5 AI Trends Every Engineer Should Know About
How can you future-proof your operations?
From digital twins to Explainable AI, the future of reliability engineering is looking smarter (and cooler) than ever.
In this article, we explain five AI maintenance trends; transformative tools that can future-proof your operations.
1. Explainable AI (XAI)
Gone are the days of AI as a black box. XAI ensures that the decisions made by AI systems are transparent and understandable.
In reliability, this builds trust and helps engineers refine processes with confidence.
Pros:
Increased trust in AI decisions.
Helps engineers identify and correct inefficiencies.
Better alignment with regulatory requirements, particularly in heavily audited industries.
Cons:
Can be complex to implement.
Requires technical expertise to interpret AI explanations.
Best For:
Industries like pharmaceuticals and utilities where regulatory compliance, data transparency, and operational efficiency are critical.
2. Digital Twins
These virtual replicas of physical assets are a game-changer.
Paired with AI, digital twins simulate performance, predict failures, and optimise operations in real time.
Pros:
Enables predictive maintenance to reduce downtime.
Supports real-time optimisation and long-term planning.
Improves decision-making with rich data visualisation.
Cons:
High initial investment and setup complexity.
Requires continuous updates to stay accurate.
Best For:
Manufacturing and utilities, especially companies with complex production lines or distributed infrastructure. For example, food and beverage facilities could use digital twins to optimise production efficiency and compliance.
3. Edge AI
Why send data to the cloud when you can process it right at the source?
Edge AI enables real-time decision-making by analysing data locally, reducing latency and improving efficiency.
Pros:
Faster insights due to minimal data transmission.
Reduces reliance on expensive cloud storage.
Improves security by keeping sensitive data onsite.
Cons:
Limited by the processing power of local devices.
Challenging to manage across large, distributed systems.
Best For:
Industries with a focus on speed and security, such as food and beverage, where immediate data processing can minimise spoilage or downtime.
4. Federated Learning
Collaborative AI training without sharing sensitive data?
Yes, it’s possible. Federated learning preserves privacy while enabling organisations to benefit from shared insights.
Pros:
Maintains privacy and security by keeping data local.
Allows organisations to learn from aggregated insights.
Enhances collaboration across distributed teams or companies.
Cons:
Requires robust local AI systems for effective training.
Coordination between participants can be complex.
Best For:
Utilities and pharmaceuticals, where privacy concerns are paramount, and collaboration across sites or organisations is necessary.
5. AI-Driven Robotics
Autonomous inspections and repairs are no longer just sci-fi.
AI-equipped robots can perform maintenance tasks safely and efficiently, even in hazardous environments.
Pros:
Reduces risk by limiting human exposure to hazardous conditions.
Operates with precision and efficiency.
Saves time on repetitive or labour-intensive tasks.
Cons:
High upfront costs for robots and training.
Limited by the complexity of tasks they can handle.
Best For:
Manufacturing, especially in industries like heavy equipment or automotive, where maintenance often involves challenging or dangerous environments.
Defining the Present
These trends aren’t just redefining the future of maintenance - they are defining the present. Staying ahead of the curve requires embracing these innovations to future-proof your operations. However, thorough research, careful preparation, and selecting the best strategies are essential to implementing these changes in a way that aligns with your unique workforce.
Read More
Curious to learn more about these trends and their applications? Download our eBook, Artificial Intelligence in Reliability, for a deeper dive into the future of AI in engineering.