Embedded Computing Design Staff

January 5, 2026

Equipment downtime in a manufacturing facility, or in almost any industrial environment, represents more than just an inconvenience. It is a direct hit to a vendor’s productivity, profitability, and brand. As production lines become more automated and capital-intensive, the tolerance for unexpected failures continues to shrink. That reality is driving manufacturers toward predictive maintenance, where AI and machine learning are used to detect failures long before they result in unplanned shutdowns.

Ken Briodagh

December 11, 2025

One of the toughest challenges with solving predictive maintenance, enabled by AI, is that there is usually no data available for training the system. Modern equipment may not break down for 5 years or more so there is insufficient data when the product is first launched on when and how it will break down. But AI systems using traditional methods such as Deep Learning can only operate when sufficiently large volumes of data are available for training the system and hence, they are incapable of addressing this problem.