Senseye, the Machine Health Management company, welcomes ADLINK Technology Inc. to the Senseye Ready Partner Ecosystem, enabling customers to utilize ADLINK’s range of machine condition monitoring (MCM) devices to achieve plug and play integration with Senseye’s predictive maintenance software suite (Senseye PdM).
ADLINK Technology Inc., a global leader in edge computing, joins the Senseye Ready ecosystem to offer predictive maintenance solutions supported by the Senseye ROI Lock™ guarantee. Manufacturing, maintenance and operations managers can now implement predictive maintenance with complete confidence that they will get a minimum 100% return on their software investment.
Senseye Ready hardware and devices offer simple integration with Senseye’s predictive maintenance platform. Customers benefit from a wide choice of compatible equipment to suit their individual requirements and budget.
ADLINK’s range of rugged machine condition monitoring (MCM) devices are pre-configured with its Edge™ IoT software, allowing the aggregation of asset performance data from multiple sensors at the edge on the factory floor. This data is streamed to Senseye’s cloud-based platform for automated analysis, identifying abnormal behavior or early signs of known failure modes. By alerting maintenance managers to take action, Senseye helps to avoid costly unplanned equipment shutdowns.
Graham Dunlop, Partner Director - Senseye, comments: “We developed Senseye Ready to provide additional benefit to our customers by simplifying procurement and assuring them access to the very best hardware solutions to increase maintenance efficiencies and avoid unplanned machine downtime. We saw a perfect fit with ADLINK and the focus both companies have on improving manufacturing operations and driving adoption of Industry 4.0 best practice.”
Daniel Collins, Director of IoT Solutions & Technology - ADLINK, comments: “Joining the Senseye Ready Partner ecosystem is part of ADLINK’s commitment to making factories smarter. Our offerings support data acquisition and analysis from many different types of manufacturing assets, enabling predictive maintenance capabilities across fleets and factories to yield higher productivity and reduce losses due to machine downtime.”