Identification of sudden transitions in sensor data from rocket tests using wavelet transforms within an integrated health monitoring system

Under a project undertaken at NASA's Stennis Space Center, an integrated framework has been 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 paper focuses on smart sensors, and shows the advantage of utilizing an enhanced version of a previously developed intelligent system, 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 wavelets. The major advantage provided by adding wavelet analysis is the ability to detect sudden transitions as well as obtaining 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 visual method or by offline analysis of the data. EDATA-SIMLAMT provides an opportunity to automatically detect sudden transitions as well as many additional data anomalies, and provide improved data correction and sensor health diagnostic abilities. The newly developed system has been tested on actual rocket test data from NASA's Stennis Space Center. (C) 2017 Elsevier Ltd. All rights reserved.