Our Predictive Maintenance solution is built upon five layers of analysis.
- Define vision and achievable goals
- Identify data sources and select relevant inputs
- Perform an off-line analysis and prove the feasibility
- Rapidly implement a small-scale solution and prove the benefit
- Integrate the solution in its full context
At ERNI, we start by creating a model and prediction based on our workstation and show it to you as a proof of concept. We also show you detailed visualisations based on the analysis of the tools.
Once we identify something that works, predictions become inputs that can automate the flow by modelling a good predictor. We deploy it as a web service in order to automatically collect the data and also model some actions based on the model of prediction. It is best to have all these tools gathered in just one toolbox. For example, the Microsoft platform is very powerful, but you can also use any other Cloud provider like Google, Amazon or IBM.
Technology trends are paving the way for a Predictive Maintenance revolution
The science of maintenance is on the cusp of a brand new transformation. The advancements in AI technology combined with Cloud solutions are disrupting the industry the way Predictive Maintenance once did; making room for an easy-to-deploy Predictive Maintenance solution. This new trend is known as the Industrial Internet of Things (IIoT).
The Industrial Internet of Things can collect an impressive amount of data from manufacturing equipment in production and transmit it to devices that can store and analyse it. The main obstacle when trying to implement the technology used to be analysing the data that had been collected. By using an Edge Computing-Servers, this analysis can easily be done on site and in real time. This greatly diminishes the burden on networks and also keeps the costs low. An Aberdeen Group study found that the best-in-class organisations (top 20 per cent) that employ predictive analytics for asset management attained:
- Increase in return on assets (ROA) of up to 24%
- Reduced unscheduled downtime to 3.5%
- Improve overall equipment effectiveness to 89%
- Cost reduction of maintenance of 13%.