Small sample fault diagnosis method based on task sorting meta learning

A fault diagnosis and meta-learning technology, applied in machine learning, pattern recognition in signals, instruments, etc., can solve problems such as interference and unrecognizable fault information, and achieve the effect of stable performance

Pending Publication Date: 2021-12-24
TIANJIN UNIV
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Problems solved by technology

When the fault sample size is small, different working conditions will cause

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  • Small sample fault diagnosis method based on task sorting meta learning
  • Small sample fault diagnosis method based on task sorting meta learning
  • Small sample fault diagnosis method based on task sorting meta learning

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[0036] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be discussed in detail below in conjunction with the accompanying drawings and examples. The following examples are only descriptive, not restrictive, and cannot limit the protection scope of the present invention with this .

[0037] The present invention proposes a small-sample intelligent fault diagnosis method for an industrial system based on a Task-sequencing Meta Learning (TSML) model. The method can intelligently perform fault diagnosis on an industrial system through a meta-learning strategy.

[0038] Meta-learning is a highly adaptive learning strategy that does not focus on learning itself, but on how to acquire learning ability. With the help of the learning ability, it only needs simple adjustments to adapt to new tasks in real industrial scenarios, instead of training the network from scratch. Specifically, meta-learning does not d...

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Abstract

The invention provides a small sample fault diagnosis method based on task sorting meta-learning, and the method comprises the following steps: S1, collecting fault data from fault equipment, carrying out the working condition-level fine-grained division of the fault data, carrying out the segmentation and Fourier transformation of a vibration signal, obtaining a time-frequency image, and then constructing a fine-grained fault time-frequency image data set; S2, performing clustering in each task of a training task module in the meta-learner to obtain a score of the task, and sorting the fault classification tasks from easy to difficult according to the scores; S3, performing inner and outer two-layer loop training through the learning of meta-knowledge and the learning of each task to obtain initialization parameters; and S4, performing fine tuning by using the initialization parameters and a small number of samples of the test task to obtain the output of fault diagnosis. According to the method, fault tasks can be well found in actual industrial scenes by finding sensitive initialization parameters with strong knowledge adaptability.

Description

technical field [0001] The invention belongs to the field of industrial fault diagnosis and machine learning, and is used for small-sample fault diagnosis in the industrial field, in particular to a small-sample fault diagnosis method based on task sorting meta-learning. Background technique [0002] Modern equipment adopts a state-based maintenance method [1], which collects state information to take maintenance measures. When there is evidence that the equipment has abnormal behavior, it is necessary to try to take maintenance measures to avoid unnecessary maintenance tasks. Diagnostics are an important aspect of this maintenance approach to complete the detection, isolation and identification of faults. Fault diagnosis and health management systems have been widely used in industry and play a key role in the Industrial Internet of Things (IIOT). By analyzing the status data collected in the IIOT environment, when the equipment is abnormal, it tries to take maintenance me...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N20/00
CPCG06N20/00G06F2218/02G06F2218/12G06F18/23213
Inventor 胡译丹刘若楠陈东月张凯胡清华
Owner TIANJIN UNIV
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