Soochow University

Guifu Du

Jun Wang

Zhongkui Zhu

Weiguo Huang

Juanjuan Shi

Xingxing Jiang

Changqing Shen

Xu Wang

Dong Wang

Aiwen Zhang

Dongmiao Song

Jianqin Zhou

Yongjun Shen

Wenjun Guo

Shuangjie Liu

Zaigang Chen

The present invention provides a dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions, including acquiring bearing vibration data under different working conditions to obtain a source domain sample and a target domain sample; establishing a deep convolutional neural network model with dynamic joint distribution alignment; feeding both the source domain sample and the target domain sample into the deep convolutional neural network model with initialized parameters, and extracting, by a feature extractor, high-level features of the source domain sample and the target domain sample; calculating a marginal distribution distance and a conditional distribution distance; obtaining a joint distribution distance according to the marginal distribution distance and the conditional distribution distance, and combining the joint distribution distance and a label loss to obtain a target function; and optimizing the target function by using SGD, and training the deep convolutional neural network model.

Dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions

The present invention discloses a fault diagnosis method under a convergence trend of a center frequency, including: (1) acquiring a dynamic signal x(t) of a rotary machine equipment; (2) setting initial decomposition parameters of a variational model; (3) decomposing the dynamic signal x(t) by using the variational model with the set initial decomposition parameters, and traversing a signal analysis band and performing iterative decomposition on the dynamic signal x(t) under the guidance of a convergence trend of a center frequency, to obtain optimized modals {m. . . m. . . m} and corresponding center frequencies {&#x3c9;. . . &#x3c9;. . . &#x3c9;}; (4) searching a fault related modal m, guiding parameter optimization by using a center frequency &#x3c9;of the fault related modal m, and retrieving an optimal target componentincluding fault information; and (5) performing envelopment analysis on the optimal target component, and diagnosing the rotary machine equipment according to an envelope spectrum.

Fault diagnosis method under convergence trend of center frequency

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Suzhou, China

Guifu Du