In order to solve the fatigue damage identification problem of helicopter moving components, a new approach for acoustic emission （AE） source type identification based on the harmonic wavelet packet （HWPT） feature extraction and the hierarchy support vector machine （H-SVM） classifier is proposed. After a four-level decomposition of the HWPT, the energy feature of AE signals in different frequency bands is extracted, which overcomes the shortcomings of the traditional wavelet packet including energy leakage, and inflexible frequency band selection and different frequency resolutions on different levels. The H-SVM classifier is trained with a subset of the experimental data for known AE source types and tested using the remaining set of data. The results of pressure-off experiments on the specimens of carbon fiber materials indicate that the proposed approach can effectively implement the AE source type identification, and has a better performance in terms of computational efficiency and identification accuracy than the wavelet packet （WPT） feature extraction.
A new compensation method for angular rate estimation of non-gyro inertial measurement unit （NGIMU） is proposed in terms of the existence of aecelerometer mounting error, which seriously affects the precision of navigation parameter estimation. Using the accelerometer output error function, the algorithm compensates the posture parameters in the traditional algorithm of angular rate estimation to reduce the accelerometer mounting error. According to the traditional aceelerometer configurations, a novel nine-accelerometer confi-guration of NGIMU is presented with its mathematic model constructed. The semi-hardware simulations of the proposed algorithm are investigated based on the presented NGIMU configuration, and the results show the effectivity of the new algorithm.
