Predictive Maintenance

"IoT Will Not Work Without Intelligince and Machine Learning"

Predictive maintenance (PdM) techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted.

The aim of predictive maintenance (PdM) is first to predict when equipment failure might occur, and secondly, to prevent the occurrence of the failure by performing maintenance. Monitoring for future failure allows maintenance to be planned before the failure occurs.

Equipment Commonality Analysis - Effect of Machine on Paper Towel Product Quality

Now, as we collect massive amounts of IoT data, our ability as humans to make sense of it becomes quite the challenge. To be more efficient, a process is needed that will automatically and in realtime collect data, make predictions, and react. Machine learning and a complete toolchain that supports this model are required.

Real-time dashboard showing predicted pump fails


The expansion of connected sensor data creates new business opportunities for monitoring machine performance and failures in the field and on the factory floor.  Service organizations have up-sell opportunities to offer options to their customers for maximizing value of their assets. Manufacturers can increase uptime, minimize costs, and optimize processes for expensive equipment on the factory floor.  Here is where Ironman Consulting provides analytical technology solutions with international partners.