|
Input: NormNet accepts data from a variety of inputs, including on-line data bases, sensor systems and off-line data storage media. Pre-processors included in the input function ensure that your existing data can be used without modification at its source.
Learn: NormNet enters a learning process where it discovers the characteristics of the system being monitored using data representing normal or expected environmental and operational conditions and derived from existing system data sources.
Detect: NormNet applies statistical methods and pattern recognition techniques to detect abnormalities that, while small are statistically significant departures from normal and foreshadow potential failures. The algorithms detect extremely small abnormalities (<2% variance) while maintaining low false alarm rates.
Identify: NormNet evaluates the values and relationships of the information used to detect the abnormality to create a fault mode signature that directs your maintenance staff to the most likely cause of the problem.
Presentation: NormNet distributes its results to designated databases, management systems and personnel. Standards-based interfaces, user-friendly presentations and easy-to-use post-processors ensure timely and accurate distribution of data to your existing logistics and maintenance systems. | 
