【TMTS 2026 Highlights – Ten Themes】Monitoring raw signals can only tell you "something has gone wrong," but we can tell you "something is about to go wrong."

Many so-called “predictive maintenance” systems on the market are in fact nothing more than preset warning thresholds that trigger alerts once values exceed the limit. This approach is too coarse and fails to truly capture equipment health or potential risks.

What you cannot see, our model can detect! Instead of relying on traditional threshold settings based on experience, our approach lets AI "learn" how your equipment operates. Sometimes degradation begins long before the human eye can notice abnormalities. Our model identifies these early signs—even earlier than the most experienced technicians—achieving genuine predictive maintenance.

This year we launched the imPHM Edge C lightweight smart maintenance solution, powered by machine learning and deep learning models. The system “learns” the behavior patterns of equipment under various operating conditions, automatically detecting: subtle but critical anomalies, statistical shifts or degradation signs invisible to the naked eye, and early warning indicators that even seasoned experts cannot easily explain.

In short, imPHM Edge C is an AI-driven, low-cost, flexible, plug-and-play solution requiring no additional operation. It delivers true Prognostics—predictions learned from data rather than guessed by thresholds. It is no longer about just “watching numbers,” but about building real equipment intelligence through extensive sensor data training.