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This investigation explores the dynamic and aerodynamic effects of imbalance on the motion of a horizontal axis wind turbine (HAWT). The nonlinear dynamic equations describing the motion of a HAWT with both static and dynamic imbalance are derived and investigated. A perturbation scheme is developed to solve a simplified version of the nonlinear differential equations. The static imbalance of the DOE's test wind turbine has been calculated and its effects have been identified as 60% of the observed once per azimuthal revolution (1P) variance in the low speed shaft torque (LSST) spectrum. It was also found that a small amount of blade pitch imbalance, 1 degree, is sufficient to account for the remaining 40% variance observed in the 1P low speed shaft torque field data. The perturbation analysis prompted a fully nonlinear numerical investigation of both the statically and dynamically imbalanced rotor dynamics. The 1P variation in LSST due to mass imbalance was found to cause chaotic motion. The necessary system parameters and/or operating conditions which resulted in classic chaotic responses in both the yaw and teeter motions were identified.

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The verification of the Electrically Scanned Thinned Array Radiometer (ESTAR) sensor for remote sensing of soil moisture is presented, and its ability to measure ocean salinity is examined. The research and development that led to verification are presented, beginning with the development of a linearly polarized thinned array, and continuing with the reconstruction of complex correlators, calibration signal distribution, and instrument packaging. Experimental results from two airborne soil moisture campaigns are presented: the first serves to quantitatively verify ESTAR soil moisture capability by comparison with an accepted model and by comparison with another operational sensor; the second demonstrates qualitatively the continuing utility of ESTAR for soil moisture under a variety of soil conditions. Salinity data are presented that suggest an ability to resolve changes in ocean salinity. As the second soil moisture experiment and the salinity experiment are quite recent, full analysis is deferred until more complete in situ data become available. Practical comments on ESTAR experimentation are included in the appendices.

A diagnostic method is introduced for helicopter gearboxes that uses the gearbox structure and characteristics of the 'features' of vibration to define the influences of faults on features. The structural influences in this method are defined based on the root mean square value of vibration obtained from a simplified lumped-mass model of the gearbox. Featural influences characterize the frequency-specific information of the vibration features which correspond to the type of gearbox faults the features represent. These influences are defined as fuzzy variables to account for the approximate nature of the simplified model of the gearbox. The fuzzy structural and featural influences are then incorporated as the weights of a connectionist network for diagnosis, so as to avoid supervised training of the network. Diagnosis in this Structure-Based Connectionist Network (SBCN) is performed by propagating the abnormal features through the weights of SBCN to obtain fault possibility values for the components in the gearbox. In the proposed diagnostic method, vibration features obtained from raw vibration are first utilized by an unsupervised Fault Detection Network (FDN) for identifying the presence of faults. Fault diagnosis is then performed by SBCN only if the presence of a fault is prompted by FDN. Since SBCN uses abnormal vibration features as inputs, an unsupervised pattern classifier is designed for abnormality-scaling of features. The abnormality-scaled features are then propagated through the weights of SBCN for isolating faulty components. The proposed diagnostic method is experimentally evaluated in application to two helicopter gearboxes: OH-58A and S-61. Experimental vibration data for the OH-58A gearbox were collected at the NASA Lewis Research Center, and vibration data from three S-61 gearboxes rejected in field operation were collected at Sikorsky Aircraft. The proposed method is evaluated in diagnosis of the OH-58A gearbox faults as well as isolating the faults within the three S-61 gearboxes. The diagnostic results indicate that the SBCN is able to correctly diagnose about 80% of the OH-58A gearbox faults and all the faults in S-61 gearboxes. In addition to evaluation of the structural influences based on diagnostic results, they are validated by comparing them with influences obtained from experimental RMS values as well as the weights of a neural network structurally similar to SBCN, but trained through supervised learning. Moreover a sensitivity analysis is performed to study the effect of variations in structural influences on diagnostic results. The structural influences developed in this method can also be utilized for assessing the importance of various gearbox accelerometers in diagnosis. Three indices are defined based on the structural influences to quantify various aspects of accelerometer significance and are evaluated using the data from the OH-58A gearbox.