2022
DOI: 10.1049/cim2.12055
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Intelligent fault diagnosis of rotating machinery using lightweight network with modified tree‐structured parzen estimators

Abstract: Deep learning-based methods have been widely used in the field of rotating machinery fault diagnosis. It is of practical significance to improve the calculation speed of the model on the premise of ensuring accuracy, so as to realise real-time fault diagnosis. However, designing an efficient and lightweight fault diagnosis network requires expert knowledge to determine the network structure and adjust the hyperparameters of the network, which is time-consuming and laborious. In order to design fault diagnosis … Show more

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Cited by 6 publications
(2 citation statements)
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“…Training data set (𝑋𝑋 𝑖𝑖 , 𝑌𝑌 𝑖𝑖 ), 𝑖𝑖 = 1,2,3, … … , 𝑁𝑁 where 𝑋𝑋𝑖𝑖 refers to a continuous-valued vector in 𝑛𝑛 dimensions, and 𝑌𝑌 𝑖𝑖 = {0,1} The corresponding class identification is denoted by "0" for normal and "1" for abnormalities. The proposed method consists of two parts: training and testing [25]. Following method, the training space is partitioned into k separate clusters C_1, C_2, C_3,..., C_K.…”
Section: Clustering-based Decision Treementioning
confidence: 99%
“…Training data set (𝑋𝑋 𝑖𝑖 , 𝑌𝑌 𝑖𝑖 ), 𝑖𝑖 = 1,2,3, … … , 𝑁𝑁 where 𝑋𝑋𝑖𝑖 refers to a continuous-valued vector in 𝑛𝑛 dimensions, and 𝑌𝑌 𝑖𝑖 = {0,1} The corresponding class identification is denoted by "0" for normal and "1" for abnormalities. The proposed method consists of two parts: training and testing [25]. Following method, the training space is partitioned into k separate clusters C_1, C_2, C_3,..., C_K.…”
Section: Clustering-based Decision Treementioning
confidence: 99%
“…The TPE algorithm is a sequential model-based global optimization algorithm that effectively determines the hyperparameters of a machine learning model. It was developed to overcome the shortcomings of the traditional Bayesian Optimization approaches when dealing with categorical and conditional hyperparameters by introducing Parzen window estimators, thereby enhancing the performance of the hyperparameter search strategy [26,27]. Employing the Parzen-window density estimation, the TPE algorithm produces probability density functions within a hyperparametric search space.…”
Section: Tree-structured Parzen Estimator (Tpe)mentioning
confidence: 99%