| Under a project being undertaken at NASA's Stennis Space Center, an integrated framework is being developed for intelligent monitoring of smart elements. Integrated Systems Health Monitoring is an implementation of a monitoring system which is robust, user friendly, and adaptable. This thesis focuses on only smart sensors, and shows the advantage of utilizing an enhanced version of a previously developed intelligent sensor algorithm, DATA-SIMLAMT, called Enhanced DATA-SIMLAMT or EDATA-SIMLAMT. This new version contains additional properties and states for improved data interpretation. The additional properties are based on a signal processing tool known as wavelet analysis. The major advantage provided by adding wavelet analysis is the ability to detect sudden transitions as well as get a handle on the frequency content using a much smaller data set then that required by the traditional Fourier transform method. Historically, sudden transitions could only be detected by a manual analysis of the data. EDATA-SIMLAMT provides an opportunity to automatically detect many data anomalies as well as improved data-correction and sensor health diagnostic abilities. The newly developed system has been tested on actual rocket test data provided by NASA's Stennis Space Center. |
