Blog Post ·
Unplanned downtime is one of the most costly risks in industrial operations. Especially where continuous processes, safety-critical systems, and strict delivery commitments intersect, outages are more than just a technical problem—they quickly become a strategic risk.
Predictive maintenance has been considered a solution for years. But there’s often a gap between theory and practice.
Many decision-makers ask themselves:
“Does this really work during ongoing operations? In existing plants? Under real-world industrial conditions?"
A recent case study from CHEMPARK provides reliable answers to these questions and shows how data-driven maintenance not only improves processes but also changes the way people think about operations.
In many industrial facilities, maintenance still follows fixed intervals. Pumps, motors, and compressors are inspected—regardless of whether they show any signs of trouble. This approach conveys a sense of security but creates a dangerous situation of flying blind.
Without continuous condition data, early signs of wear remain hidden. Damage is often not detected until anomalies have already reached a critical stage. The consequences are:
It was precisely against this backdrop that CHEMPARK reframed the question: "What if maintenance were based not on assumptions, but on real-time data?"
The following case study documents a real-world project for condition monitoring of high-performance pumps in a chemical industrial setting. It illustrates not only the technical implementation but, above all, the operational benefits of predictive maintenance.
Chemical Industrial Park (critical infrastructure)
Reactive, Interval-Based Maintenance
Reducing Unplanned Downtime
Retrofit During Operation
Sensors (vibration, temperature), LoRaWAN®, IoT platform
Live data within a few weeks
High (Pumps, Rotating Assets, Brownfield)
In the plant section under review, those in charge operated a large number of rotating machines. Despite regular inspections, unplanned outages occurred time and again. The condition of the pumps was known—but only on a case-by-case basis.
Looking back, one person involved summarizes the situation as follows:
“We knew outages were coming—but not when. That made planning nearly impossible.”
The decisive step was not to further optimize maintenance, but to change the approach. Instead of fixed intervals, condition data was continuously collected. Vibrations and temperatures provided early indications of deviations from normal operation.
The goal here was not maximum data depth, but the ability to act early. Anomalies were to be detected before they escalated into damage.
A key success factor was the decision to pursue a retrofit scenario. Existing pumps were retrofitted with industrial-grade sensors—without disrupting ongoing operations.
Data was transmitted wirelessly via LoRaWAN®. A central IoT platform aggregated the information, visualized trends, and automated notifications when thresholds were exceeded.
The result: Live data was available within a few weeks. Operations did not need to be restructured, but simply reimagined.
The solution’s impact was clearly measurable:
These figures are not theoretical targets, but were achieved in real-world operations.
Murat Mutlu, Solution Portfolio Manager, Conneqtive
Several overarching insights can be drawn from the case study:
1. Early detection is more cost-effective than perfection
The goal is not to analyze every detail, but to identify relevant deviations early on. Time is saved not by collecting more data, but by making better decisions.
2. Retrofitting is the realistic starting point
Predictive maintenance is not exclusive to new facilities. Existing plants can also be made data-capable with manageable effort.
3. Platforms Accelerate Time to Value
Using an existing IoT platform drastically shortens implementation times and reduces project risks.
4. Condition-Based Maintenance Changes Processes—and Mindsets
The greatest impact is not technical, but organizational: Maintenance shifts from reacting to planning.
Although the project focuses on high-performance pumps, the underlying approach is broadly transferable:
What matters is not the specific use case, but the combination of sensor technology, connectivity, and clear decision-making rules.
What makes this case study particularly valuable is not just the result, but its context. It shows that predictive maintenance is no longer an experiment, but a robust operating model—when implemented pragmatically.
The real added value arises when such projects are not viewed in isolation, but are used as a blueprint for further use cases.
This case study provides reliable answers to one of the central questions of industrial digitalization:
“Does predictive maintenance really work in everyday practice?”
Yes, when technology, processes, and the vision align.
The complete case study, including all technical details, architectural diagrams, and KPI analyses is available for download as a PDF here.
