Rotary machine intelligent fault diagnosis method based on integrated depth auto-encoder

A technology of deep self-encoding and rotating machinery, which is applied to the automatic fault diagnosis of rotating machinery. Based on the field of intelligent fault diagnosis of rotating machinery based on integrated deep self-encoder, it can solve the problems of classification accuracy, low generalization ability and low robustness. And other issues

Active Publication Date: 2019-10-15
XIDIAN UNIV
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However, this method only considers the overall performance of the deep autoencoders. The deep autoencoders are directly combined using an integration strategy without selection, and the difference in the classification accuracy of each deep autoe

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  • Rotary machine intelligent fault diagnosis method based on integrated depth auto-encoder
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  • Rotary machine intelligent fault diagnosis method based on integrated depth auto-encoder

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[0045] The following describes the present invention in further detail with reference to the drawings and specific embodiments:

[0046] Reference figure 1 , The present invention includes the following steps:

[0047] Step 1) Get the training data set X 1 And test data set X 2 .

[0048] The invention can be used for the intelligent fault diagnosis of rolling bearings, gearboxes and other rotating machinery. In this embodiment, rolling bearings are taken as an example, and the bearing fault data of Case Western Reserve University is used for experimental analysis. A total of 12 fault types of rolling bearings are collected through the data acquisition system. , 3600 vibration time domain signals as a data set. details as follows:

[0049] The vibration time domain signals used in this embodiment all come from the bearing data set of Case Western Reserve University in the United States. The test bearing mainly includes four types of failures: normal state, ball defect (BD), outer r...

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Abstract

The invention provides a rotary machine fault diagnosis method based on a depth automatic encoder, and aims to improve the fault diagnosis precision of a rotary machine, and the method comprises the steps: firstly collecting a vibration acceleration time domain signal of the rotary machine, and obtaining a training data set and a test data set; secondly, for each activation function, training a series of depth auto-encoders through different training sets by using a K-fold cross validation method; secondly, verifying the trained depth auto-encoder through a verification set, and obtaining theprecision of each fault label; thirdly, searching an optimal selection parameter by adopting a grid search method, screening the depth auto-encoder through the optimal selection parameter, and constructing an integrated depth auto-encoder model; and finally, obtaining a prediction label of the input sample, and mapping the prediction label back to the fault type of the rotating machine to realizefault diagnosis of the rotating machine.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis and signal processing and analysis, and relates to a fault diagnosis method for rotating machinery, in particular to an intelligent fault diagnosis method for rotating machinery based on an integrated depth autoencoder, which can be used for faults of rotating machinery such as rolling bearings and gearboxes Automatic diagnosis. Background technique [0002] Rotating machinery is the most widely used mechanical equipment in the industrial field and is of great significance to social and economic development. The key components of rotating machinery will inevitably experience various failures under harsh working conditions such as heavy load, heavy impact, high speed, and large background noise. These failures can cause huge losses and serious casualties. In order to monitor the operating status of rotating machinery, improve the safety and reliability of rotating machinery, and avoid une...

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Application Information

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IPC IPC(8): G06K9/62G01M99/00
CPCG01M99/00G06F18/2413G06F18/24G06F18/214
Inventor 王奇斌孔宪光马洪波吴晓东杨文徐锟
Owner XIDIAN UNIV
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