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AI is the future of smart maintenance

26 November 2024, 09:48

RISE is leading the way with innovative AI solutions for predictive maintenance, applied in industries such as manufacturing, transportation, energy and infrastructure.

With the rapid development of technology, industry is facing new challenges and opportunities. Madhav Mishra, senior researcher and expert in the Smart Predictive Maintenance area at RISE, explains how artificial intelligence (AI) is revolutionizing maintenance processes.

“Smart maintenance with AI is about predicting, preventing and optimizing maintenance processes in industrial environments,” says Madhav. “By analyzing large amounts of sensor data and historical maintenance records, we can predict failures, recommend the right actions in time, and ensure smoother operations.

Applied areas

AI-based maintenance has a wide application in industries such as manufacturing, transportation, energy and infrastructure. Examples of concrete applications include:

- Manufacturing: AI predicts component wear and tear and plans maintenance to avoid costly production downtime.

- Transportation: In rail systems, train components are monitored to detect failures before they occur, improving safety and reliability.

- Energy: For wind turbines, AI can detect anomalies in real time, preventing breakdowns and maximizing energy production.

- Infrastructure: Sensor technology and AI identify critical points in bridges and buildings to ensure their long-term sustainability.

Madhav shares a concrete example:

“In a production line, we were able to use AI to reduce unplanned downtime and extend the life of machines. Similarly, we monitor the performance of batteries in electric vehicles, increasing safety and extending battery life.”

Benefits for customers

The financial and operational benefits are numerous:

- Cost savings: Proactive maintenance reduces the need for emergency repairs and stockpiling of spare parts.

- Increased reliability: Predictive analytics minimize unexpected outages.

- Sustainability: Optimized resource use reduces energy consumption and waste.

A prominent example is a customer in the energy sector who experienced significant improvements in turbine availability, which not only lowered costs but also reduced their carbon footprint.

How it works in practice

The technical solution combines sensor networks, machine learning algorithms and advanced data analysis. The system works through:

1. Data collection: sensors capture information from different components and systems

2. Data analysis: AI identifies patterns and detects anomalies that signal potential problems.

3. Prediction and recommendation: algorithms predict when failures are likely to occur and suggest optimal actions.

4. Actions: Results are displayed in user-friendly interfaces that help engineers make timely decisions.

“For example, if a vibration sensor on a motor detects unusual frequencies, the system flags the motor for inspection before it breaks down, Madhav says. 

RISE is an innovation partner

RISE offers research, development and implementation of AI solutions tailored to specific needs. Working with industry partners, RISE designs and implements systems that deliver concrete benefits.

“In one project with a vehicle customer, we significantly reduced downtime while increasing safety,” says Madhav.

For more information about smart maintenance and current projects, visit Smart Predictive Maintenance or contact Madhav Mishra directly at madhav.mishra@ri.se.

Madhav Mishra

Senior Scientist

+46 10 228 42 69

Read more about Madhav

Contact Madhav
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