Extending Machinery Life With MaaS

The latest research details the key technologies needed for successful predictive maintenance on machines and machine components.

Downtime Survey Results

While predictive maintenance has existed as a buzzword in recent years, PMMI, The Association for Packaging and Processing Technologies, recognized the need for an industry-wide definition and recently released an in-depth 2021 Predictive Maintenance whitepaper. It defines predictive maintenance as monitoring a machine or a machine component to determine when it is likely to fail and to take action to prevent it from happening, thus avoiding unplanned downtime. 

Using that definition as a jumping-off point, many engineers probably feel that those bases are covered, since the monitoring and maintenance of machinery have existed for a long time. Consider portable monitoring devices, designed to read industrial assets’ health, which obviously fall into the monitoring category. In actuality, it would probably be best to classify these devices as precursors for predictive maintenance, with more of a supporting role alongside emerging predictive maintenance technologies centered on sensors.

In the broadest sense, there are two categories of technology that must be implemented to create a predictive maintenance solution. One of these is hardware. Increasingly, the key category of hardware in any predictive maintenance solution consists of smart sensors. These sensors are constantly growing in capability, and a likely future trend will include processing capacity embedded directly into sensors. Some of the more advanced predictive maintenance solutions noted in the whitepaper are edge computing devices, which sift through gathered data before transmission to the cloud.

The key prerequisite to deciding where to deploy smart sensors for predictive maintenance within specific machines is understanding which data is useful. OEMs and integrators believe that run time, current/voltage draw, and speed are the three most useful data types for performing predictive maintenance. However, run time for predictive maintenance assumes that equipment is most likely to fail in the latter part of its life, which often is not the case. Many failures on motor-driven equipment can happen within the first year due to manufacturing defects or installation errors. 

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