Autonomous underwater robot propeller weak fault feature extraction method

An underwater robot, fault feature technology, applied in the direction of instruments, computer parts, character and pattern recognition, etc., can solve problems such as small fault degree and hard fault output loss degree of thrusters

Pending Publication Date: 2022-03-15
HARBIN ENG UNIV
View PDF0 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Many scholars have achieved good research results in AUV thruster fault diagnosis technology, but most of them focus on the hard faults of the propeller and the faults with a large output loss, and seldom study the faults whose output loss is less than 10% of the total output. weak fault

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Autonomous underwater robot propeller weak fault feature extraction method
  • Autonomous underwater robot propeller weak fault feature extraction method
  • Autonomous underwater robot propeller weak fault feature extraction method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0053] figure 1 The flow chart of the AUV fault feature extraction method patented by the present invention. combine figure 1 , the specific implementation steps of the weak fault feature extraction method for autonomous underwater robot thrusters based on multi-source state signals and control signals are as follows:

[0054] Step 1: Initialize the mode number, center frequency and balance parameters of the variational mode decomposition (VMD), and perform VMD to obtain the initial mode set. This step is the same as the conventional MVMD method.

[0055] Step 2: Obtain the improved optimization evaluation function (MTF) and the entropy value of each mode based on negative entropy.

[0056] In this step, the patent uses a negative entropy method to obtain the entropy value of each mode, and obtains an improved optimization evaluat...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides an autonomous underwater robot propeller weak fault feature extraction method, which belongs to the technical field of underwater robot fault diagnosis and comprises two parts: fault feature enhancement and feature fusion. Firstly, Gaussian properties of all modes of a multi-source state signal and a control signal are judged through negentropy, so that parameters are optimized, noise reduction is completed, and fault features are extracted and enhanced based on a modified Bayesian algorithm. Afterwards, the characteristic signals are divided into a plurality of time intervals, faults occurring in all the intervals serve as focal elements, first-time characteristic fusion is conducted on all the signals except for the longitudinal speed, second-time fusion is conducted on the result of the first-time fusion and the characteristic signals of the longitudinal speed, and the fault characteristics are further enhanced; and meanwhile, monotonicity is presented between the fault feature and the fault degree. The method can provide a basis for subsequent fault detection and identification, and is especially suitable for state monitoring of an autonomous underwater robot propeller.

Description

technical field [0001] The patent of the present invention relates to the technical field of fault diagnosis of underwater robots, in particular to a monitoring method suitable for the safety of autonomous underwater robots. Background technique [0002] As the non-renewable resources on land are decreasing day by day, the position of the ocean in the survival and development of human beings is becoming more and more prominent. Autonomous underwater vehicle (AUV: Autonomous Underwater Vehicle), as a marine development carrier and coastal defense equipment, plays an irreplaceable role in the fields of politics, economy, and military. AUV unmanned unmanned work in a complex marine environment, safety is one of the important research contents in the process of AUV research and practical application [1] . The thruster is the most important power component in AUV, and it is also one of the main fault sources of AUV. The research on the fault diagnosis technology of thruster has...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/10G06F2218/04G06F2218/12G06F18/24155G06F18/253
Inventor 张铭钧于大程刘星姚峰
Owner HARBIN ENG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap