With IIoT democratizing asset and process information for remote condition monitoring, the next grand battle in the PdM market will be between OEMs and maintenance companies. The maintenance field has been greatly impacted by digitalization. As data collection and analysis capacities increased, companies were able to move from reactively performing maintenance in their equipment to
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Even companies that are playing in the advanced league of the condition monitoring game may face the challenge of knowing which machines need to be prioritized for maintenance. For example, how can a professional responsible for the maintenance of compressors in a company realize which ones need to go to the top of priorities list?
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Vibration monitoring is key to performing condition monitoring-based maintenance in rotating equipment such as engines, compressors, turbines, pumps, generators, blowers, and gearboxes. However, periodic route-based vibration monitoring programs are not enough to prevent breakdowns, as they normally offer a narrower view of the machines’ conditions. Adding Machine Learning algorithms to this process makes it scalable,
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Predictive maintenance, despite its measurable results and successes, has been suffering from lack of scalability. Models designed and trained for a given machine with a particular set of sensors often had to be retrained before being used with other similar machines. In addition, the quantity and variety of machines in a factory makes developing, training,
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