Diagnosis of multi-descriptor condition monitoring data

Condition of equipment can be presented by a series of descriptors derived from the raw data. Typically a great number of descriptors are needed and they might not be commensurable. Neural networks can effectively be used as a diagnostic tool to analyze the data for anomalies and known faults. Proper pre processing of descriptors related to a specific machine condition offer an opportunity to automatically learn typical failure patterns and use this experience to diagnose any similar conditions in other machines operating in comparable environments. It is important to understand that the descriptors not only contain information on the type of the fault, but on the severity as well. Therefore the prognosis of failure severity can be based on the experimental data instead of an imprecise statistical approach. This paper presents several patented solutions for automating the diagnostic and prognostic processes using neural networks.