Autonomous underwater robot propeller fault feature extraction method

Abstract

The invention relates to an autonomous underwater robot propeller fault feature extraction method based on empirical mode decomposition, fractal dimension and an SHFC positioning algorithm, and belongs to the technical field of underwater robot fault diagnosis. The method comprises the following steps: performing data preprocessing by adopting empirical mode decomposition to replace a common filtering method in fractal dimension; in a high-frequency part after modal decomposition, introducing a rolling time window through extraction of fractal dimension fault features of a small sample in eachtime window, capturing sudden change of a moment fractal dimension feature value from a fault to a fractal dimension, extracting a maximum value of the sudden change of the fractal dimension, and then enhancing an extraction effect of the fault features. Fault characteristics can be enhanced, whether the underwater robot thruster breaks down or not can be conveniently detected, and the method isparticularly suitable for state monitoring of the autonomous underwater robot thruster and wide in application prospect.

Description

technical field [0001] The invention relates to an autonomous underwater robot propeller fault feature extraction method based on empirical mode decomposition, fractal dimension and SHFC positioning algorithm, and belongs to the technical field of underwater robot fault diagnosis. Background technique [0002] Autonomous underwater vehicles (AUV: Autonomous Underwater Vehicle) work unmanned and without cables in complex marine environments. Safety is the prerequisite to ensure the smooth completion of tasks. Fault diagnosis technology is one of the key technologies to ensure the safety of underwater vehicles. [0003] Fault feature extraction is one of the important contents of AUV thruster fault diagnosis research, and its typical methods include modified Bayesian and fractal dimension methods. In order to explore the extraction of fault features from the state quantities of AUVs, this patent adopts the modified Bayesian method and fractal dimension method in the experiment...

AI Technical Summary

Problems solved by technology

In order to explore the extraction of fault features from the state quantities of AUVs, this patent uses the modified Bayesian method and the fractal dimension method in the experimental research on the fault diagnosis of AUV thrusters. The effect is good, and when the failure degree is small (the failure of the thruster output loss is less than 20%), it is easily affected by the noise of the sensor and other noises, which may easily lead to the failure of fault feature extraction

In order to reduce sensor noise interference, the wavelet threshold noise reduction method is used to preprocess the signal. In the experimental research of this method, it is found that the wavelet noise reduction method has a significant effect in reducing random interference when the wavelet coefficient is less than the threshold value, but it is greater than the wavelet coefficient. In the case of a threshold value, the signal corresponding to the wavelet coefficient will be regarded as a useful signal and be retained. The retained part of the signal will affect the difference between the fault feature value and the noise feature value, thereby affecting the extraction effect of the fault feature value.

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