Fault diagnosis method for underwater robot thrusters based on wavelet modified Bayesian convolution energy

Abstract

The invention discloses a fault diagnosis method for underwater robot thrusters based on wavelet modified Bayesian convolution energy, which reduces the influence of random noise interference in signals based on wavelet decomposition, and enhances the singular behavior amplitude of dynamic signals based on modified Bayesian algorithm. The volatility of the singular behavior of the dynamic signal is reduced based on the convolution calculation, and the position of the minimum value in the dynamic signal is used as the boundary of the energy region to extract the energy fault features, and the fault features and fault classification are coordinated based on the homomorphic transformation, and finally based on the support The vector field description algorithm realizes the fault degree classification of thrusters. The fault degree classification accuracy of the patented method is less affected by the modified Bayesian step size, and the classification accuracy is 100%.

Description

technical field [0001] The invention belongs to underwater robot fault diagnosis technology, in particular to an underwater robot propeller fault diagnosis method based on wavelet modified Bayesian convolution energy. Background technique [0002] The underwater robot moves under the thrust of the propeller. When the propeller fails, dynamic signals such as the speed signal of the underwater robot and the rate of change signal of the propeller control voltage usually produce singular behaviors, and the greater the degree of propeller failure, the greater the energy contained in the singular behavior. Based on this, energy fault features can be extracted for thruster fault diagnosis. [0003] The known signal amplitude square sum method is a commonly used energy feature extraction method. In this known method, the signal amplitudes in the singular behavior area are squared and then summed. In the process of extracting energy fault features using known methods, the following...

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Problems solved by technology

In the process of extracting energy fault features using known methods, the following problems are encountered: the amplitude of the singular behavior of the dynamic signal of the underwater robot is relatively weak and needs to be enhanced; the random noise interference in the signal affects the size of the fault energy feature; the dynamic signal singular behavior fluctuates strong, affecting the boundary identification of the fault energy area

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