Fault Detection for Wall-Climbing Robot Using Complex Wavelet Packets Transform and Fractal Theory
Xiaochuan Zhang · ACTA PHOTONICA SINICA · 2007
A novel fault detection method for Wall-climbing robot is presented based on complex wavelet packets analysis and fractal theory. It employs complex wavelet packets transform to obtain the real and imaginary parts complex wavelet coefficients of the Wall-climbing robot sensor output signals. The high frequency noise in the output signals is excluded and the characteristic frequency of fault signal is abstracted via shrinking and reconstructing the complex wavelet coefficients by using hard-thresholding method. Furthermore, The multi-scale spectrum correlation dimensions of the fault signals are computed out by using fractal theory. It employs the dimension furthest distance method to define the fault sensitive dimension of system state at a series of fixed dimension, so the nonstationary characteristic of the noise fault signals is picked up when the some faults happen. The simulation experiment shows that the characteristic space of the noise fault signals is accord with the fault state space of Wall-Climbing Robot well. The results also show that the correlation dimension of fault signal is bigger than that of normal signal. This conclusion is a good quantitative evidence to judge whether the fault occurs or not.