Machine learning component diagnostics

Bluefruit Software’s UK-based embedded specialists work with innovative clients worldwide. Our team of over 85 includes software engineers, testers, UX, compliance and quality specialists. Bluefruit believes excellent embedded software happens through investing in people and processes and a dedication to quality. We aim to meet your organisation’s needs across hardware design, software, and high-level technical consulting.

AI isn’t just for the cloud. With advances in microprocessors and microcontrollers supporting AI directly on embedded systems, there’s no better time to discover how you can bring machine learning-based intelligent analysis and decision making to embedded systems.

Bluefruit Software are involved in AeroSpace Cornwall funded R&D around AI and machine learning in embedded systems and edge AI projects for several clients.

We have previously successfully designed, implemented and sold IP for an innovative machine learning algorithm that allowed our client to pre-empt device failure, and infer device state without the use of invasive sensors.

We would like to identify suitable US partners that are interested in exploring the feasibility of similar technology in the offshore wind sector. We would be particularly interested in those that can provide specialist knowledge of component systems and facilitate data collection and testing.

Why Machine Learning?

1. Predictive Maintenance: Machine learning algorithms analyze vast datasets from sensors and historical turbine performance to predict when maintenance is needed. This proactive approach reduces downtime and minimizes costly unscheduled repairs.

2. Anomaly Detection: A ML implementation can detect subtle anomalies in turbine data that are often missed by traditional monitoring systems. By identifying irregularities in real-time, we can address potential issues before they escalate into major failures.

3. Data-Driven Insights: Machine learning provides invaluable insights into the health and performance of individual components, such as blades, gearboxes, and generators. This granular level of analysis enables precise maintenance actions and extends the lifespan of components.

4. Cost Savings: By minimizing unplanned maintenance and optimizing service schedules, machine learning reduces operational costs and maximizes the return on investment for offshore wind farms.

5. Safety First: Preventing component failures not only preserves the turbine's integrity but also enhances the safety of offshore wind operations. Minimizing the need for personnel to access remote turbines for repair reduces potential risks.

6. Environmental Benefits: Reliability through machine learning not only reduces maintenance-related emissions but also ensures a consistent and reliable power supply from clean energy sources.

We're looking to explore all possibilities for collaboration, so please reach out and talk to us.