An Improved MSCNN and GRU Model for Rolling Bearing Fault Diagnosis

The fault diagnosis of rolling bearings is a critical aspect of rotating machinery, as it significantly contributes to the overall operational safety of the mechanical equipment. In the practical engineering environment, the complex and variable working conditions, along with the presence of overlapping noise, contribute to intricate frequency information in the acquired signals and their highly time-dependent characteristics, which makes it difficult to extract the available fault features hidden in the signal. Based on this, a hybrid fault diagnosis method named GGRU-1DCNN-AdaBN is introduced, which combines improved gap-gated recurrent unit network(GGRU), one-dimensional convolutional neural network(1DCNN), and adaptive batch normalization(AdaBN). The proposed approach involves several parts to enhance fault diagnosis accuracy in vibration signals under constant load conditions and variable load conditions. Firstly, the end-layer structure of the traditional GRU is replaced with a one-dimensional global average pooling layer (1D-GAP) to aggregate the influence components of defects and reduce model training parameters. Secondly, the fusion of different types of frequency and sequence features is achieved by combining 1DCNN, addressing the limitation of a single network's feature extraction capability and the loss of temporal features in a cascaded hybrid model. Subsequently, the fused features are input into a softmax multi-classifier to obtain fault type identification results. Lastly, the GGRU-1DCNN method is further improved by incorporating the AdaBN algorithm, enhancing the model's domain adaptive capability under variable load conditions and noisy environments. The method is validated using datasets obtained from Case Western Reserve University (CWRU), aero-engine bearings, Xi'an Jiaotong University, and the Changxing Sumyoung Technology (XJTU-SY). The findings suggest that the proposed method demonstrates superior accuracy and robustness in fault diagnosis, as well as excellent generalization capability and universal applicability.

Section: Introductionmentioning

confidence: 99%

A hybrid fault diagnosis method for rolling bearings based on GGRU-1DCNN with AdaBN algorithm under multiple load conditions

Sun,

Zhu,

Xiao

et al. 2024

“…The combination of graphic modeling techniques and deep learning (DL) techniques have proven to be successful and beneficial in fault identification. Various DL-based techniques, such as CNNs [17][18][19], Res2Net [20], RNNs, transformer [16], and swin transformer [21], have been extensively employed for this purpose. For instance, the deep residual shrinkage network [22] is an innovative DL architecture that utilizes a soft threshold approach, serving as a CNN variant specifically designed to handle noise.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical dynamic aggregation graph modeling of vibration for semi-supervised fault diagnosis of power transformer

Liu,

2023

Power equipment fault diagnostics is a critical aspect of ensuring the stability of the power grid system. However, it presents substantial challenges in obtaining labeled data that is spatial-temporal, multi-scale, and multi-domain, low noise for effective fault analysis and diagnosis. To address this issue, we propose a novel approach called hierarchical dynamic aggregation graph (HDAG) modeling for self-supervised fault diagnosis of power transformers using vibration data. Firstly, HDAG focuses on modeling the spatial and temporal correlations within the fault vectors, before converting them into time–frequency images for visualization. Secondly, our proposed fault diagnosis approach, (comprising the ST-sparse swin-transformer and multi-domain transformer fusion module), is integrated into the methodology. The ST-sparse swin transformer incorporates soft threshold modules, enabling the retention of relevant information while discarding irrelevant information. The multi-domain transformer fusion is designed to utilize the intra-domain and inter-domain signal characteristics to achieve a comprehensive feature representation. Finally, we present case studies based on experimental data that demonstrate the feasibility and effectiveness of our approach. Comparative evaluations against eight state-of-the-art techniques validate the improved information representation and diagnostic capabilities of our recommended strategy.

“…Better diagnostic performance of the model can often be achieved by using multi-scale feature extraction methods. Wang et al [21] implemented adaptive feature extraction for fault diagnosis of rolling bearings using a multi-scale convolutional neural network, an attention mechanism, and gated recurrent unit. Xie et al [22] combined complementary ensemble empirical mode decomposition and multi-scale multi-layer perceptron for fault diagnosis of train bearings.…”

Section: Introductionmentioning

confidence: 99%

A novel collaborative bearing fault diagnosis method based on multi-scale dynamic fusion network under speed fluctuating condition

Xing,

Wang,

Han

et al. 2023

Improving bearing fault diagnosis accuracy under speed fluctuation is a challenge in engineering applications. With the development of big data processing technology, a new solution, multi-sensor complementary information, has emerged. However, single-scale dimension compression, which is adopted in most multi-sensor data fusion methods, captures only a small amount of valuable information. To deal with this deficiency, a multi-scale dynamic fusion network (MSDFN) is proposed. First, considering the existence of non-stationary features in the fluctuating speed signal, the FReLU function is adopted to activate the features after considering contextual information. Then, multi-sensor features are fused by multiple scales to obtain richer feature information, and fusion features at different scales are weighted by using the attention mechanism. Finally, batch normalization (BN) is employed to standardize the variable speed feature distribution. The validity of the MSDFN is proved by conducting fault diagnosis experiments on two bearings under speed fluctuating conditions. Experimental results indicate that the MSDFN is not only effective in identifying various types of fault samples, but also shows higher stability in multiple trials when compared with other methods.