Predictive maintenance uses machine learning to help determine the condition of in-service equipment in order to accurately predict when maintenance should be performed, around the production schedule. Using predictive maintenance to monitor your equipment and other assets can help determine condition and detect anomalies and possible defects to be fixed before failure occurs.

Our solution, built on the Azure platform, utilising Machine Learning algorithms, with Azure databricks, and Synapse amongst other services, is for Facilities Managers and Asset Owners who want to use their data to optimise the condition of in-service equipment in order to accurately predict when maintenance should be performed.

What you will Get

Our solution will be tailored to your business needs with the aim of providing you with the following two business efficiencies;

1. A pilot ML predictive model that scores an asset on its likelihood of failure within a timeframe
2. A data driven approach to optimise service regimes for assets.