Superlinear’s AI for Sustainability series
Our Machine Learning Engineer, Pierre Gerardi, delivered a presentation on harnessing AI for predictive maintenance as a means to boost sustainability. He highlighted the significance of maintenance across different industries, examined various maintenance approaches, and demonstrated how AI can improve these strategies by offering valuable insights, detecting anomalies, and generating predictions for strategic maintenance planning, and more.
Watch his presentation below, or continue reading instead ⬇️
The importance of a good maintenance strategy
Maintenance is a set of activities aimed at keeping a system in good working condition to minimize downtime, performance losses, and breakdowns. A comprehensive maintenance strategy typically includes inspection, repair, replacement, and upgrading devices to maintain optimal working conditions.
A good maintenance strategy can help improve sustainability in several ways:
Increased device lifetime: Regular maintenance helps to extend the life of devices, reducing the need for frequent replacements and minimizing waste.
Enhanced operational efficiency: Well-maintained devices operate more efficiently, consuming less energy and producing fewer emissions.
Improved safety and reliability: High-quality devices are more reliable and safer to use, leading to fewer incidents and a better work environment.
Maintenance strategies
There are three main types of maintenance strategies:
Reactive maintenance: This is the least desirable strategy, as it involves waiting for devices to break down before taking action.
Preventive maintenance: This strategy involves routine checks and minor fixes to keep devices functioning for longer periods.
Predictive maintenance: This is the ideal strategy for most companies, as it involves predicting when parts will break down and planning maintenance accordingly, thus minimizing the required peak of efforts.
Artificial intelligence as a maintenance copilot
Data and AI can help improve maintenance strategies in the following ways:
Data visualization: By aggregating device-related data and presenting it in a dashboard, experts can quickly identify areas requiring maintenance.
Anomaly detection: AI can detect unusual patterns in data, such as a sudden temperature spike in a device, and alert maintenance crews to potential issues.
Prediction: AI can establish relationships between historical device data and breakdowns, allowing it to predict future failures and inform optimal maintenance schedules.
Predictive maintenance for ships by Toqua
A small company in Gent, Toqua, is implementing a predictive maintenance strategy for ships. Many ships still need to follow a reactive maintenance strategy, which leads to increased fuel consumption, waste, and suboptimal repairs. Toqua uses AI to analyze physical and performance data from ships to predict when parts will break down. This allows for more efficient planning and execution of maintenance tasks before departure and during trips, resulting in less downtime, fewer repair station visits, and overall improvements in safety and sustainability.
In conclusion
Predictive maintenance, powered by AI and data analytics, offers significant benefits for industries such as shipping. By implementing a predictive maintenance strategy, companies can extend the lifetime of their devices, reduce waste, and improve overall sustainability. With AI as a copilot, experts can optimize their maintenance strategies and make better-informed decisions, ultimately contributing to a more efficient and environmentally-friendly industry.
Are you ready to welcome AI as your copilot in making more informed decisions that also benefit our planet? Don't hesitate to reach out to Superlinear!
