Integrated Systems Health Monitoring (ISHM) is an implementation of monitoring strategies for complex systems whereby avoiding catastrophic failure, extending life and leading to improved asset management. An ISHM generally encompasses intelligence at many levels and sub-systems including sensors, actuators, devices, etc., but this paper focuses on only smart sensors. Wavelet analysis is used to enhance previous work done in this area. 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 were detected by visual methods or by offline analysis of the data. The algorithms presented in this paper provide 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 developed algorithms have been tested on actual rocket test data provided by NASA’s Stennis Space Center.