Condition Monitoring: Definition, Methods & MES Role

What is condition monitoring?

Condition monitoring is the continuous measurement and analysis of machine health indicators — vibration, temperature, pressure, current, acoustics — to detect degradation before it causes unplanned downtime. Unlike time-based maintenance (replace every X hours) or reactive maintenance (fix after failure), condition monitoring triggers action based on the actual state of the equipment. It is the foundation of predictive maintenance and a core capability of any modern Manufacturing Execution System (MES).

How does condition monitoring work in practice?

Sensors capture machine parameters in real time. Edge or cloud systems analyze the data against baselines. Deviations trigger alerts, maintenance orders, or automatic process adjustments — depending on the maturity level of the implementation.

What are the 4 main condition monitoring methods?

In practice, most discrete manufacturers start with vibration analysis (highest detection rate for rotating equipment) and PLC-based alarm correlation (captures downtime causes automatically from the machine controller). At Neoperl, PLC alarm capture — a form of condition monitoring at the controller level — identified that 4 alarm codes correlated to 80 % of all downtime events.

How does condition monitoring compare to other maintenance strategies?

Condition monitoring is the practical middle ground: it delivers 80 % of the value of full predictive maintenance at a fraction of the complexity. Most SYMESTIC implementations start here — capturing machine alarms and process data in real time — before layering on ML-based prediction as data history builds.

What role does an MES play in condition monitoring?

Standalone condition monitoring captures data. An MES makes it actionable. The MES connects sensor data with production context — which order was running, which operator was on shift, which process parameters were active — so that deviations can be traced to root causes, not just flagged as anomalies.

• Alarm correlation: MES links machine alarms to downtime events and quality defects. At Neoperl, this correlation reduced scrap by 15 %.
• Automatic downtime classification: PLC-based alarm capture classifies stops without operator input — the machine explains itself.
• Cross-machine pattern recognition: Across 15,000+ connected machines, the MES identifies which alarm patterns precede which failure modes — building the data foundation for predictive maintenance.
• Maintenance workflow integration: When a threshold is exceeded, the MES can trigger a maintenance order, notify the responsible technician, and reschedule production — automatically.

FAQ

What is the difference between condition monitoring and predictive maintenance?
Condition monitoring measures current machine state and triggers action when thresholds are exceeded. Predictive maintenance uses historical condition data + ML to forecast when a failure will occur. Condition monitoring is the prerequisite — you cannot predict without first monitoring.

Which machines benefit most from condition monitoring?
Any machine where unplanned downtime is expensive: bottleneck machines, high-speed assembly lines, presses with critical bearing loads, CNC spindles. The ROI is highest where downtime cost per hour is highest. In mid-market discrete manufacturing, that typically means € 100–500/hour per machine.

Can condition monitoring work on brownfield machines?
Yes. Modern IoT gateways (IXON, DI-Gateway) capture digital signals from machines without modifying PLC logic. SYMESTIC connects both OPC-UA-capable modern machines and 10–30-year-old brownfield equipment across 15,000+ installations in 18 countries.

Related: Predictive Maintenance · MES: Definition & Functions · OEE Explained · MES Use Cases · OPC UA · Smart Factory