**7. Conclusion**

630 Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

Gyroscope original signal

0 5000 10000 15000

0 5000 10000 15000

0 5000 10000 15000

Sampling time(t=10ms)

According to the simulation results, db4, db6 and bior5.5 may be good choice for wavelet functions, because the curve of these de-noised signal are smoother than the others. But bior5.5 have lager computation cost, it is not suitable for real time computation. In Figure 13, the de-noised signals of db4, db6 are compared with the original signal. The denoising result, got from db6 wavelet function, is smoother than the result of db4. And the de-noised

Periodogram Power Spectral Density Estimate

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Normalized Frequency ( rad/sample)

Fig. 14. The periodogram power spectral density estimate of original signal

signal of db6 is closer to the real angular moment of RUAV than the original signal.

Sampling time(t=10ms)

Denoising results using db6 wavelet

Sampling time(t=10ms)

Denoising results using db4 wavelet








Power/frequency (dB/rad/sample)




0

Fig. 13. Contrast of denoised signal and original signal

deg/s

deg/s

deg/s

In this chapter, wavelet-based algorithm is applied to fault diagnosis and gyroscope noise reduction. Its advantage is that it does not require a prior model of a sensor. The proposed wavelet-based algorithm for fault detection of the RUAV sensor system gives us a multiscale analysis approach to identify the feature of flight data failures, which are not readily identified by traditional approaches. The results presented in this chapter have shown that

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the method based on wavelet transform is a promising alternative to other approaches to the fault detection system for RUAV system. With the wavelet-based scheme, the RUAV sensor fault detection system can detect the failure locations of abrupt signal effectively. In order to overcome the drawbacks of the low-pass filter, the thresholding denoising method base on wavelet transformation is used to reduce the short-term measured noise of the MEMS gyroscope. The article compared different wavelet functions and level of decompositions, and found the effective filter parameters. Using db6 wavelet function at level 5, the denoised signal is suitable for integrated navigation system and flight control system. This will improve the calculation precision of angle rotation matrix, and the high frequency noise will be decrease.

In the Further, further flight tests are needed to verify the actual performance of waveletbased denoising method and wavelet-based fault detection in ServoHeli-40's integrated navigation system.
