Collecting and analyzing data from a wide range of installed equipment can benefit both processors and their OEMs (Original Equipment Manufacturers). However, implementing a predictive maintenance system can be costly and time-consuming. While condition monitoring tools such as infrared thermometers, ultrasonic probes, and vibration monitors are becoming more affordable, the initial cost remains a barrier for many companies.
Collecting and analyzing data from a wide range of installed equipment can benefit both processors and their OEMs (Original Equipment Manufacturers). However, implementing a predictive maintenance system can be costly and time-consuming. While condition monitoring tools such as infrared thermometers, ultrasonic probes, and vibration monitors are becoming more affordable, the initial cost remains a barrier for many companies.
Data collection is just the first step. To achieve effective predictive maintenance, a significant amount of information is needed to make reliable predictions about potential failures. Often, equipment manufacturers cannot compare machine performance once it leaves their facilities, further complicating failure predictions.
However, some companies are making strides toward more effective predictive maintenance. For example, West Liberty Foods collected performance data from 50 slicing machines and shared it with the slicer OEM, which allowed for the identification of maintenance procedures with numerous downtime events. Additionally, Tetra Pak is using a remote monitoring service that involves additional sensors on their packaging machines, sending data to a cloud server for analysis. If a problem is detected, the manufacturer is notified, and preventive maintenance is scheduled.
Tetra Pak's service also includes the use of Microsoft HoloLens, which enables field technicians to communicate with experts in real-time and receive guidance on maintenance. This technology has significantly reduced machine downtime, resulting in time and cost savings for processors.
Although these advancements are promising, predictive maintenance still faces challenges in terms of cost and complexity. However, with technological advancements and the increasing availability of data, it is likely that we will see more companies adopting innovative approaches to enhance efficiency and reduce downtime in the future.