Software Predicts Machine Failure


Senseye is the only solution that offers scalable predictive maintenance. It uses machine learning to perform condition monitoring and prognostics analysis, without requiring deep pockets or a team of expert data analysts.


Driven by Industry 4.0, industrial operations are increasingly autonomous. Factories contain numerous sensors that provide real-time data on the status of production and machinery to optimize operations.


New to the platform are Trend Recognition algorithms that are capable of automatically identifying machine problems at an earlier stage than was previously possible.


The Automatic Trend Recognition algorithms use Artificial Intelligence (A.I.) to monitor very gradual changes in the condition of industrial machinery. The algorithms analyze 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.

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