Abstract: Existing Bayesian optimization algorithms often suffer from high computational complexity and suboptimal efficiency in locating the global optimum. To address these limitations, an IBO( Improved Bayesian Optimization) algorithm is proposed. First, the MCMC(Monte Carlo Markov Chains) algorithm is improved by using a Gaussian proxy function to improve the proposal function, while adding a hyper-prior to the prior function, which simplifies the complexity of the internal hyper-parameter optimization computation of the Gaussian process, and improves computational efficiency. Second, Gaussian modeling of the loss function with respect to the dataset size in the hyperparameter optimization process of CNN(Convolutional Neural Networks) is proposed to enable the model to adaptively select the dataset size to optimize the hyperparameters, so that the combination of hyperparameters that minimizes the loss function can be found using fewer datasets. The improved Bayesian optimization algorithm is tested using the Branin-Hoo function, which proves that the IBO algorithm is able to find the optimal value in the shortest time. Using IBO-CNN for fault diagnosis on PU( University of Paderborn) dataset and comparing with other hyper-parametric optimization algorithms, the results prove that the IBO algorithm can find the minimum value of the loss function more quickly, so that the training process converges quickly, the diagnostic accuracy is higher than the other algorithms by about 0. 5% to 3% , and it exhibits a good fault diagnostic performance on dataset under different working conditions. Thus it is proved that the algorithm has higher computational efficiency than other optimization algorithms. 

Key words: bayesian optimization, convolutional neural networks ( CNN) hyperparameters, bearing fault diagnosis, monte carlo markov chains(MCMC)