What if engineers could predict when maintenance should be performed on machinery and equipment? Modeling data collected by new sensors inside machines and engines now enable predictive maintenance (PdM).
The end-game is maximum efficiency and the complete automation of business decisions.
First Steps: Data Monitoring
PdM is designed to avoid unplanned downtime and accidents, reduce unseen damage, wear, engine failure and emergency repairs, and make parts replacement merely a matter of scheduling. At its core, PdM systems can be very simple, says Mobeen Khan from AT&T’s Industrial IoT Solutions Dept. The service has deployed PdM systems in the heavy equipment industry. “It’s very simple to know an alarm is being generated, or the check engine light in a tractor is on. You can then manually dispatch someone to go fix something.”
The next step is to monitor streams of data to see if something is about to break, increasing the uptime of assets. “Say there was a downtime of two days every two years. We can get that down to a few hours.”
Maintenance on Demand
A great example of PdM at work is in the logistics industry. Supported by the EU, the Maintenance On Demand (MoDe) project involves 11 companies—including Volvo and DHL—pooling their technical know-how to develop a commercially viable truck that autonomously signals when and how it requires maintenance.
Sensors are placed in the engine’s injection, damper and oil systems to detect damage or degradation. They send updates over a wireless network to a maintenance platform, which uses this condition monitoring data to make maintenance decisions.
Since all the trucks are centrally monitored, fleet management software can re-route them to maintenance depots. Proponents of the MoDe project claim that uptime is increased by as much as 30%.