Able System's autonomous analysis engine operates on a powerful, two-stage philosophy that moves beyond simple alarms to deliver actionable intelligence. First, for initial fault detection, we employ a cutting-edge unsupervised learning paradigm. Unlike traditional systems that require extensive historical data of failures, our unsupervised learning models operate right on the first collected raw vibration signature of your machinery. This is enhanced by advanced adaptive filtering techniques that autonomously process the raw signal to reveal hidden periodic impulses, ensuring even the earliest signs of a developing issue are captured without the need for manual interpretation or prior fault examples. The bearing fault rate is estimated for each bearing component (outer race, inner race, rolling element) to guide the user to assess possible cause-and-effect relations in that specific fault.

Once an anomaly is detected, our proprietary Energy Source Analysis engine provides a precise diagnosis to pinpoint the root cause. This method deconstructs the machine's complex vibration signature and using a high-fidelity digital twin of your asset, calculates the exact energy contribution of each component—be it a shaft, gear, fan blade, or bearing. The system then quantifies what percentage of the anomalous vibration is coming from each source, transforming a generic alert into a clear, actionable diagnosis. This allows your team to move from symptom detection to true root-cause analysis, enabling targeted, efficient maintenance that prevents unplanned downtime and increases production performance.