Early warning algorithms automatically identify the first signs of machine failure

Senseye is rolling out new Trend Recognition algorithms capable of automatically identifying machine problems at an earlier stage than was previously possible.

Senseye’s new Automatic Trend Recognition algorithms use Artificial Intelligence (A.I.) to monitor very gradual changes in the condition of industrial machinery. The algorithms analyse basic diagnostics data from machine sensors to spot small but significant variations in vibration, pressure, temperature, torque, electrical current and other sources that indicate deterioration in machine health.

Senseye has invested more than 8,000 work hours from its data scientists, mechanical engineers and application developers in developing its Automatic Trend Recognition algorithms, which are the first automated offering of its kind to be deployed as a SaaS application. The algorithms will be made available to all Senseye customers around the world by the end of September 2018.

Simon Kampa, CEO of Senseye, comments: “The earliest warning signs of machine failure tend to emerge very gradually and have previously been difficult for an A.I. application to spot. Our new algorithms can reliably and automatically identify problems several weeks earlier than was previously possible. This reduces the cost of downtime by providing a greater window of opportunity for engineers to apply the right maintenance remedy without impacting on production schedules.”