For companies where cost of critical machine failure and operational downtime is a big risk, countermeasures to avoid downtime is crucial. In this regard, one of the best countermeasures to avoid costly downtime is predictive maintenance. Predictive maintenance offers a proactive approach to solving unplanned interruptions due to equipment or machine failure that can lead to costly downtime.
Pumps operate in complex environments inside large housings that are rarely visible outwardly which means it is often harder to hear or see the warning signs or trends that predict an imminent failure. In this scenario, predictive maintenance can proactively analyse and detect potential errors by, for example, the machine’s vibrations as a result of the ocean hitting against the ship as well as outside temperatures. The predictive maintenance model can further eliminate environmental noise and so render diagnostics more accurately. A further benefit is that 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. It also opens the possibility of an alternative business model for the pump manufacturer, allowing them to move from selling pumps to providing Equipment as a Service.
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.
The Polymorph Platform
It is important to keep in mind that effective predictive maintenance requires accurate data. The data, however, is often found at various locations and not optimally utilised. A further important aspect to consider is cleaning the data and so exclude any unnecessary data that plays no role in improving and refining the machine learning model.
In terms of data storage, data is stored in a central cloud database making it easy and fast to extract and write to. Polymorph further created an easy to use user interface that can be built into intelligent dashboards. The next step in the process is to build a trained, healthy model that will be used to detect errors and abnormal behaviour. The aim of this model is for it to never stop learning. As it collects and processes increased data it becomes better trained to make better decisions.
Alerts and reporting
The Polymorph platform can easily integrate with business processes, create alarms and send emails and SMS’s to selected parties in case of an emergency or simply inform the maintenance team should a machine or piece of equipment need to be serviced.
Downtime as a result of machine failures can be more costly than the actual machines breaking and, as mentioned above, can cost companies millions. Predictive maintenance using advanced IOT and machine learning can prevent unwanted downtime and save organisations a lot of money. Machine learning is a cost-effective approach to predicting imminent failures and the maintenance cycles of machinery or equipment. This technique detects any deterioration or the onset of it in machinery that requires maintenance and it can aid ship pump manufacturers to address any maintenance issues beforehand.