Mostly all the industries are often doing preventive maintenance. This is fundamentally based on time rather than actual equipment conditions and predictive methods. Typically production lines are composed of multiple assets, and maintenance is performed at a high frequency with regular intervals to lower the likelihood of expensive failures significantly. This routine will result in unnecessary maintenance costs and unanticipated downtime.
But, modern industrial production lines are complex interactions of multiple mechanical, electrical and controls systems. A large number of possible operating configurations and nonlinear cross dependencies make it harder to carry out general preventive maintenance mechanisms. This made industries choose predictive maintenance technologies to detect failures reliably and identify and respond to unanticipated equipment or process degradation. This insight information enables industries to work out the actual operating conditions of the devices, equipment, and machinery to predict the failure far ahead of time.
Accurately predict when equipment is likely to fail not only enables reduced downtime, it makes maintainers and asset managers optimize maintenance activities.
By improving maintenance scheduling, predictive maintenance can yield the following benefits:
Predictive maintenance is fundamentally driven by sensors continually capturing data from equipment in the field, optionally processing it at the edge before transmitting critical data to a centrally hosted system for both near real-time and historical analysis.
Already pre-trained machine learning models are capable of detecting anomalies, suspicious behaviours and predict equipment failures, performance degradation, and process distribution.
If we take the example of typical generator life, preventive maintenance is typically scheduled based on the time span, as per the timeline diagram above, preventive maintenance only detect after second scheduling. At the same time, predictive maintenance is smarter to tell the operations to make decisions at a most convenient time.
At Cerexio, we are leveraging industrial AI technologies to accurately monitor the current condition of machines or any type of industrial equipment, using automated low-latency real-time analytics and pre-trained machine learning models. By employing real-time predictive analytics on an array of industrial IIoT data, process manufacturers have clear visibility into production processes and their losses compared to time-based preventive mechanisms.