An artificial intelligence algorithm was developed that greatly increases accuracy in diagnosing the health of complex mechanical systems. Typical vibration analysis searches for anomalies in the vibration of machinery such as engines and gearboxes. These changes in vibration can signal wear and future maintenance needs long before the machinery fails.
The difficulty in extracting useful information from machinery vibration is the amount of random noise that exists in normal operating environments. Finding that useful information has been a needle-in-a-haystack problem. The new approach takes an artificial intelligence algorithm and teaches it the basic principles of physics that govern faults in a vibrating environment.