LightGBM fault diagnosis method based on improved Bayesian optimization

A fault diagnosis and fault diagnosis model technology, applied in the direction of instruments, error detection/correction, calculation, etc., can solve the problems of the influence of fault diagnosis accuracy and fault diagnosis model accuracy, and achieve high fault prediction efficiency and accuracy, The effect of good performance and low computational complexity

Active Publication Date: 2019-11-05
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0003] In view of the above-mentioned problems existing in the prior art, in order to solve the problem that hyperparameter uncertainty affects the accuracy of fault diagnosis, the purpose of the present invention is to provide an improved ...

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  • LightGBM fault diagnosis method based on improved Bayesian optimization
  • LightGBM fault diagnosis method based on improved Bayesian optimization
  • LightGBM fault diagnosis method based on improved Bayesian optimization

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Embodiment Construction

[0041] The present invention will be further described below in conjunction with accompanying drawing of description, but protection scope of the present invention is not limited thereto:

[0042] Improved Bayesian optimized LightGBM fault diagnosis method, including the following steps:

[0043] 1) Determine the hyperparameters and hyperparameter value ranges that the LightGBM model needs to optimize; step 1) determines the hyperparameters and hyperparameter value ranges that the LightGBM model needs to optimize, in the following manner:

[0044] The hyperparameter max_depth sets the value range to the interval [1,11];

[0045] The hyperparameter learning_rate sets the value range to the interval [0.1,0.9];

[0046] The hyperparameter colsample_bytree sets the value range to the interval [0.1,0.9];

[0047] The hyperparameter subsample sets the value range to the interval [0.1,0.9];

[0048]The hyperparameter max_bin sets the value range to the interval [25,150].

[0049]...

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Abstract

The invention discloses a LightGBM fault diagnosis method based on improved Bayesian optimization. The LightGBM fault diagnosis method comprises the following steps: 1) determining hyper-parameters needing to be optimized by a LightGBM model and a hyper-parameter value range; 2) improving the Bayesian optimization algorithm to obtain an improved Bayesian optimization algorithm GP-ProbHedge; 3) selecting an optimal hyper-parameter combination of the fault diagnosis model by using the method in the step 2) in combination with a five-fold cross validation mode; and 4) constructing an improved Bayesian optimization LightGBM fault diagnosis model, and giving a model iteration process and an optimization result. By adopting the technology, compared with the prior art, according to the invention,an improved Bayesian optimization algorithm is provided to carry out optimization selection on parameters of a fault model; by improving an acquisition function of a traditional Bayesian optimizationalgorithm and a covariance function of a Gaussian process of the traditional Bayesian optimization algorithm, an improved Bayesian optimization LightGBM fault diagnosis method is provided, and equipment faults are diagnosed and predicted.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to an improved Bayesian optimized LightGBM fault diagnosis method. Background technique [0002] At present, the research hotspot of fault diagnosis is mainly the data-driven fault diagnosis method based on machine learning algorithm, but the fault diagnosis model constructed based on this method still has a large number of uncertain parameters, which leads to the problem of large fluctuations in the accuracy of fault diagnosis of the model. The change of different hyperparameters has a great influence on the prediction accuracy of the fault diagnosis model. Selecting a better parameter combination can give full play to the superior performance of the fault diagnosis model and greatly improve the recognition rate of fault diagnosis, which will bring huge benefits to the enterprise. economic benefits. So how to choose the combination of hyperparameters that can make the mode...

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Application Information

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IPC IPC(8): G06F11/34G06F17/50G06K9/62
CPCG06F11/3447G06F11/3457G06F18/214
Inventor 姜少飞许青青邬天骥李吉泉李志高启龙
Owner ZHEJIANG UNIV OF TECH
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