Fault diagnosis method for rotor system based on principal component analysis and broad learning

A principal component analysis, system failure technology, applied in character and pattern recognition, mechanical component testing, machine/structural component testing, etc. question

Active Publication Date: 2019-09-17
CIVIL AVIATION UNIV OF CHINA
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Problems solved by technology

However, after the feature extraction of the fault signal by the above method, the formed feature matrix has problems such as complex structure, high feature cor

Method used

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  • Fault diagnosis method for rotor system based on principal component analysis and broad learning
  • Fault diagnosis method for rotor system based on principal component analysis and broad learning
  • Fault diagnosis method for rotor system based on principal component analysis and broad learning

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Embodiment Construction

[0082] The present invention provides a rotor system fault diagnosis method based on principal component analysis and width learning, combined below figure 1 The schematic flow chart of the present invention is described.

[0083] Such as figure 1 Shown, technical scheme of the present invention is:

[0084] A rotor system fault diagnosis method based on principal component analysis and width learning, comprising the following steps:

[0085] Step 1: Collect fault data T(n) in time domain;

[0086] Step 2: Carry out Fourier transform according to formula (1), transform the collected fault data T(n) in time domain into fault data X in frequency domain,

[0087]

[0088] in,

[0089]

[0090] In the above formula (1) and formula (2), n=0,1,...,N-1, k=0,1,...,N-1, N is the length of time domain fault data, j is a complex symbol, X is the fault data in the frequency domain, including training samples and test samples, X={x 1 , x 2 ,...,x i ,...x m}, i=1,...,m, T(n) ...

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Abstract

The invention discloses a fault diagnosis method for a rotor system based on principal component analysis and broad learning. Principal component analysis is used for carrying out dimension reduction on a feature matrix formed after feature extraction, the linear correlation between the data is reduced, redundant attributes are eliminated, and a low-dimensional matrix capable of retaining the essential features is obtained; and then the matrix is input into a broad learning system for fault identification, and the fault classification task of the rotor system is completed. According to the method in the invention, the principal component analysis and the broad learning system are introduced into the fault diagnosis and identification of the rotor system, through the method, the fault classification complexity can be effectively reduced, the data modeling time can be greatly shortened, the fault identification efficiency of the rotor system can be improved, therefore, the fault diagnosis task of the rotor system can be efficiently completed, the practicability is good, and the method is worthy of popularization.

Description

technical field [0001] The invention belongs to the technical field of fault detection of mechanical parts, and in particular relates to a fault diagnosis method of a rotor system based on principal component analysis and width learning. Background technique [0002] As the core component of rotating machinery, the rotor system plays an irreplaceable role in various related fields. The rotor system is widely used in rotating machinery. The failure of the rotating machinery in the working process will cause major economic losses, a large part of which is caused by the failure of the rotor system. The failure hazards include noise, rotor instability, serious It may even damage the mechanical structure and cause major safety accidents. Therefore, it has very important scientific significance and application value to effectively analyze and accurately diagnose the initial faults of the rotor system. [0003] At present, the widely used methods for rotor fault diagnosis are win...

Claims

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Application Information

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IPC IPC(8): G01M13/02G01M13/028G06K9/62
CPCG01M13/028G01M13/02G06F18/24
Inventor 邓武赵慧敏徐俊洁
Owner CIVIL AVIATION UNIV OF CHINA
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