Morphological component bearing failure diagnosis method based on dictionary study

A technology of morphological components and dictionary learning, applied in mechanical bearing testing, measuring devices, testing of mechanical components, etc., can solve problems such as difficult to extract fault features, can not best match the complex signal structure characteristics of the analysis, and achieve easy implementation, simple effect

Inactive Publication Date: 2017-02-22
SHIJIAZHUANG TIEDAO UNIV
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  • Summary
  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

However, since each fixed dictionary has a definite mathematical model, such as Dirac dictionary, Fourier dictionary, wavelet dictionary and wavelet packet dictionary,

Method used

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  • Morphological component bearing failure diagnosis method based on dictionary study
  • Morphological component bearing failure diagnosis method based on dictionary study
  • Morphological component bearing failure diagnosis method based on dictionary study

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Embodiment 1

[0059] Such as figure 1 As shown, the embodiment of the present invention discloses a morphological component bearing fault diagnosis method based on dictionary learning. The method includes the following steps:

[0060] S101: Take the fault signals of the inner ring and outer ring of the bearing as training samples, apply the dictionary learning algorithm to learn the training samples, seek the optimal dictionary space, and obtain a dictionary that can sparsely represent the inner ring and outer ring of the bearing;

[0061] S102: Replace the fixed dictionary in the morphological component analysis method (MCA) with the learned dictionary. According to the morphological differences of the components contained in the signal, use the morphological component analysis method to analyze the inner ring fault characteristics and outer ring faults in the rolling bearing fault signal The characteristics and noise components are separated to obtain the impact component of the inner ring and...

Embodiment 2

[0063] Such as figure 2 As shown, the embodiment of the present invention discloses a morphological component bearing fault diagnosis method based on dictionary learning. The method includes the following steps:

[0064] S201: Take the fault signals of the inner ring and outer ring of the bearing as training samples, apply K-singular value decomposition (K-SVD) dictionary learning algorithm to adaptively seek the optimal dictionary space according to the time-domain characteristics of the training samples, and pass K- The SVD method learns the sample signals and obtains a dictionary that can sparsely represent the bearing inner ring and the bearing outer ring.

[0065] The specific process of step S201 includes the following steps:

[0066] S2011: Dictionary initialization. The initial dictionary uses part of the original data and sets the dictionary matrix D (0) ∈R n×K , And use l 2 The norm is standardized for each column of the dictionary.

[0067] S2012: Sparse coding. Accordin...

Embodiment 3

[0091] Such as image 3 As shown, the embodiment of the present invention discloses a morphological component bearing fault diagnosis method based on dictionary learning. The method includes the following steps:

[0092] S301: A piezoelectric acceleration sensor is used to collect bearing vibration signals, and the obtained signals include signals indicating that the outer ring and inner ring of the rolling bearing are faulty.

[0093] S302: Taking the fault signals of the inner ring and outer ring of the bearing as training samples respectively, applying the K-singular value decomposition (K-SVD) dictionary learning algorithm to adaptively seek the optimal dictionary space according to the time-domain characteristics of the training samples, and pass K- The SVD method learns the sample signals to obtain a dictionary that can sparsely represent the bearing inner ring and the bearing outer ring.

[0094] The specific process of step S302 includes the following steps:

[0095] S3021: Di...

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Abstract

The invention discloses a morphological component bearing failure diagnosis method based on dictionary study, and relates to the technical field of bearing failure diagnosis methods. The method comprises the steps that failure signals of an inner ring and an outer ring of a bearing are adopted as training samples, study is conducted on the training samples by applying a dictionary study algorithm, and a best dictionary space is sought; the studied dictionary replaces a fixed dictionary in a morphological component analysis method, and according to morphological difference contained of each component in the signals, separation is conducted on inner ring failure characteristics, outer ring failure characteristics and noise components in rolling bearing failure signals by utilizing the morphological component analysis method; spectral analysis is conducted on the failure characteristic components after envelope to diagnose the failure and the position of the bearing. The morphological component bearing failure diagnosis method based on the dictionary study can effectively extract failure characteristics of the outer ring and the inner ring of the rolling bearing under a strong noise environment, the principle of the method is simple, and the algorithm is easy to achieve.

Description

Technical field [0001] The invention relates to the technical field of bearing fault diagnosis methods, in particular to a morphological component bearing fault diagnosis method based on dictionary learning. Background technique [0002] One of the mainstream of modern machinery is power machinery and transmission machinery called "transmission system". The most critical and important component of the transmission system is the bearing. According to statistics, 70% of mechanical failures are vibration failures, and 30% of vibration failures are caused by rolling bearings. Therefore, the running state of the rolling bearing must be judged in time and corresponding countermeasures must be taken to prevent accidents. Therefore, the fault diagnosis technology of rolling bearings is an important part of the fault diagnosis technology of mechanical equipment. The analysis and research of the fault diagnosis technology of rolling bearings has great scientific and practical significanc...

Claims

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Application Information

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IPC IPC(8): G01M13/04
CPCG01M13/045
Inventor 郝如江吴洋李非李辉沈英明
Owner SHIJIAZHUANG TIEDAO UNIV
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