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Autonomous underwater vehicle propeller fault detecting method based on wavelet single branch reconstruction

An underwater robot, single-branch reconstruction technology, applied in instruments, special data processing applications, electrical digital data processing, etc., can solve the problems of AUV measurement noise impact, difficult to accurately detect the sequence position when the thruster failure occurs, etc. achieve the effect of improving accuracy

Inactive Publication Date: 2015-06-03
HARBIN ENG UNIV
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Problems solved by technology

[0005] Therefore, combining the discrete multi-layer wavelet decomposition method with the wavelet Shannon entropy method constitutes a new type of AUV thruster fault detection method, which can effectively solve the problem that the AUV is affected by external interference and measurement noise. The traditional multi-layer wavelet decomposition method is difficult to accurately Problems detecting sequence position when thruster failure occurs

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  • Autonomous underwater vehicle propeller fault detecting method based on wavelet single branch reconstruction
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  • Autonomous underwater vehicle propeller fault detecting method based on wavelet single branch reconstruction

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[0023] The attached drawings are attached below.

[0024] to combine figure 1 The specific implementation steps of the fault detection method of the present invention are as follows:

[0025] 1. Sliding window processing signal: when the control signal data with a data length of L=300 is collected, the detection algorithm is started, and when new data is collected again, the first data of the original array is discarded and the newly collected data is placed in the original At the end of the array, always keep the data length as L=300, and the original data of the AUV controller signal is as follows figure 2 shown.

[0026] 2. Discrete multi-layer wavelet decomposition: use the discrete multi-layer wavelet decomposition method to decompose the autonomous underwater vehicle control signal data, and perform W=4-level wavelet analysis on the autonomous underwater robot sensor and controller signals intercepted in step (1). Decomposition, the wavelet basis function is "db4", a...

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Abstract

The invention provides an autonomous underwater vehicle propeller fault detecting method based on wavelet single branch reconstruction. The method includes: performing multilayer discrete wavelet decomposition on an autonomous underwater vehicle control signal to obtain the fault description, at multiple frequency bands, of a propeller fault; respectively calculating to obtain the wavelet entropy of multi-frequency-band fault information and the optimal wavelet single branch reconstruction dimension; performing wavelet single branch reconstruction by using the obtained optimal wavelet single branch reconstruction dimension, wherein the time sequence position corresponding o the model maximum value point of the reconstructed signal is the fault occurrence moment of a propeller, and the autonomous underwater vehicle propeller fault detecting result is obtained. By the method applicable to the fields such as autonomous underwater vehicle propeller fault detecting, the problems that fault signal features are easily submerged by external interference signals after wavelet decomposition due to the influence of external interference, and fault detecting accuracy is low are solved, and the accuracy of the autonomous underwater vehicle propeller fault detecting is increased.

Description

technical field [0001] The invention relates to a fault diagnosis method for a robot, in particular to a fault detection method for an autonomous underwater robot propeller. Background technique [0002] With the dwindling land resources, the pace of human development of the ocean is getting faster and faster. Autonomous underwater vehicle (AUV: Autonomous Underwater Vehicle) is currently the only carrier that can detect and develop in the deep sea without human beings, and has been highly valued by researchers at home and abroad. Propeller is the most important executive part of AUV and has the heaviest load. Once it fails, it will directly affect the safety of AUV. Accurate fault detection is of great significance and practical value for improving the safety of AUV. [0003] Wavelet transform is a time-frequency analysis tool developed rapidly in recent years. It overcomes the defect that Fourier transform can only express the frequency characteristics of the signal but c...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 张铭钧刘维新刘星殷宝吉王玉甲赵文德姚峰
Owner HARBIN ENG UNIV
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