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Rotating-machinery life-stage identification method based on deep self-encoding learning network of noise enhanced samples

A technology for learning networks and rotating machinery, applied in neural learning methods, biological neural network models, special data processing applications, etc.

Inactive Publication Date: 2017-08-04
CHONGQING JIAOTONG UNIVERSITY
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

At the same time, it is not easy to obtain samples in the life stage of rotating machinery, and often only limited samples can be obtained. For limited sample

Method used

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  • Rotating-machinery life-stage identification method based on deep self-encoding learning network of noise enhanced samples
  • Rotating-machinery life-stage identification method based on deep self-encoding learning network of noise enhanced samples
  • Rotating-machinery life-stage identification method based on deep self-encoding learning network of noise enhanced samples

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Experimental program
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Embodiment

[0116] In this embodiment, the validity of the present invention is verified by the following steps:

[0117] The first step: collect data, add noise sample enhancement. Obtained a certain type of rolling bearing full-life experimental sample, the bearing was continuously operated for 90 days under the conditions of a rotating speed of 3000rpm and a load of 3kg, and vibration signals were collected every 4 hours, and two groups of signals were collected in sequence each time, and the sampling frequency was 51.2kHz. The sample length is 100k. After obtaining the amplitude spectrum of the first group of vibration signals collected each time, the graphs are drawn sequentially according to the sampling time to obtain the spectrogram as shown in figure 2 shown. Observing the figure, with the increase of the bearing running time (number of running circles), the bearing gradually wears out, and the spectrum energy and spectrum structure of the vibration signal have changed. The ge...

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Abstract

The invention relates to a rotating-machinery life-stage identification method based on a deep self-encoding learning network of noise enhancement samples. For the purpose that extraction and expression of rotating-machinery life features as well as life stage identification are automatically learned under the condition of a small sample size, noise enhancement are conducted on training samples; after a plurality of sparse self codes are stacked, classification layers are added to construct the deep sparse self-encoding learning network which can not only automatically learn extraction of the life features, but also intelligently identify the life stages. Stepwise non-supervision adaptive learning and supervision fine tuning are conducted on the the samples obtained after noise enhancement through multi-layer sparse self encoding, so as to inhibit deep-network over fitting and improve network robustness. Therefore, automatic extraction and expression of the rotating-machinery life features are achieved, and finally intelligent identification of the rotating-machinery life stages in the classification layers are completed. The rotating-machinery life-stage identification method can be applied in identifying rolling bearing life stages, and identifying results are good under the condition of a small sample size.

Description

technical field [0001] The invention belongs to the technical field of status monitoring and life assessment of rotating machinery, and relates to a method for identifying life stages of rotating machinery based on a noise-added sample enhanced deep self-encoding learning network. Background technique [0002] Rotating machinery is an important equipment in production and life, and its life and reliability are directly related to the operating performance and reliability of the production system. With the development of economy, society and science and technology, higher and higher requirements are put forward for the life of rotating machinery. It is necessary to carry out research on high reliability and long life of rotating machinery, formulate reasonable maintenance strategies for rotating machinery, and reduce the The prerequisite for production stoppage or even production accident is to accurately evaluate and identify the life stages of rotating machinery. [0003] ...

Claims

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Application Information

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IPC IPC(8): G06F19/00G06N3/04G06N3/08
CPCG06N3/084G06N3/088G16Z99/00G06N3/045
Inventor 陈仁祥吴昊年杨黎霞陈志毅李军向阳黄鑫
Owner CHONGQING JIAOTONG UNIVERSITY
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