Research on rolling bearing fault diagnosis method based on AMVMD and convolutional neural networks

Zhang, Huichao; Shi, Peiming; Jia, Linjie

doi:10.1016/j.measurement.2023.113028

Cited by 14 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) Cross entropy loss: The cross-entropy loss of classification is often paired with Softmax classifier in classification problems to calculate the classification loss of source domain samples in the DDTLN model [35]. Its specific expression is as follows:…”

Section: Optimize the Loss Functionmentioning

confidence: 99%

Wind turbines fault diagnosis method under variable working conditions based on AMVMD and deep discrimination transfer learning network

Shi,

Jia,

et al. 2024

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

With the wide application of wind turbines, the bearing fault diagnosis of wind turbines has become a research hotspot. Under complex variable working conditions, the vibration signals of bearing components show non-stationary characteristics. Therefore, it is challenging to extract fault features using typical fault diagnosis methods. This paper proposes Adaptive Multivariate Variational Mode Decomposition combined with an improved Deep Discrimination Transfer Learning Network (AMVMD-IDDTLN) for bearing fault diagnosis of wind turbines under variable working conditions. First, the AMVMD method is used for the adaptive decomposition of the original signal, and use SE-ResNet18 convolutional neural network to obtain the transfer features of the source domain and target domain. Then, marginal distribution differences and conditional differences are assessed by DDM measures. The whole model is optimized by cross-entropy and IJDA loss function, and the identification and classification of cross-working fault characteristics of the wind turbine- bearings are realized. The model achieves 99.48% transfer learning for the ten classifications of CWRU data set, 97% transfer learning for the four classifications of UPB data set, and 90% transfer learning for wind turbine bearing data across working conditions and across equipment. It is concluded that: Compared with similar models, the AMVMD-IDDTLN model proposed in this paper has higher diagnostic accuracy and faster convergence rate, which has certain practicality.

show abstract

Section: Optimize the Loss Functionmentioning

confidence: 99%

Wind turbines fault diagnosis method under variable working conditions based on AMVMD and deep discrimination transfer learning network

Shi,

Jia,

et al. 2024

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Xiong et al combined a complementary ensemble empirical mode decomposition with multidimensional non-dimensional indicators to extract complementary ensemble multi-dimensional indicators (CEMDIs) from vibration signals, which were then transformed into two-dimensional data as the input for the CNN to perform the fault diagnosis of rotating machinery [25]. Zhang et al proposed an adaptive multi-dimensional variational mode decomposition to decompose an original signal and used a multi-scale CNN to extract the fault features from the denoised signal for the fault-type recognition of rolling bearings [26]. Kim et al incorporated a healthadaptive time-scale representation (HTSR) into a CNN to extract richer fault information and perform the intelligent diagnosis of gearboxes [27].…”

Section: Introductionmentioning

confidence: 99%

Joint-Module Health Status Recognition for an Unmanned Platform: A Time–Frequency Representation and Extraction Network-Based Approach

Zhu,

Yang,

Song

et al. 2024

Machines

View full text Add to dashboard Cite

Due to the complex structure of the joint module and harsh working conditions of unmanned platforms, the fault information is often overwhelmed by noise. Moreover, traditional mechanical health state recognition methods usually require a large amount of labeled data in advance, which is difficult to obtain for specific fault data in engineering applications. This limited amount of fault data restricts the diagnostic performance. Additionally, the characteristics of convolutional neural networks (CNNs) limit their ability to capture the relative positional information of fault features. In order to obtain more comprehensive fault information, this paper proposes an intelligent health state recognition method for unmanned platform joint modules based on feature modal decomposition (FMD) and the enhanced capsule network. Firstly, the collected vibration signals are decomposed into a series of feature modal components using FMD. Then, time–frequency maps containing significant fault features are generated based on the continuous wavelet transform (CWT). Finally, a multi-scale feature enhancement (MLFE) module and an efficient channel attention (ECA) module are proposed to enhance the feature extraction capability of the capsule network, extracting more comprehensive global and local feature information from the time–frequency maps to achieve the intelligent state recognition of joint modules. This approach enhances fault features while reducing the impact of redundant features, significantly improving the feature extraction capability without increasing the model’s computational complexity. The effectiveness and superiority of the proposed method are validated through experiments on an unmanned platform joint-module testbed. An ablation experiment demonstrates the effectiveness of the MLFE and ECA modules, and a comparison with other advanced network models proves the superiority of the proposed method for health status recognition.

show abstract

“…The achieved results show that the proposed approach is clearly promising for bearing degradation monitoring. Huichao Zhang, et al [37] proposed a bearing fault diagnosis method based on adaptive multivariate variational mode decomposition (AMVMD) and multi-scale convolutional neural network (Multi-scale CNN). The results show that the fault diagnosis accuracy of AMVMDMSCNNs can achieve 98.60%.…”

Section: Introductionmentioning

confidence: 99%

A Novel 1D-Convolutional Spatial-Time Fusion Strategy for Data-Driven Fault Diagnosis of Aero-Hydraulic Pipeline Systems

Yang

Wang

et al. 2023

Mathematics

View full text Add to dashboard Cite

Intelligent diagnosis of faults in an aero-hydraulic pipeline is important for condition monitoring of its systems. However, there are no more qualitative formulas or feature indicators to describe the faults of aero-hydraulic pipelines because of the complexity and diversity of aero-hydraulic pipeline systems, which leads to a very complex pipeline fault mechanism. In addition, although it is well known that the expression of interpretable and representable pipeline intelligent diagnosis models with pipeline fault characteristics are buried in high background noise and strong noise disturbance conditions in practical industrial scenarios, this has yet to be discussed. Inspired by the demand, this paper proposes a novel diagnosis strategy: the 1D-convolutional space-time fusion strategy for aero-engine hydraulic pipelines. Firstly, by optimizing the convolutional neural network and using it to design a one-dimensional convolutional neural network (1DCNN) with a wide input scale to expand the input field of perception, thereby obtaining more comprehensive spatial information of the pipeline data, which can effectively extract richer short sequence features. Secondly, a network of bidirectional gated recurrent Unit (Bi-GRU) is proposed, which integrates a short sequence of high-dimensional features for temporal information fusion, resulting in a certain degree of avoiding memory loss and gradient dispersion caused by the too-large step size. It is demonstrated that, for the noise signal and variable pressure signal, the fault identification accuracy approximated 95.9%, proving the proposed strategy’s robustness. By comparing with the other five methods, the proposed strategy has the ability to identify 10 different fault states in the aero-hydraulic pipeline with higher accuracy.

show abstract

Research on rolling bearing fault diagnosis method based on AMVMD and convolutional neural networks

Cited by 14 publications

References 16 publications

Wind turbines fault diagnosis method under variable working conditions based on AMVMD and deep discrimination transfer learning network

Wind turbines fault diagnosis method under variable working conditions based on AMVMD and deep discrimination transfer learning network

Joint-Module Health Status Recognition for an Unmanned Platform: A Time–Frequency Representation and Extraction Network-Based Approach

A Novel 1D-Convolutional Spatial-Time Fusion Strategy for Data-Driven Fault Diagnosis of Aero-Hydraulic Pipeline Systems

Contact Info

Product

Resources

About