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A series of tests have been conducted to determine the survivability and functionality of a piezoelectric-sensor-based active structural health monitoring (SHM) SMART Tape system under the operating conditions of typical liquid rocket engines such as cryogenic temperature and vibration loads. The performance of different piezoelectric sensors and a low temperature adhesive under cryogenic temperature was first investigated. The active SHM system for liquid rocket engines was exposed to flight vibration and shock environments on a simulated large booster LOX-H2 engine propellant duct conditioned to cryogenic temperatures to evaluate the physical robustness of the built-in sensor network as well as operational survivability and functionality. Test results demonstrated that the developed SMART Tape system can withstand operational levels of vibration and shock energy on a representative rocket engine duct assembly, and is functional under the combined cryogenic temperature and vibration environment.

A 1.2 micron O S chip set (processor and controller) has been designed for applications i n high performance FET based d i g i t a l signal processing systems. The processor chip performs about 500 million arithmetic o p r a t i o n s Fer second and operates a t an 1/0 r a t e of 5 billion b i t s per second. The controller chip provides t o t a l system control for FFT based DSP systems. Algorithm such a s FET, spectrum analysis, d i g i t a l f i l t e r i n g (via frequency domain) can be defined on the chip set by coding 5 t o 10 instructions i n t h e controller.Although a single chip set can process data rates a t very high speeds (e.g., lK FFT i n 61 ).IS), multiple stages can be cascaded very simply for extremely high performance (up t o 100 MHZ data rates).

The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically "learns" a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to "train" and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it "learned" a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location.

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