Predictive maintenance and anomaly detection in green energy production

To minimize the downtime cost of critical production devices in the field, a major manufacturer has hired Koehn AI to develop a predictive maintenance system.

When it comes to energy production, any downtime - as short as it may be - comes at a cost. It is therefore essential to detect possible failure points before they happen, so that technicians may preemptively address the fault and minimize costs; all in real-time.

Koehn AI was hired in 2024 by a global manufacturer of solar technology devices to develop an end-to-end predictive maintenance solution, that would allow users to monitor their devices for errors and anomalies. We set out to build a comprehensive end-to-end product: from configuring the devices for data output, to setting up data pipelines and data governance infrastructures, to AI model building and output visualization. In the process, we have leveraged state-of-the-art data analytics and warehousing services: Microsoft Fabric and Databricks instances were set up in Azure with a code-first, infrastructure-as-code approach, upon which ETL pipelines based on Spark structured streaming were deployed. Once access to clean and actionable streaming data had been ensured, the extensive scientific and technical knowledge-base of Koehn AI came into play in the development of custom, use-case-specific algorithms, designed with the internal workings of the devices in mind. The algorithms, which now run on a scheduled basis over the database, output their results to a pleasing visualization dashboard.

What we have provided

• A scalable, real-time data pipeline that can handle the growing demands of the client’s expanding device fleet.
• A comprehensive, marketable anomaly detection solution as a value-added service to the client’s customers.
• A user-friendly dashboard that enables real-time monitoring and alerts, exposed to internal developers.