Predictive Maintenance

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.

Predictive Maintenance

Trusted by over 1000+ clients across the world

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Why Predictive?

Predictive vs Reactive Maintenance

Stop reacting to failures. Start predicting them.

Reactive Maintenance

Unexpected equipment failures
Costly emergency repairs
Extended unplanned downtime
Safety risks from sudden failures
Higher overall maintenance costs
Avg Cost per Failure $15,000+

Predictive Maintenance

AI predicts failures in advance
Planned, cost-effective repairs
Minimized scheduled downtime
Enhanced safety compliance
30% reduction in maintenance costs
Avg Cost per Repair $3,500

How It Works

5-Step Predictive Maintenance Workflow

From data collection to automated work orders, our AI handles the entire predictive maintenance cycle.

1
Collect
Sensor Data
2
Analyze
AI Processing
3
Predict
Failure Forecast
4
Alert
Notifications
5
Execute
Auto Work Order

AI Analytics

AI-Powered Failure Prediction

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.

Failure Prediction

Predict failures from maintenance logs.

Trend Analysis

Analyze trends across asset types.

Risk Assessment

Early warnings and risk evaluation.

Optimization Tips

Performance recommendations.

Predictive Analytics

AI analyzes patterns to predict maintenance needs and operational insights

Predictive Alert

Based on maintenance log patterns, Pump P-204 may require bearing replacement within 30 days.

85% Confidence
Upcoming Maintenance

Compressor C-102 showing vibration anomalies. Schedule inspection within 14 days.

72% Confidence

Health 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
Live

94%

Fleet Health

97.2%

Availability
Pump P-101
98%
Compressor C-102
76%
Pump P-204
52%
Bearing replacement needed

Auto 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
AI Created
WO-2025-0892 High Priority
Pump P-204 Bearing Replacement

Preventive bearing replacement based on AI prediction. Failure probability: 87% within 12 days.

Assigned To John Smith
Est. Duration 4 hours
Due Date Dec 30, 2025
Parts Reserved 3 items
Required Parts (Auto-Reserved)
SKF 6205-2RS Bearing In Stock
Mechanical Seal Kit In Stock
Lubricant (2L) In Stock

Analytics

Trend Analysis Dashboard

Visualize maintenance trends over time. Track failure patterns, identify recurring issues, and optimize maintenance schedules based on historical data and AI insights.

Failure Trends

Track failure patterns over time.

Cost Analysis

Maintenance cost tracking.

Downtime Reports

Analyze downtime causes.

ROI Calculator

Measure predictive maintenance ROI.

Trend Analysis
Last 12 Months

↓ 45%

Downtime

↓ 30%

Costs

↑ 25%

MTBF
Jan
Mar
May
Jul
Sep
Nov
Unplanned Downtime Hours (Monthly)
Top Predicted Issues
Bearing Wear
32%
Vibration Anomalies
24%
Temperature Spikes
18%

Complete Integration

Predictive Maintenance Integrates With

Seamlessly connected with all OxMaint modules for complete maintenance management.

Assets
Equipment tracking
Work Orders
Auto-generation
PM Schedules
Optimized timing
Inspections
Condition checks
Team
Skill matching
Inventory
Parts forecasting
Documents
AI analysis
Reports
Analytics insights

Proven Predictive Results

Organizations using OxMaint Predictive Maintenance see measurable improvements in equipment reliability.

target

45%

Reduced Downtime

bar-chart

30%

Cost Savings

employees

87%

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

FAQ

Frequently Asked Questions

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.

Start predicting failures today.

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.

iphone