Predictive maintenance case study: 5 real examples with ROI

Unlike reactive maintenance, which fixes equipment after it fails, or preventive maintenance, which follows a fixed schedule regardless of the equipment’s actual condition, predictive maintenance acts on the real condition of each asset. For that data to mean anything, you first need an accurate model of that asset, as shown in the first case study below.

The first of the five cases shows the step that makes predictive maintenance possible in the first place, the digital twin, with a real Foundtech project in Switzerland. The other four come from public case studies in paper manufacturing, steel, wind energy, and rail transport, each with the original problem, the technical solution applied, and the measured result.

1. The step before predictive maintenance: the Werdhölzli digital twin, Switzerland

Before a maintenance team can anticipate failures with sensors and data, it needs an accurate model of the asset to interpret that information against. At the Werdhölzli wastewater treatment plant in Switzerland, Foundtech built that model through 3D laser scanning and BIM modeling of the entire existing infrastructure, a digital twin that became the foundation for the plant’s redesign.

The project cut time and cost in the redesign phase by working from real as-built data instead of outdated drawings. That same model was set up for future use in maintenance and operations, exactly the foundation a predictive maintenance system needs to correctly interpret data from sensors installed on the real asset. See more Foundtech projects.

2. Paper mill: vibration monitoring on water pumps

A paper mill documented by Principia Solutions faced frequent failures in the water pumps of its production line, a problem that interrupted operations and generated unplanned repair costs. The maintenance team implemented a vibration monitoring system able to detect mechanical anomalies before they turned into a complete failure, shifting the approach from reactive to predictive.

The result was a 70% reduction in unplanned failures and a 25% increase in machine availability, along with significant maintenance cost savings during the system’s first year of operation. The full case is documented by Principia Solutions.

3. Steel plant: oil analysis on rolling mill motors

At a steel plant, premature bearing wear in the electric motors of the rolling mills caused unexpected stoppages that affected production on a recurring basis. The solution applied was periodic oil analysis to identify wear patterns and schedule maintenance before the failure occurred, instead of waiting for the equipment to stop on its own.

Bearing lifespan extended by 30%, unplanned stoppages dropped by half, and maintenance resources were optimized by focusing only on components showing real wear. The case is reported by Industria Química.

4. Wind farm: ultrasonic inspection of turbine blades

Deterioration of wind turbine blades from erosion and fatigue is one of the costliest problems for a wind farm’s operation, because it directly affects energy generation. In this case, the solution was an ultrasonic inspection system capable of detecting micro-cracks and fissures in the blades before they compromised the structure.

Blade lifespan increased by 20%, repair costs went down, and wind energy generation became more reliable by cutting unplanned stoppages. Endesa documents the full project.

5. Railway network: track vibration monitoring

Excessive wear on rails and track components is a frequent cause of derailments and train delays, with a direct impact on system safety. The solution documented in this case was an onboard monitoring system that measures track vibration and deformation in real time during normal operation.

Derailment incidents dropped by 60%, ride quality improved, and track maintenance costs went down by focusing interventions where they were actually needed. The full study is available on Scielo.

What these five cases have in common

In the four cases documented with figures, the savings don’t come from the technology alone, they come from catching the failure before it stops the operation. That’s the same principle connecting predictive maintenance to the rest of Foundtech’s services: a digital twin gives the maintenance team an accurate representation of the asset to monitor its real condition against, instead of relying only on fixed inspection schedules.

If your plant still runs on reactive maintenance or a fixed preventive schedule, the first step isn’t installing sensors, it’s having an accurate model of the asset to interpret that data against, the same way Foundtech did in Werdhölzli. Schedule a diagnosis with Foundtech to review which of your assets are the best candidates to start with.

Frequently asked questions about predictive maintenance

What does a predictive maintenance case study typically include?

A solid case study covers four elements: the original problem the plant was facing, the specific technology or method applied, the measurable result, expressed in percentages, cost savings, or extended asset life, and enough detail about the industry and equipment involved for the reader to judge whether it applies to their own operation.

What’s the difference between predictive and preventive maintenance?

Preventive maintenance follows a fixed schedule, a part gets inspected or replaced at set intervals regardless of its actual condition. Predictive maintenance instead uses sensors and condition data, such as vibration, temperature, or oil analysis, to intervene only when the equipment actually needs it. This avoids both unexpected failures and the cost of replacing parts that still work fine.

How do you build a business case for predictive maintenance?

The strongest business case compares the cost of unplanned downtime and reactive repairs against the cost of sensors, monitoring software, and the digital model needed to interpret the data. Start with the assets whose failure is most expensive to the operation, those are the ones where predictive maintenance pays back fastest, and use that as the pilot case before scaling to the rest of the plant.