In January 2021 the Bachmann Group, global leaders in wind automation, announced their acquisition of German tech start-up Indalyz Monitoring & Prognostics GmbH (IM&P).

The move enriches Bachmann’s well-established Condition Monitoring portfolio and, with the addition of owner Professor Michael Schulz and his expert team, will set new standards in the development, implementation and commissioning of intelligent monitoring software. In particular focus is the development of innovative algorithms assisted by new mathematical models for complex systems.

“We are delighted to welcome IM&P to the Bachmann family,” says Bernhard Zangerl, CEO of the Bachmann Group. “Our organizations are well aligned when it comes to pushing the boundaries of Condition Monitoring. This partnership is an exciting opportunity to enrich our applications with artificial intelligence (AI) and Machine Learning, and to deliver new solutions to our customers’ challenges.”

With the addition of Professor Michael Schulz, who holds countless publications in renowned journals, Bachmann advances the expansion of its modern, certified Remote Monitoring Center, where a team of experts diagnoses 7,000 machines and plants worldwide daily.

Strategic expansion in the fields of predictive and preventive maintenance is invaluable for manufacturers and operators alike, especially when it comes to expensive and production-critical machinery. The basic principles of data acquisition apply to an enormous range of industries and applications, and the corresponding analysis, as well as the determination of corrective actions, apply irrespective of plant, product or manufacturing method.

With the acquisition, Bachmann signals its intentions for significant further growth. There is still plenty to do, and effective solutions are always in demand. Bachmann continues to significantly and specifically strengthen its competence in the areas of Structural Health Monitoring, predictive maintenance and higher-level Condition Monitoring to lower maintenance costs and extend machine lifecycles.