CPMXai:Cognitive Predictive Maintenance and Quality Assurance using EXplainable AI and Machine Lea

Important results from the project

The project has successfully driven research and applications in predictive maintenance and explainable AI. By combining industrial data with AI methods, the project has developed systems that can identify early signs of equipment deterioration, enabling rapid action and reducing risks. The project achieved its objective by developing predictive maintenance solutions, making AI decisions more transparent and understandable, and laying the foundation for reliable AI.

Expected long term effects

In the longer term, CPMXai´s results from three use cases slurry pumps in mines, electron microscopes and embedded testing devices are expected to impact industry and research. Through predictive maintenance and XAI, partners can achieve more efficient, reliable and cost-effective solutions. Explainability increased industry´s understanding of AI value. It contributes to reliable AI and methods for other critical areas, while networks in Sweden, Europe, the US and Asia strengthen collaborations.

Approach and implementation

The project followed a structured plan across three use cases, the implementation went well and met the goal of the project. The time plan needs to be adjusted as there were initial challenges due to partner company´s reorganization. But the collaboration ultimately proved excellent, resulting in solid outcomes, fulfillment of company expectations, and new avenues for research.

External links

CPMXai enhances predictive maintenance using AI/ML, cognitive explanations and automation.