Predictive Maintenance

Predictive Maintenance connects condition monitoring data, machine learning insights, and maintenance execution to prevent unplanned downtime.

What it solves

• Detects abnormal behavior before failure occurs.
• Prioritizes interventions based on risk and asset criticality.
• Bridges OT/IT data to maintenance workflows.

Core capabilities

• Data connectors for vibration, temperature, energy, or PLC data.
• Threshold rules and anomaly detection models.
• Health scores and risk heatmaps by asset or line.
• Automated ticket creation or plan adjustments when thresholds are breached.
• Feedback loop to refine models using work order outcomes.

Implementation playbook

1. Select pilot assets. Focus on high-impact machines with existing sensors.
2. Connect data sources. Stream readings into OpexMx via API, OPC UA, or CSV uploads.
3. Define rules. Start with simple thresholds, then layer ML models for pattern detection.
4. Operationalize alerts. Route validated alerts to the Ticketing System with recommended actions.
5. Close the loop. Capture technician feedback to improve model accuracy and reduce false positives.

Data captured

• Time-series sensor readings and derived features.
• Alerts, severity levels, and response actions.
• Failure outcomes to train predictive models.

Best practices

• Involve reliability engineers to interpret alerts before automating responses.
• Combine with Reporting & Analytics to quantify avoided downtime.
• Periodically review thresholds to reflect changing operating conditions.

KPIs to watch

• Number of avoided breakdowns attributed to predictive alerts.
• False-positive rate vs. tolerance.
• Lead time between alert and planned intervention.