asset Health Scoring
Monitor 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.
Health Score
0-100% asset health rating.
MTBF Tracking
Mean Time Between Failures.
MTTR Analysis
Mean Time To Repair metrics.
Availability Rate
Equipment uptime percentage.
Asset Health Dashboard
94%
Fleet Health97.2%
AvailabilityAuto Generation
Automated Work Order Creation
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.
Auto Priority
Set priority based on risk level.
Smart Assignment
Assign based on skills & MTTR.
Parts Reservation
Auto-reserve required inventory.
Deadline Scheduling
Set due date before predicted failure.
Auto-Generated Work Order
Pump P-204 Bearing Replacement · Manchester
Preventive bearing replacement based on AI prediction. Failure probability: 87% within 12 days.
Required Parts (Auto-Reserved)
Analytics
Trend Analysis Dashboard
Visualise maintenance trends over time. Track failure patterns, identify recurring issues, and optimise maintenance schedules based on historical data and AI insights.
Failure Trends
Track failure patterns over time.
Cost Analysis
Maintenance cost tracking.
Downtime Reports
Analyse downtime causes.
ROI Calculator
Measure predictive maintenance ROI.
Trend Analysis
↓ 45%
Downtime↓ 30%
Costs↑ 25%
MTBFTop Predicted Issues
Complete Integration
Predictive Maintenance Integrates With
Seamlessly connected with all OxMaint modules for complete maintenance management.
Assets
Equipment trackingWork Orders
Auto-generationPM Schedules
Optimised timingInspections
Condition checksTeam
Skill matchingInventory
Parts forecastingDocuments
AI analysisReports
Analytics insightsProven Results
AI-Powered Predictions That Deliver
45%
Reduced Downtime
Predict failures before they cause costly shutdowns.
30%
Cost Savings
Reduce maintenance costs with optimised scheduling.
87%
Prediction Accuracy
Industry-leading AI accuracy with confidence scores.
"AI predicted our compressor failure 3 weeks in advance. Saved us £40,000 in emergency repairs."
Maintenance Director
Oil & Gas · Aberdeen
"Downtime reduced by 45% in the first year. The ROI was visible within 3 months."
Operations Manager
Manufacturing · Birmingham
"Auto work order creation means our technicians are always one step ahead. Game changer."
Plant Manager
Utilities · Leeds
"The trend analysis dashboard gives us insights we never had before."
Reliability Engineer
Transportation · Sheffield
Start Predicting Failures Today.
Let AI predict equipment failures before they happen and automatically create work orders to prevent costly downtime.
OxMaint Predictive Maintenance is available on web, iOS, and Android. UK GDPR compliant.
GDPR Compliant · 87% Prediction Accuracy · 30-min demo
Pump P-204 Bearing
Manchester · 12 days WO-GB-0892 CreatedUpcoming Predictions
94%
Health156d
MTBFAsset Status
FAQ
Frequently Asked Questions
Everything you need to know about OxMaint's Predictive Maintenance.
Predictive Maintenance uses AI and machine learning to analyse 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 optimises 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 prioritise actions accordingly. The AI continuously learns from your equipment data, improving accuracy over time as it gathers more historical patterns.
The AI analyses 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 optimise technician assignments and predict maintenance windows.