TaKaDu, a global software provider of Central Event Management (CEM) solutions for the water industry and Asystom, a specialist in universal smart monitoring for predictive maintenance, today announce a collaboration to provide water utility customers with an integrated and enhanced central event/asset management solution.
The Asystom solution monitors and analyses any rotating machine, to predict anomalies wirelessly and can now easily feed this event information in to TaKaDu’s Central Event Management (CEM) platform.
The Asystom sensors (including ATEX/IECEx) monitor not only heat and vibration, but uniquely ultrasound too, meaning customers have the earliest possible alert of drift (before any damage has occurred). Delivering this early event information to TaKaDu’s automated cloud-based service, enrich the TaKaDu spectrum of network and assets events it manages.
“TaKaDu’s CEM platform is a fantastic tool; you can immediately see the benefit it brings to clients. Most of ours say their biggest issue is finding the correct data. By collecting the data via AsystomPredict and delivering it TaKaDu, clients are able to find the data and have it presented clearly, saving them huge sums of time and money. Together, this offers something cutting edge to the industry.” said Pierre Naccache President & Founder, Asystom.
”Right now, especially, customers are looking for broad solutions to meet the enhancing needs of their network and the growing amount of data and solutions” TaKaDu Founder & CEO Amir Peleg added. “The Asystom solution allows industry customers to minimize downtime by predicting failures of equipment and transmitting all data in real time and accurately to the TaKaDu CEM system and efficiently manage all their events with one easy to-use dashboard”.
About Asystom
Created in 2016, by Pierre Naccache, supported by an experienced international team, Asystom is located in the Toulouse region (France). The company specializes in the development, manufacture and marketing of universal intelligent monitoring solutions for predictive maintenance
