Industrial Explainable AI Framework
Engineers need to comprehend AI decsion making for embedded actioning.
Independent Project in System Design, Digital Experience, AI
2026
01 The Challenge
Predictive maintenance AI can analyze sensor data and detect when a machine might fail.
But prediction alone is not enough.
Industrial systems are becoming increasingly complex. As AI takes on more responsibility for critical decisions, it must remain accessible and actionable for the humans working with it. In industrial environments, decisions need to balance risk, cost, downtime, and production impact. The challenge is therefore not just to make accurate predictions, but to make AI recommendations understandable and trustworthy – while keeping the engineer in control.
AI will detect failures, order spare parts, solve issues and improve processes on its own. How can human workers oversee these activities and interact with the system?
02 The Concept
What if AI could not only detect failure, but help workers decide what to do next.
The system continuously monitors machine data, detects anomalies and predicts potential failures. It then explains why a warning was communicated and proposes several possible actions.
03 The Experience
AI recommends, human decides.
From real-time machine monitoring to predictive maintenance and explainable AI, the interface turns complex industrial data into a clear decision process. Operators can explore what is happening, why AI thinks it is happening, what options are available, and what each decsion could mean.
04 Explainable Decisions
Transparency is part of the interface
Instead of presenting a black-box prediction, the system highlights the signals behind it - such as rising vibration, temperature or energy consumption - together with confidence, risk and potential impact. This allows operators to question, understand, and validate AI recommendations before taking action.
Work in progress...
