A Fault Diagnosis Method with Noisy Labels Based on Recurrent Neural Networks

A cyclic neural network, fault diagnosis technology, applied in program control, instruments, testing/monitoring control systems, etc., can solve problems such as low diagnostic accuracy

Active Publication Date: 2020-12-04
TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0003] In order to solve the problem of low diagnostic accuracy of existing rotating machinery fault diagnosis methods, the present invention provides a fault diagnosis method with noise labels based on cyclic neural network

Method used

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  • A Fault Diagnosis Method with Noisy Labels Based on Recurrent Neural Networks
  • A Fault Diagnosis Method with Noisy Labels Based on Recurrent Neural Networks
  • A Fault Diagnosis Method with Noisy Labels Based on Recurrent Neural Networks

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Embodiment Construction

[0055] A fault diagnosis method with noise labels based on recurrent neural network, the method is realized by the following steps:

[0056] Step 1: Use the acceleration sensor to collect the vibration signals of the core components of the rotating machinery under different types of fault conditions, and normalize the vibration signals in sections to form a data set; the data set is expressed as:

[0057]

[0058]

[0059] the y m ∈{1,2,...,C};

[0060] In the formula: M represents the number of data samples in the data set; x m Indicates the mth data sample in the data set; y m Indicates the corresponding label of the mth data sample in the data set; Represents N-dimensional time series; {1,2,…,C} represents the type of fault;

[0061] Step 2: Generate the noise label of the data sample according to the corresponding label of the data sample; the noise label of the data sample is expressed as:

[0062]

[0063] In the formula: Indicates the noise label of the m...

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Abstract

The invention relates to a rotary machine fault diagnosis method, in particular to a fault diagnosis method with a noise label based on a recurrent neural network. According to the invention, the problem of low diagnosis accuracy of the existing rotary machine fault diagnosis method is solved. The fault diagnosis method with a noise label based on a recurrent neural network comprises the followingsteps: 1, carrying out segmentation normalization of vibration signals, and forming a data set; 2, adding a noise label of a data sample into the data set to form a noise label data set; 3, adjustingthe parameters of the whole network by using an optimization algorithm to minimize an adaptive correction loss function, thereby completing the training of the recurrent neural network; and 4, inputting a test data set into the trained recurrent neural network, and outputting a fault diagnosis result. The method is suitable for fault diagnosis of a rotary machine.

Description

technical field [0001] The invention relates to a fault diagnosis method for rotating machinery, in particular to a fault diagnosis method with noise labels based on a cyclic neural network. Background technique [0002] Rotating machinery is the core component of mechanical equipment, and its operating status is closely related to the safety and reliability of mechanical equipment. Due to the harsh working environment, rather complex structure and highly automated operation, rotating machinery is prone to various failures. Any failure that cannot be detected may quickly trigger a chain reaction, damage other components, generate huge economic losses, and even threaten personal safety. Therefore, there is a great need to develop and improve fault diagnosis methods for rotating machinery. However, the practice shows that the existing rotating machinery fault diagnosis methods have the problem of low diagnosis accuracy because they ignore a large number of noise labels in th...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B23/02
CPCG05B23/0262G05B2219/24065
Inventor 谢刚聂晓音赵文晶胡啸王银
Owner TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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