Senseye is focused in getting to the operational stage as quickly as possible and getting to the ROI evidence in months rather than years. Experience has shown that the most likely things to save money are the simple cases, eg, simple partial counting in oil. In addition, experience shows the things not considered in the first stage of identifying the failure modes are the things that really hurt the business. Senseye turns this on its head, and puts more emphasis on the analytics of the data than the tight specification of the CM functionality.
Senseye announces the release of their ground-breaking Predictive Maintenance software as a service solution. Senseye helps manufacturers maximize Overall Equipment Effectiveness (OEE) and save expenses for manufacturing businesses by reducing machine downtime.
What's the value?
Condition Monitoring (CM) is the process of monitoring data (vibration, acoustic emissions, temperature, etc) from machinery in order to identify changes which may indicate faults.
We recently announced our newest open source project, Roger, which allows the use of the scientific programming language R from Go. We use R at Senseye for quick exploration of data and rapid algorithm prototyping. Data visualisation is key to this process and is an area where R really shines. We felt it was only right that Go should be able to profit from R’s visualisation capabilities and recent updates to Roger have made this a reality. This blog post will demonstrate this capability, creating a Go web API which, when called, will execute an R script to generate a graph which will be returned in response to the API request.