General normalized maximum mean discrepancy: intelligent fault identification method for bearings and gears under unstable conditions

Self Cite

Variable speed is one of the common working conditions of mechanical equipment,which poses an important challenge to equipment fault diagnosis. The current solutions have the shortcomings of low computational efficiency and large diagnostic errors. The ability of attention mechanism to automatically extract useful features has begun to attract widespread attention in the field of mechanical intelligent fault diagnosis. Combining the advantages of attention mechanism and unsupervised learning, this paper proposes a squeeze-excitation attention guided sparse filtering (SESF) method for mechanical intelligent fault diagnosis method under variable speed. Firstly, the SE attention mechanism is embedded in SF algorithm to guide model training. Then, unsupervised feature extraction is carried out on the variable speed signal samples. The training results are adaptively screened and weighted to make the model pay more attention to the region with the most classify discrimination, so as to improve the feature extraction ability of the model to obtain useful information. Finally, two sets of gear and bearing tests under variable speed condition are adopted to testify the performance of the proposed method. The experimental results show that the SESF method can overcome the influence of variable speed to achieve accurate recognition of different mechanical faults and is superior to the other methods.

Section: Gear Variable Speed Data Experimentsmentioning

confidence: 99%

Attention mechanism guided sparse filtering for mechanical intelligent fault diagnosis under variable speed condition

Han,

Wang,

Wan

et al. 2024

Self Cite

“…The Adam optimization algorithm [27] is applied and set the learning rate to 1 × 10 −3 . Reference to [1], the parameters of SAE are set as follows: when the input dimension is 300, the counts of neurons in the hidden layer is set as 600-200-100. When the input dimension is 300, the number of neurons is set as 200-150-100 respectively.…”

Section: Experimental Studymentioning

confidence: 99%

“…In recent years, machines learning technologies are widely used in rotating machines for fault diagnosis [1,2]. It is generally assumed that when the sensor collects signals at a constant speed, most fault diagnosis methods can obtain accurate health predictions.…”

Section: Introductionmentioning

confidence: 99%

Intelligent rotating machinery fault diagnosis based on super-resolution enhancement using data augmentation under large speed fluctuation

Wang¹,

Han²,

Lü³

et al. 2021

Self Cite

In the real production of industry, in order to solve the problem that it is usually difficult to obtain correctly labeled samples, data augmentation algorithms have received more and more attention. Many efficient deep learning models have been successfully applied to the intelligent fault diagnosis of rotating machinery. However, the premise of the above method is the working conditions of the machinery are constant. It is inevitable that the equipment runs under large speed fluctuation in real industries. To achieve data augmentation under the condition of variable speed, an efficient sub-pixel deep residual neural network (ESPDRN) is proposed. The ESPDRN framework is implemented as follows: utilize low resolution (LR) samples as input to the network, to effectively learn high-level and extract feature maps by constructing a deep residual network. Through sub-pixel convolution layers to arrange the LR features of multi-channels periodically and get a set of ultra high resolution features, and the data points are augmented 16 times compared to raw data. Statistical indicators and experimental results demonstrate that the proposed ESPDRN model can effectively generate data under the condition of variable speed.

“…Chen et al [12] enabled the model to make better decisions by designing multiple subnet-works and a multi-subnet collaborative decision module. Zhang et al [13] and Pan et al [14] fine-tuned the network parameters to make the model more accurate. Additionally, Zhang et al [15] and Qian et al [16] completed fault classification by adjusting the feature distribution of the two domains.…”

Section: Introductionmentioning

confidence: 99%

Intelligent fault diagnosis method of rolling bearing based on multi-source domain fast adversarial network

She,

Zhang,

Wang

et al. 2024

Fault diagnosis of rolling bearings are among the most crucial links in the prognostic and health management (PHM) of bearings. To solve the problem that single-source domain transfer learning can not adapt well to the target domain, a transfer diagnosis method based on Multi-source Domain Fast Adversarial Network (MSDFAN) is proposed. Firstly, the signals from all domains are input into a common subnetwork of fast neural networks to reduce the complexity and network running time of neural networks. Secondly, several adversarial networks are constructed as domain specific feature extractors and then use Higher-order Moment Matching (HoMM) to reduce distribution differences between A and B domains. The two experimental cases of rolling bearing supports the effectiveness and superiority of the proposed MSDFAN.