LightGBM fault diagnosis method based on improved Bayesian optimization

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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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 Bayesian optimized LightGBM fault diagnosis method for solving The problem that the uncertainty of hyperparameters affects the accuracy of the fault diagnosis model, so as to improve the accuracy of fault diagnosis and the robustness of the model

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