Operational downtime is one of the biggest risks that large manufacturing companies are exposed to. The business cost of a critical machine failure and downtime can cost millions. Predictive maintenance offers a proactive approach to solving unplanned interruptions due to equipment or machine failure that can lead to costly downtime.
Polymorph is currently developing two predictive maintenance solutions founded on IOT data and incorporating machine learning. The first is for an international German shipping pump manufacturer and the second is for the predictive maintenance of large industrial solar energy plants. Polymorph was granted access to the manufacturers’ existing and historical data to first analyse and then use to create and improve a machine-learning model that can detect operational anomalies and determine on a granular level when and exactly where certain equipment is starting to drift outside of its performance specifications which is usually indicative of an imminent breakdown.
German shipping pump
The aim of the solution developed here is to eliminate environmental noise and so render diagnostics more accurately. Ultimately, the ship’s management team is now able to understand both current and future machine functioning abilities. With proper predictive maintenance, over the lifetime of a machine, faulty components can be swapped before malfunctioning or performance decreases.
Energy plant company
In this case, a photovoltaic (PV) solar plant supplies electricity through a complex combination of equipment, including many solar panels. Predictive maintenance aims to detect faults or potential breakages before the human eye detects it and, of course, before a costly breakdown occurs.
Read the full case study here
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