Downtime is something that every business wants to avoid. All too often engineers spend most of their time firefighting component failure, leaving little time for system optimisation and improvements. High levels of downtime mean that production is compromised and profits are minimised.
Predictive maintenance solutions are often seen as the expensive option and systems may appear complicated to install and operate. However, condition monitoring allows engineers to identify failure before it occurs, enabling maintenance to be scheduled at a convenient time. But is predictive maintenance really realistic for every business?
The SKF Plug and Play package includes an SKF QuickCollect sensor, the downloadable SKF Pulse app, 24 free pulse checks by experts and access to the e-learning platform.
The QuickCollect sensor measures velocity, acceleration and temperature, ensuring that the readings are within the pre-set parameters. Results are displayed on the app which can be downloaded to most iOS or Android smartphones or tablets.
If an alert is triggered, the user has the option to send the data to a SKF expert for a pulse check. With 24 pulse checks included with the package, predictive maintenance is made both easy and affordable. Further pulse checks are also available, Speak to a member of our sales team to confirm prices.
This simple system and pulse check integration means that no specialist training is required to use the system. Simply connect the sensor, load up the app and view the status of your machine components. If you want to understand more about the system and the performance of your machinery, the e-learning platform will develop and enhance your knowledge, allowing you to increase the benefits of the system.
With many companies looking to limit visits from outside technicians to reduce contact, simple methods to monitor machinery in-house are becoming more and more valuable. The SKF Plug and Play asset health system is a fantastic starting point for any company looking for a cost-effective and scalable entry level predictive maintenance system.