Depth-learning-theory-based fault diagnosis method for rotating machinery

A technology for rotating machinery equipment and fault diagnosis, applied in neural learning methods, biological neural network models, etc., can solve the problem of not overcoming frequency ambiguity, not using temperature information in time domain, and not providing deep neural network self-learning and self-improvement. and other problems to achieve the effect of eliminating non-stationarity, accurate diagnosis and accurate diagnosis.

Active Publication Date: 2017-02-15
北京昊鹏智能技术有限公司
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Problems solved by technology

However, this method only uses the frequency domain characteristics of the vibration signal, and does not overcome the frequency ambiguity caused by vibration non-stationarity, nor does it use the time domain information of the vibration signal and the temperature information of the monitored object that is highly correlated with the fault. The effect of the non-uniform speed scene is not ideal, and the severity of the fault cannot be judged
In addition, this method does not provide a method for self-learning and self-improvement of the constructed deep neural network.

Method used

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  • Depth-learning-theory-based fault diagnosis method for rotating machinery
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  • Depth-learning-theory-based fault diagnosis method for rotating machinery

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Embodiment Construction

[0025] The specific implementation of the present invention will be described below by taking the bearing fault diagnosis of a certain type of wind turbine as an example in conjunction with the accompanying drawings.

[0026] 1. Construct a deep neural network DNN, including the type of deep neural network DNN, the number of layers, and the number of nodes in each layer;

[0027] The type of deep neural network described in further step 1 includes: autoencoder AutoEncoder, denoising autoencoder Denoising Autoencode, sparse coding Sparse coding, limit Boltzmann machine RestrictedBoltzmann Machine (RBM), deep belief network Deep Belief Networks , Convolutional Neural Networks;

[0028] According to the characteristics of vibration signals of mechanical faults, this embodiment selects DenoisingAutoencode with a 7-layer denoising automatic encoding machine, and the number of nodes in each layer is 2,097,170.

[0029] Because the environment of mechanical equipment is complex, the...

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Abstract

The invention relates to a depth-learning-theory-based fault diagnosis method for rotating machinery. An equal-angle interval sampling time sequence signal is constructed by using an equal-interval sampling sequence signal, thereby eliminating a non-stationary property caused by non-constant-speed rotation; training and fault diagnosis of a deep neural network are carried out by using an autocorrelation sequence and Fourier transform of the equal-angle interval sampling time sequence signal and a temperature during working of the diagnosed rotating machinery as inputs of the deep neural network; newly generated data and corresponding fault states are added into a training sample set; and the deep neural network is trained again by using the new sample set. Therefore, self learning and self perfection of the deep neural network (DNN) are realized.

Description

technical field [0001] The invention relates to fault diagnosis of rotating mechanical equipment, in particular to a fault diagnosis method for rotating mechanical equipment based on deep learning theory. Background technique [0002] With the development of modern industry and science and technology and the further improvement of the degree of automation, mechanical equipment is developing in the direction of large-scale, high-speed, continuous, centralized, and automated. Rotating mechanical equipment has been in high-speed operation for a long time (generally more than 3000 revolutions per minute or even as high as tens of thousands of revolutions). Due to the influence of various factors, some failures will inevitably occur, and these failures often cause huge economic losses or even catastrophic consequences. , so it is very important for fault diagnosis of rotating machinery equipment. Vibration detection and diagnosis method is the most commonly used diagnosis method...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08
CPCG06N3/08
Inventor 李雅婧
Owner 北京昊鹏智能技术有限公司
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