Industrial maintenance has evolved more in the last ten years than in the previous five decades. For generations, the norm was to wait for something to break to repair it (corrective maintenance) or to set periodic inspections based on time or operating hours (preventive maintenance). Today, technology makes it possible to go one step further: act before a failure occurs, based on real data about the equipment’s condition. That is predictive maintenance.

In this article, we compare both approaches, analyze their advantages and disadvantages, and explain how IIoT is making predictive maintenance an accessible option for any industrial plant.

Preventive maintenance: the familiar approach

Preventive maintenance is based on fixed intervals: every X operating hours, every X weeks, or every X production cycles, a series of inspections, replacements, or adjustments are carried out regardless of the equipment’s actual condition.

Its advantages are clear: it is predictable, easy to plan, and reduces unplanned downtime compared to purely corrective maintenance. Its drawbacks are also clear: interventions are performed on equipment that is still in perfect condition (unnecessary cost), and deterioration that develops faster than expected between two scheduled inspections can be missed.

Predictive maintenance: acting on data

Predictive maintenance continuously monitors the actual condition of equipment using sensors (vibration, temperature, current, pressure, ultrasound) and analyzes that data to detect patterns that indicate degradation or imminent failure.

Intervention is scheduled exactly when it is needed—neither before nor after. The result is a significant reduction in both unnecessary maintenance costs and unplanned downtime.

Common indicators in predictive maintenance

• Vibration: increased vibration in motors or bearings is one of the most reliable indicators of wear or misalignment.
• Temperature: a rise in temperature in an electric motor may indicate overload, insufficient lubrication, or imminent insulation failure.
• Electrical current: variations in a motor’s current profile can anticipate mechanical or electrical issues.
• Pressure: in hydraulic or pneumatic systems, pressure deviations indicate leaks or component deterioration.
• Oil analysis: in lubricated systems, the presence of metal particles in the oil indicates wear of internal components.

Direct comparison

Implementation cost

Preventive maintenance is cheaper to implement initially (it only requires planning and scheduling). Predictive maintenance requires instrumentation (sensors), connectivity, and an analytics platform, but costs have dropped dramatically with the maturation of IIoT and no-code platforms.

Operating cost

Preventive maintenance has predictable fixed costs but includes unnecessary interventions. Predictive maintenance has variable costs, but interventions are only performed when needed, with a generally lower total cost.

Effectiveness

Preventive maintenance does not detect failures that develop between inspections. Predictive maintenance monitors continuously and can detect subtle changes weeks before a failure occurs.

Complexity

Preventive maintenance is simple to manage. Predictive maintenance requires data, analysis, and interpretation, although modern IIoT platforms have greatly simplified this process.

When is each one advisable?

There is no single answer. In practice, most industrial plants use a combination of both approaches:

• Preventive maintenance for equipment with low replacement cost, low criticality, or that is difficult to instrument.
• Predictive maintenance for critical equipment, with high replacement cost, or where downtime has a major impact on production.

The general rule is clear: the higher the cost of unplanned downtime, the greater the return on investing in predictive maintenance.

IIoT as an enabler of predictive maintenance

Predictive maintenance is not new as a concept, but for a long time it was accessible only to large companies with the resources to implement complex SCADA systems and specialized engineering teams. IIoT has democratized access to this capability.

With a platform like coppioT, it is possible to connect existing sensors (or add new low-cost sensors), send the data to the cloud, set up alerts when a parameter exceeds a threshold, and visualize the historical evolution of any variable—without needing to program or hire a cloud architect.

Do you want to get started with predictive maintenance in your plant? coppioT makes it easy. Request a demo and we will show you how.