Most manufacturers run some version of preventive maintenance: change the oil every 500 hours, replace the belts on schedule, grease the bearings every month. It's better than running to failure. But it has a fundamental inefficiency built in — you're replacing components based on a calendar, not on the actual condition of the equipment.
Predictive maintenance is the alternative: use real data from the equipment itself to determine when maintenance is actually needed. The result is fewer unnecessary replacements, earlier warning of genuine failures, and maintenance work planned in advance rather than in response to a breakdown.
Your control system already collects much of the data needed to do this. The question is whether you're using it.
The Maintenance Spectrum
Maintenance approaches exist on a spectrum from reactive to proactive:
Reactive (Run-to-Failure): No maintenance until breakdown. Lowest upfront cost, highest downtime cost. Appropriate only for non-critical, easily replaced equipment.
Preventive (Time-Based): Scheduled maintenance at fixed intervals regardless of condition. Better than reactive but wastes resources when components are replaced before they need it — and still misses failures that develop between maintenance windows.
Condition-Based Maintenance: Maintenance triggered by condition indicators — temperature, vibration, amperage draw, cycle counts — reaching defined thresholds. Reduces unnecessary maintenance while catching developing problems.
Predictive Maintenance: Uses historical data and trend analysis to predict when a failure is likely to occur, enabling planned maintenance at the optimal time. Most effective when data history is sufficient to establish failure patterns.
Most manufacturing plants use a mix of all four approaches. The goal isn't to eliminate preventive maintenance — it's to apply each approach where it makes sense, and to build the data capability to support condition-based and predictive approaches for your highest-value equipment.
What Your Control System Already Knows
Modern PLCs and SCADA systems are constantly collecting data that's useful for maintenance — but that data often goes unused beyond its immediate control function. Examples:
- Motor current draw: A motor drawing significantly more current than normal is often a sign of mechanical load increase — bearing wear, contamination in a conveyor, pump cavitation. Trend the current over time and you can catch the deviation before it becomes a fault.
- Cycle time deviation: If a press cycle that normally takes 4.2 seconds is now taking 4.8 seconds, something has changed. That might be normal process variation — or it might be a worn component, increased friction, or a flow restriction developing.
- Fault frequency: How often is a specific drive faulting? How often is a sensor giving a bad reading? Increasing fault frequency on a specific device is an early indicator of component degradation.
- Temperature trends: Thermal overloads on drives and motors tell you when heat is building. An enclosure running warmer than normal may indicate a failed cooling fan before the drive itself faults.
- Valve and actuator position confirmation: If a valve is being commanded to open and the confirmation feedback is taking longer than normal to arrive, the actuator may be developing mechanical wear.
None of this requires exotic sensors or expensive condition monitoring hardware. It requires logging the data your PLC already has, establishing what normal looks like, and setting alerts when values deviate.
Building a Condition Monitoring Program
Step 1: Identify High-Value Assets
Start with the equipment where unplanned failure has the largest impact: highest cost of downtime, longest lead time for replacement parts, or greatest safety consequence. Condition monitoring for a conveyor that feeds a $10,000/hour production line is worth more than the same monitoring applied to a standalone utility pump.
Step 2: Define Normal
For each monitored parameter, establish a baseline. What does current draw look like on this motor under normal load? What's the normal range? Capture this data during a period when the equipment is running well — ideally at multiple operating points if the process has different modes.
Without a baseline, you have data but no context. A motor drawing 18A is fine or alarming depending entirely on what the normal value is.
Step 3: Set Alert Thresholds
Alert thresholds should be set based on the baseline and known failure patterns for that equipment type. A motor drawing 15% above baseline for sustained periods is worth an alert. An instantaneous spike during startup is not.
Most SCADA and historian platforms allow time-averaged or filtered alarm conditions — alerts that trigger on sustained deviation rather than transient excursions. Use them.
Step 4: Build the Maintenance Response
A condition alert is only useful if it triggers action. Define what happens when a threshold is crossed: who gets notified, what investigation is required, and what the maintenance response looks like. Without a process, alerts become noise.
Step 5: Close the Loop
When maintenance is performed, record what was found and what was done. Over time, this history tells you how accurately your leading indicators predicted the actual failure — and lets you tune your thresholds for better performance.
Remote Monitoring's Role
Condition-based maintenance becomes significantly more powerful when combined with remote monitoring. Instead of reviewing trends during a weekly walk-through, your control system can push alerts to your maintenance team the moment a threshold is crossed — day, night, weekend, or holiday.
This is the layer our remote monitoring systems are built to handle — pushing condition-based alerts to your maintenance team in real time instead of waiting for the next scheduled walk-through.
For multi-site operations, centralized remote monitoring means that a maintenance engineer can watch condition trends across all facilities from a single dashboard. Developing problems can be identified and addressed at a time of your choosing rather than in response to a failure call.
The Bottom Line
Predictive and condition-based maintenance doesn't require a new sensor network or a six-figure software platform. It starts with the data your control system already has, a systematic approach to defining normal and detecting deviation, and a maintenance process that responds to leading indicators rather than lagging ones.
The companies getting the most value from their automation investment aren't just using it to control production. They're using it to protect the assets that make production possible.
Logic Control Systems designs remote monitoring systems that integrate condition-based maintenance alerting with your existing SCADA and PLC infrastructure. Contact us at 817-757-9507 or [email protected] to discuss what your control system can tell you about your equipment health.