Bearing fault diagnosis based on CNN-BiLSTM and residual module

Insufficient and imbalanced samples pose a significant challenge in bearing fault diagnosis, leading to low diagnosis accuracy. However, the fault characteristics of vibration signals are weak and difficult to extract when faults occur in the early stage. This paper proposes an effective fault diagnosis method that addresses small and imbalanced sample problems under noise interference. First, the number of faulty samples in the form of 1D signals is increased mainly by the sliding split sampling method. The preprocessed data are used to create 2D time–frequency diagrams using the continuous wavelet transform (CWT), which can extract effective features to improve the data quality. Subsequently, the minority samples are oversampled by combining Synthetic Minority Oversampling Technique (SMOTE) to realize TFCAO. Moreover, the clustering method and random undersampling method are introduced to prevent the overfitting and underfitting problems respectively. Then, we propose a hybrid attention mechanism to enhance the extraction of effective feature information. This combination, integrating CWT with a multicolumn modified DRN, effectively extracts fault characteristics and suppresses noise effects. The experimental results demonstrate the effectiveness of the proposed method by comparison with other advanced methods using two case studies of bearing datasets.

Section: Introductionmentioning

confidence: 99%

A novel approach for bearings multiclass fault diagnosis fusing multiscale deep convolution and hybrid attention networks

Li,

Zhao

2024

“…Deep learning models such as convolutional neural networks (CNNs) [4,5], Autoencoder [6,7], recurrent neural networks [8,9], and deep confidence networks [10] have been widely used in the field of fault diagnosis and have achieved high accuracy rates. For example, Fu et al [11] proposed a CNN-BiLSTM model with residual module and channel attention using timedomain signals and continuous wavelet transformed timefrequency images as inputs to further improve the ability of extracting fault features as well as noise immunity. Yan et al [12] proposed a residual convolutional variational selfencoder model based on the attention mechanism (AM-RCVAE) and experimentally validated the proposed method on two bearing datasets for experimental validation, which demonstrated the improved diagnostic performance of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

A small sample bearing fault diagnosis method based on ConvGRU relation network

Zhao,

Zhang

2024

Considering that in the fault diagnosis of bearing based on relation network, using the sample mean value as the class prototype for each class is susceptible to outliers, resulting in inaccurate class prototypes, this paper proposes a ConvGRU relation network fault diagnosis model; Firstly, the model utilizes a embedding module to extract bearing fault features, and then uses the Convolutional Gate Recurrent Unit (ConvGRU) as a learnable class prototype generator to generate class prototypes for each class. Secondly, a relation module is utilized to measure the similarity between class prototypes and the sample features of the query set, obtaining relation scores, and ultimately achieving fault diagnosis. In order to test the validity and advantages of the model, experimental verification and analysis were conducted on the Case Western Storage rolling bearing dataset. The results of the experiment show that the class prototypes generated by the ConvGRU class prototype generation module have better discrimination and accuracy compared to the class prototypes generated by the relation network. In the 10-way 5-shot experiment, the accuracy of the model proposed in this paper reaches 99.60%, which increases by 6.63%, 5.10%, 4.80% , and 1.75% compared to KNN, CNN, Prototype Network , and Relation Network. The method proposed in this paper helps to generate more accurate class prototypes and has a certain effect on improving the accuracy of model fault diagnosis.

“…However, these methods do not adequately address the deeper fusion of features and the inherent disparities among features at different scales. Besides, in the actual working environment of rolling bearings, noise is inevitable which affects the diagnostic results and is neglected by the above methods [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A multi-scale collaborative fusion residual neural network-based approach for bearing fault diagnosis

Qian,

Gao,

Shao

et al. 2024

In recent years, deep learning techniques have become popular for diagnosing equipment faults. However, their real industrial application performance is hindered by challenges related to noise and variable load conditions that prevent accurate extraction of valid feature information. To tackle these challenges, this paper proposed a novel approach known as the Multi-Scale Collaborative Fusion Residual Neural Network (MCFRNN) for bearing fault diagnosis. To begin with, the methodology introduces a Multi-Scale Systolic Denoising Module (MSDM) designed to extract features at multiple scales while mitigating the influence of noise. Subsequently, a Central Fusion Module (CFM) is employed to explore the intrinsic correlation among the multiple channels and effectively fuse their respective features. Additionally, a Global Sensing Module (GSM) is incorporated to enhance the perceptual field of MCFRNN, thereby facilitating the extraction of global features. Furthermore, online label smoothing and AdamP are applied to alleviate overfitting and improve the diagnostic capability of MCFRNN under small sample. Finally, the effectiveness of MCFRNN is verified with two publicly available datasets under complex operational and limited sample conditions. The experimental results show that the proposed method has more excellent diagnostic performance and adaptivity than the existing popular methods.