predictive fleet maintenance & energy management
Our client is a maritime data analytics business focusing on the energy management of significant maritime assets. The key to Digital Bricks’ solution was to deliver predictive maintenance and insights to optimise their customers’ fleets’ performance and energy consumption.
- Build an ML-powered solution to enable our clients’ end-customers to move from “reactive” to “predictive” fleet maintenance, using vast datasets from their vessels.
- To develop a Machine Learning solution to identify independent variables suitable for emissions control levers. This would allow vessel owners, operators and charterers to identify practices that will manage and reduce emissions in line with industry goals.
- Created alternative models and algorithms to identify anomalies across different vessel types.
- Identified a collection of solutions to generate actionable insights with early alerts and notifications to prevent failure.
- Digital Bricks built models and algorithms which revealed inefficiencies in the engine combustion process and generated valuable new insights into root causes.
- These improvements can be applied to reduce fuel consumption and emissions.
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