Predictive maintenance in automated plants: what is it and how to implement it?

Every unplanned shutdown in an automated plant has a direct cost—lost production, overtime, emergency parts—and an equally devastating indirect cost: loss of customer confidence and erosion of margins. Predictive maintenance is the technological solution to this problem. It’s not about inspecting machines on a schedule, but rather listening to them continuously and acting only when the data indicates that something is about to fail.

1What is predictive maintenance and how does it differ from preventive maintenance

Predictive maintenance is a strategy based on the continuous monitoring of equipment’s actual condition using sensors, data analysis, and algorithms, with the goal of predicting failures before they occur. Unlike preventive maintenance—which operates on fixed time intervals regardless of the equipment’s condition—predictive maintenance intervenes only when indicators signal that intervention is necessary.

The result is twofold: unexpected downtime is avoided, and unnecessary interventions that consume time and resources without adding real value are eliminated.

Comparison of Maintenance Strategies

2What technologies make predictive maintenance possible

The implementation of a predictive strategy relies on a combination of technologies that work in an integrated manner. It is not about adopting a single tool, but rather building an ecosystem for data capture, transmission, and analysis. The ISA-95 standard from the International Society of Automation establishes the reference frameworks for integrating these systems into plant architecture.

3Which parameters are monitored and why

Each type of equipment has its own characteristic warning signals. Understanding them is the first step in determining which sensors to install and which thresholds to configure. The systems integrated into the industrial facility allow these parameters to be captured continuously without operator intervention.

4How to implement it step by step

The biggest mistake when approaching predictive maintenance is trying to implement it across the entire plant all at once. The key is to start with critical equipment—those whose downtime directly impacts production or safety—and scale up gradually. If your plant is also looking to increase competitiveness without adding staff, predictive maintenance offers one of the fastest returns on investment.

Phase 1 — Inventory and Criticality

Catalog all plant equipment and classify it by criticality: impact on production, repair cost, historical failure rate, and availability of spare parts. This determines where to place the first sensors.

Phase 2 — Instrumentation and Connectivity

Install the appropriate sensors on each priority piece of equipment and ensure connectivity to the analytics system. This is where the industrial facility’s architecture plays a decisive role: a well-designed plant network greatly facilitates this phase.

Phase 3 — Baseline and Reference Models

During the first few weeks, the system collects data under normal operating conditions. This baseline serves as the reference against which future deviations will be detected. Without it, any alert would be arbitrary.

Phase 4 — Alert Configuration and Work Orders

Define alert thresholds for each parameter and integrate the system with the CMMS so that it automatically generates a work order when a threshold is exceeded. Human intervention should be limited to executing the action, not detecting the problem.

Phase 5 — Continuous Review and Model Improvement

Every intervention performed provides learning data. Models must be reviewed periodically to adjust thresholds, incorporate new patterns, and improve the accuracy of predictions.

5 KPIs to measure the program’s success

Without clear metrics, it is impossible to justify the investment or determine whether the program is working. The following indicators should be measured before implementation—to establish a baseline—and continuously once the program is up and running. BMS-type energy monitoring systems complement these KPIs by providing consumption data that also anticipates degradation.

6Common Mistakes and How to Avoid Them

Most predictive maintenance programs that fail to meet their objectives do not fail because of the technology: they fail due to change management and misaligned expectations. Understanding the most common mistakes allows you to avoid them from the program’s design phase. If you also need to assess whether your electrical panels are ready to support the additional instrumentation, this is a starting point worth verifying before installing sensors.