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Rolling bearing fault diagnosis method based on parallel feature learning and multiple classifiers

A rolling bearing and multi-classifier technology, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problems of not being able to filter out deep features, affecting the accuracy of model classification and diagnosis, and evaluating deep features.

Active Publication Date: 2019-08-09
XIDIAN UNIV
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Problems solved by technology

However, although this method can extract deep features by integrating the deep autoencoder model in the feature extraction and selection stage, it does not effectively evaluate the extracted deep features, and cannot screen out the most relevant and more representative features that are highly relevant to the diagnostic target. The deep features of the model affect the classification and diagnosis accuracy of the model; in addition, this method only uses a softmax classifier for diagnosis in the fault classification and diagnosis stage, which has poor robustness and leads to low diagnostic accuracy of the model under random interference

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  • Rolling bearing fault diagnosis method based on parallel feature learning and multiple classifiers
  • Rolling bearing fault diagnosis method based on parallel feature learning and multiple classifiers
  • Rolling bearing fault diagnosis method based on parallel feature learning and multiple classifiers

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[0041] The following describes the present invention in further detail with reference to the drawings and specific embodiments:

[0042] Reference figure 1 , The present invention includes the following steps:

[0043] Step 1) Obtain training sample set and test sample set

[0044] A total of 12 fault types and 3,600 vibration time-domain signals of rolling bearings are collected through the data collection system as a data set, 2400 of which are used as the training set, and the remaining 1,200 data are used as the test set, as follows:

[0045] The vibration time domain signals used in this embodiment all come from the bearing vibration time domain signals collected by the bearing accelerated life test bench PRONOSTIA. The platform consists of three parts: drive module, load module and data acquisition module. The main function of the test device is to provide signals of different types of faults. The main components of the test device include a drive motor, a torque sensor and a ...

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Abstract

The invention provides a rolling bearing intelligent fault diagnosis method based on multi-classifier integration and parallel feature learning, and aims to improve the classification precision of a model, and the method comprises the following implementation steps: obtaining a training sample set and a test sample set; establishing a plurality of stacked auto-encoder models, carrying out paralleltraining on the stacked auto-encoder models by taking the training sample set as an input, and extracting a plurality of characteristics of the training sample set; performing feature evaluation on the extracted features based on a softmax model, and screening the features according to a corresponding threshold value and an evaluation index value to form a feature subset; establishing a pluralityof classifiers based on a softmax model according to the feature subset, obtaining the classification precision of each classifier by taking the feature subset as input, reselecting a plurality of classifiers according to a threshold value to construct an integrated multi-classifier model, obtaining an integrated multi-classifier model prediction label through a majority voting method, and mapping the prediction label and the fault type of the rolling bearing to realize the intelligent fault diagnosis of the rolling bearing.

Description

Technical field [0001] The invention belongs to the technical field of intelligent fault diagnosis of rotating machinery, and relates to a rolling bearing fault diagnosis method, in particular to a rolling bearing fault intelligent diagnosis method based on parallel feature learning and integrated multi-classifier, which can be used for automatic fault diagnosis of rotating machinery such as rolling bearings. Background technique [0002] Rotating machinery plays an important role in industrial equipment. Rolling bearings are one of the most important parts in rotating machinery such as motors, wind turbines and gearboxes. It consists of rolling elements, outer rings, inner rings and cages. Rolling bearings usually work under complex working conditions, such as different working conditions, vibration, temperature, load, etc. These factors often lead to the degradation of rolling bearing performance or even failure. The performance status of the rolling bearing directly affects t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24G06F18/214
Inventor 王奇斌赵博程广凯孔宪光马洪波常建涛
Owner XIDIAN UNIV
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