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A differential capacitance detection circuit aiming at detection of rotating angle in a novel levitation structure is presented. To ensure the low non-linearity and high resolution, noise analysis and non-linearity simulation are conducted. In the capacitance interface, an integral charge amplifier is adopted as a front end amplifier to reduce the parasitic capacitance caused by connecting wire. For the novel differential capacitance bridge with a coupling capacitor, the noise floor and non-linearity of the detection circuit are analyzed, and the results show that the detecting circuit is capable of realizing angle detection with high angular resolution and relative low non-linearity. With a specially designed printed circuit board, the circuit is simulated by PSpice. The practical experiment shows that the detection board can achieve angular resolution as high as 0.04° with a non-linearity error 2.3%.

In the novel prototype of micro gyroscope structure, the new configured capacitance sensing scheme for the micro gyroscope is analyzed and the virtual instrument based detection scheme is implemented. The digital lock-in amplifier is employed in the capacitance detection to restrain the noise interference. The capacitance analysis shows that 1000aF capacitance variation corresponds to 0.1 degree of the turn angle. The differential capacitance bridge and the charge integral amplifier are used as the front signal input interface. In the implementation of digital lock-in amplifier, a new routine which warranted the exactly matching of the reference phase to signal phase was proposed. The result of the experiment shows that digital lock-in amplifier can greatly eliminate the noise in the output signal. The linearity of the turn angle output is 2.3% and the minimum resolution of turn angle is 0.04 degree. The application of the digital lock-in amplifier in the field of micro-electro-mechanical-system (MEMS) device signal detection is a new attempt, and it shows the prospective for a high-performance application.

Using db5 wavelet to decompose first layer of structural response momentum of acceleration in six layers, selecting damage feature of damaged sensitivity based on response single of acceleration, thus recognizing damage time of structure and realizing the surveillance on time of structural damage. Through wavelet package decomposition of acceleration response signal of obtained structure, it can obtain eigen vector of energy in al frequency ranges and construct characteristic parameter input of BP neutral network and then construct one group of simple network output related to the input. The output can not only indicate location of structural damage and degree of structural damage, but also greatly reduce time spent on training and emulation of network. The adopted BP neural network structure is 8-7-7-6, the activation function in hidden layer is Sigmoid and the activation function in output layer is linear function training 405 group sample, realizing identification on damage location and damage degree. Analogue calculation has indicated that using wavelet analysis and BP neural network together can accurately diagnose the time, location and degree of structure, being of some feasibility.

For the purpose to detect the low capacitance signal, which emerged in noise, the virtual instrument based detection scheme was used in the novel prototype of micro gyroscope structure. Software arithmetic and the software generated reference waveform were designed for the demodulator to pick up the variable capacitance. The result of the experiment shows that the software realized demodulator can eliminate the noise in the output signal, and the improved signal to noise ratio reaches 60dB. The minimum resolution of the turn angle is 0.04 degree. The application of the software realized demodulator shows the prospective for a high performance measurement technology in MEMS application