Soochow University

Xingxing Jiang

Jun Wang

Zhongkui Zhu

Weiguo Huang

Changqing Shen

Juanjuan Shi

Dong Wang

Aiwen Zhang

Xu Wang

Dongmiao Song

Guifu Du

Yongjun Shen

Yu Xia

Liang Chen

Jianqin Zhou

Jun Dai

Chuancang Ding

Wenjun Guo

Shuangjie Liu

Zaigang Chen

Piao Lei

Jing Xie

Minjie Chen

Xiaofeng Shang

Hongbo Que

Lin Kong

The present invention provides an unsupervised fault diagnosis method for mechanical equipment based on an adversarial flow model, main steps including: data preprocessing, converting a mechanical vibration signal into a frequency domain signal, and normalizing the amplitude value of the signal into a range of [0, 1]; prior distribution designing: designing a mixture of Gaussian distribution with K subdistributions, wherein K is determined by the number of mechanical equipment status; model construction: constructing an unsupervised fault diagnosis model by combining an autoencoder, a flow model, and a classifier; model training: training the unsupervised fault diagnosis model by using various classes of status data, along with the designed prior distribution, preset training steps, loss functions, and an optimization algorithm; and fault diagnosis: inputting status data of mechanical equipment into the trained unsupervised fault diagnosis model to obtain a data clustering result and a fault diagnosis result.

Unsupervised fault diagnosis method for mechanical equipment based on adversarial flow model

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 invention relates to a fault diagnosis method for a rolling bearing under variable working conditions. Based on a convolutional neural network, a transfer learning algorithm is combined to handle the problem of the reduced universality of deep learning models. Data acquired under different working conditions is segmented to obtain samples. The samples are preprocessed by using FFT. Low-level features of the samples are extracted by using improved ResNet-50, and a multi-scale feature extractor analyzes the low-level features to obtain high-level features as inputs of a classifier. In a training process, high-level features of training samples and test samples are extracted, and a conditional distribution distance between them is calculated as a part of a target function for backpropagation to implement intra-class adaptation, thereby reducing the impact of domain shift, to enable a deep learning model to better carry out fault diagnosis tasks.

Intra-class adaptation fault diagnosis method for bearing under variable working conditions

The invention provides an adaptive manifold probability distribution-based bearing fault diagnosis method, including constructing transferable domains and transfer tasks; converting a data sample in each transfer task into frequency domain data via Fourier transform, inputting the frequency domain data into a GFK algorithm model, and calculating a manifold feature representation matrix related to a bearing fault in each transfer task by using the GFK algorithm model; calculating a cosine distance between centers of a target domain and a source domain in each transfer task according to a manifold feature representation, and defining a target function of in-domain classifier learning; then solving the target function, to obtain a probability distribution matrix of the target domain; and selecting a label corresponding to the largest probability value corresponding to each data sample in the target domain from the probability distribution matrix as a predicted label of the data sample in the target domain.

Adaptive manifold probability distribution-based bearing fault diagnosis method

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

Xingxing Jiang