Diagnosis model for industrial equipment fault diagnosis, construction method thereof and application thereof

A technology for industrial equipment and fault diagnosis, applied in data processing applications, instruments, computing, etc., can solve the problems of insufficient denoising of the original vibration signal, weak generalization ability, and insufficient practicability of industrial equipment fault diagnosis, and achieves good results. Noise reduction effect, high diagnostic accuracy, not easy to expand

Inactive Publication Date: 2020-01-17
HUAZHONG UNIV OF SCI & TECH
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

[0006] The invention provides a diagnostic model for fault diagnosis of industrial equipment and its construction method and application, which are used to solve the problem of insufficient denoising of the original vibration signal and the failure of the fault diagnosis model to input

Method used

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  • Diagnosis model for industrial equipment fault diagnosis, construction method thereof and application thereof
  • Diagnosis model for industrial equipment fault diagnosis, construction method thereof and application thereof
  • Diagnosis model for industrial equipment fault diagnosis, construction method thereof and application thereof

Examples

Experimental program
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Effect test

Embodiment 1

[0046] A diagnostic model construction method 100 for industrial equipment fault diagnosis, such as figure 1 shown, including:

[0047] Step 110, using the variational mode decomposition method to decompose each original vibration signal of the training industrial equipment to obtain multiple sub-modal components, and select an optimal sub-modal component from the multiple sub-modal components;

[0048] Step 120, using Bayesian optimized one-dimensional fast non-local mean method to denoise all optimal sub-mode components;

[0049] Step 130: Based on all denoised optimal sub-modal components, use metric learning to change the sample distance metric function in the classifier and train it. The trained classifier is a diagnostic model for industrial equipment fault diagnosis.

[0050] Since the fault features that are effective for the training model are often buried in useless noise such as noise, around the problem of noise reduction, considering that the variational mode dec...

Embodiment 2

[0080] A diagnostic model for fault diagnosis of industrial equipment, which is constructed by adopting any diagnostic model construction method for fault diagnosis of industrial equipment as described in the first embodiment above.

[0081] The relevant technical solutions are the same as those in Embodiment 1, and will not be repeated here.

[0082] The diagnostic model constructed by using any of the above diagnostic model construction methods for industrial equipment fault diagnosis has the characteristics of high accuracy, strong anti-noise performance and wide applicability due to the use of VMD, Bayesian optimized NLM and metric learning.

Embodiment 3

[0084] A method 200 for fault diagnosis of industrial equipment, such as image 3 shown, including:

[0085] Step 210, collecting the original vibration signal of the industrial equipment to be tested;

[0086] Step 220, using the variational mode decomposition method and the one-dimensional fast non-local mean method of Bayesian optimization as described in the first embodiment above, to decompose the original vibration signal to obtain the optimal sub-mode component and denoise;

[0087] Step 230, based on the optimal sub-modal component after denoising, use the diagnostic model described in the second embodiment above to diagnose and obtain the fault type of the industrial equipment to be tested.

[0088] The relevant technical solutions are the same as those in Embodiment 1 and Embodiment 2, and will not be repeated here.

[0089] Focusing on the problem of noise reduction, considering that VMD can decompose the signal into multiple intrinsic mode functions with limited ...

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Abstract

The invention discloses a diagnosis model for industrial equipment fault diagnosis, a construction method thereof and an application thereof. The construction method comprises the steps: employing a variational mode decomposition method to decompose and train each original vibration signal of industrial equipment, obtaining a plurality of sub-modal components, and selecting an optimal sub-modal component from the plurality of sub-modal components; adopting a Bayesian optimization one-dimensional fast non-local mean method to denoise all the optimal sub-modal components; and based on all the denoised optimal sub-modal components, adopting metric learning to change a sample distance metric function in the classifier and training to obtain a diagnosis model of industrial equipment fault diagnosis. According to the construction method, variational mode decomposition is adopted to separate fault features from original vibration signals; and a non-local mean denoising algorithm with high denoising performance is further introduced, and Bayesian optimization is carried out on the parameters, so that a good denoising effect on the vibration signal with the high signal-to-noise ratio is realized; and finally, metric learning is applied to training of the classifier. The construction method is high in diagnosis accuracy and wide in application range.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of industrial equipment, and more particularly relates to a diagnostic model for fault diagnosis of industrial equipment and its construction method and application. Background technique [0002] As an important component of rotating machinery and equipment, rolling bearings are widely used in various industrial equipment. Bearings often work in a high-load and high-speed environment, and their life shows obvious discreteness and unpredictability. More than 90% of the bearings' actual life is less than the design life. Once a bearing fails, it can easily lead to equipment downtime and even pose a threat to personal safety. It is of great significance both in theory and in reality to study a set of effective methods to realize the fault diagnosis of bearings. [0003] The vibration signal acquisition of rolling bearings is relatively simple and contains rich information. Therefore, the analysis bas...

Claims

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Application Information

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IPC IPC(8): G06Q10/00G06Q10/06G06Q50/06
CPCG06Q10/067G06Q10/20G06Q50/06
Inventor 肖江文陆子鸣王燕舞黄正义
Owner HUAZHONG UNIV OF SCI & TECH
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