Oil and Gas, Manufacturing
Europe
3
The client is a mid-sized industrial manufacturer known for producing high-precision products that require reliable, continuous operation. Their production depends on rare, high-value pumping equipment that plays a critical role in ensuring product quality and consistent output.
The existing maintenance process suffered from three critical issues:
Although the pumps were equipped with sensors measuring temperature, voltage, pressure, and operating hours, the company lacked structured data about historical failures, repairs, and root causes. This made traditional predictive maintenance approaches difficult to implement.
The pumps consisted of multiple interdependent subsystems — bearings, engine components, isolation systems. A failure in any subsystem could trigger a full pump shutdown and production loss.
These pumps are extremely costly to repair and difficult to replace due to long manufacturing lead times. Even a single failure during production can compromise an entire batch during QA, resulting in major financial losses and operational delays.
The client needed a solution that could leverage existing IoT sensor data, operate reliably despite limited maintenance logs, and provide high-confidence predictions ahead of planned maintenance cycles.
Our team developed an AI-driven predictive maintenance framework using available sensor data, advanced statistical techniques, and iterative ML model refinement. The project scope included four major components:
We collected and normalized time-series data from multiple pump sensors:
The team cleaned, synchronized, and enriched the dataset to compensate for missing maintenance records and inconsistent entries. Additional derived features were introduced to represent wear patterns, degradation rates, and subsystem anomalies.
We analyzed sensor readings to understand how the pumps behaved over time and what early warning signs preceded failures. The team identified key patterns and cleaned inconsistencies.
This enabled us to transform raw IoT logs into a structured dataset suitable for building predictive models despite incomplete historical labels.
We tested several AI approaches to find the one that could reliably predict pump failures based on sensor data. After comparing different models, we selected the method that delivered the most accurate and stable results.
To validate the model in real operational settings, we ran a parallel decision system:
After several maintenance cycles, the company replaced a few components strictly following the algorithm’s recommendation. Subsequent disassembly showed that these parts were indeed near the point of failure — confirming the model’s accuracy and reliability.
The predictive maintenance solution delivered significant operational and financial improvements:
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