A Multi-Source Weighted Deep Transfer Network for Open-Set Fault Diagnosis of Rotary Machinery

Chen, Zhuyun; Liao, Yixiao; Li, Jipu; Huang, Ruyi; Xu, Lei; Jin, Gang; Li, Weihua

doi:10.1109/tcyb.2022.3195355

Cited by 86 publications

(19 citation statements)

References 41 publications

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“…Therefore, it is necessary to explore a learning strategy to reduce various noises according to the noise level of the input image. The soft thresholding function has been widely used as a key step in many signal-denoising methods [35]. It can be represented as follows:…”

Section: Construction Of Residual Shrinkage Balanced Network (Rsbn)mentioning

confidence: 99%

“…It only sets the near-zero features to zeros, which can effectively preserve the negative features. Accordingly, the partial derivative of the soft thresholding function can be expressed by [35]…”

Section: Construction Of Residual Shrinkage Balanced Network (Rsbn)mentioning

confidence: 99%

“…where z l is the scaled output vector of the final layer. Accordingly, the threshold used in the RSM can be represented as [35]…”

Section: Construction Of Residual Shrinkage Balanced Network (Rsbn)mentioning

confidence: 99%

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A Robust Residual Shrinkage Balanced Network for Image Recognition from Japanese Historical Documents

Qiu

Dong

2023

Journal of Sensors

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Kuzushiji, as characters written in a cursive style, had been continuously used for both publishing and handwriting in Japan before the end of the 19th century. However, well into the twentieth century, most modern Japanese lost the ability to read Kuzushiji due to changes in writing systems. Therefore, how to develop advanced machine learning algorithms to identify Japanese historical characters has great significance in preserving cultural knowledge and promoting sustainable development in urban living. Unlike traditional digit image recognition, Kuzushiji image recognition is a more challenging task due to the complex script for cursive handwriting, multiple variations between characters, and imbalanced data. To address such issues, a novel deep learning-based character recognition method called residual shrinkage balanced network (RSBN) is developed for classifying Japanese historical characters. A novel residual shrinkage structure with a soft threshold function is constructed to reduce redundant features using specially designed skip-connected subnetworks. Then, multiple residual shrinkage modules are stacked together to learn hierarchical features from raw input. Furthermore, a novel class rebalancing strategy is further designed to improve learning performance from imbalanced data. The experiments are conducted on Japanese classical literature datasets. The results demonstrate that the proposed method obtains more than 89.01% testing accuracy on Kuzushiji-MNIST dataset, which outperforms the existing deep learning-based character image recognition methods. Moreover, without additional data augmentation techniques, the proposed approach also achieves 97.16% balanced accuracy on Kuzushiji-49 dataset, which is the best one compared with existing published results. When considering the computational performance, the number of trainable parameters of the proposed method is competitive with that of the typical ResNet-18.

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Section: Construction Of Residual Shrinkage Balanced Network (Rsbn)mentioning

confidence: 99%

Section: Construction Of Residual Shrinkage Balanced Network (Rsbn)mentioning

confidence: 99%

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A Robust Residual Shrinkage Balanced Network for Image Recognition from Japanese Historical Documents

Qiu

Dong

2023

Journal of Sensors

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“…Various research on essential diagnosis issues, such as deep learning methods [3,4], knowledge transfer [5][6][7][8][9], fault decoupling and detection [10][11][12], imbalance data augmentation, and model generalization [13][14][15][16], have been carried out. For example, Syed Muhammad Tayyab et al [17] used machine learning through optimal feature extraction and selection for intelligent fault diagnosis of machine elements.…”

Section: Introductionmentioning

confidence: 99%

Clustering Federated Learning for Bearing Fault Diagnosis in Aerospace Applications with a Self-Attention Mechanism

Yang

Jin

et al. 2022

Aerospace

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Bearings, as the key mechanical components of rotary machinery, are widely used in modern aerospace equipment, such as helicopters and aero-engines. Intelligent fault diagnosis, as the main function of prognostic health management systems, plays a critical role in maintaining equipment safety in aerospace applications. Recently, data-driven intelligent diagnosis approaches have achieved great success due to the availability of large-scale, high-quality, and complete labeled data. However, in a real application, labeled data is often scarce because it requires manual labeling, which is time-consuming and labor-intensive. Meanwhile, health monitoring data are usually scattered in different regions or equipment in the form of data islands. Traditional fault diagnosis techniques fail to gather enough data for model training due to data security, economic conflict, relative laws, and other reasons. Therefore, it is a challenge to effectively combine the data advantages of different equipment to develop an intelligent diagnosis model with better performance. To address this issue, a novel clustering federated learning (CFL) method with a self-attention mechanism is proposed for bearing fault diagnosis. Firstly, a deep neural network with a self-attention mechanism is developed in a convolutional pipe for feature extraction, which can capture local and global information from raw input. Then, the CFL is further constructed to gather the data from different equipment with similar data distribution in an unsupervised manner. Finally, the CFL-based diagnosis model can be well trained by fully utilizing the distributed data, while ensuring data privacy safety. Experiments are carried out with three different bearing datasets in aerospace applications. The effectiveness and the superiority of the proposed method have been validated compared with other popular fault diagnosis schemes.

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“…In conclusion, the integration of AI-enabled industrial equipment monitoring, diagnosis, and health management is poised to significantly impact the future of predictive maintenance and operational efficiency in various industries. With the continued advancement of technologies such as transfer learning, explainable AI, digital twin, and reinforcement learning [43][44][45][46][47][48][49][50][51][52], we foresee a transformation in fault diagnosis, real-time monitoring, and proactive health management of industrial equipment. The application of these cutting-edge technologies in prognostics and health management is expected to revolutionize the industry, offering more precise predictions, minimized downtime, and enhanced equipment performance, ultimately leading to heightened productivity and cost savings.…”

mentioning

confidence: 99%

AI-enabled industrial equipment monitoring, diagnosis and health management

Chen,

Shao,

Han

et al. 2024

Meas. Sci. Technol.

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Artificial intelligence (AI) has achieved significant progress in recent years and its applications cover a wide range of fields such as computer vision, natural language processing, autonomous driving and medical diagnosis. In industry, the rapid development of real-time-sensor measurement techniques promotes equipment surveillance and maintenance into the era of big data. It is still challenging to manually analyze this big data and establish general physical modelling by using the information hidden in these data. To this end, AI technology, such as machine learning and neural networks, has emerged as a promising tool to extract useful knowledge from measured data, and to tackle the real-time monitoring, diagnosis problems as well as enhancing the health management and reliability of modern industrial equipment. With the vision of establishing a strong link between AI and industrial equipment surveillance and maintenance, this special feature is designated to select, organize and exhibit the latest research progress on the cutting-edge research topics relevant to AI-Enabled Industrial Equipment Monitoring, Diagnosis and Health Management. Submissions for this topic in Measurement Science and Technology are open from 23 March 2022 to 30 September 2022 and contains 42 outstanding papers in above research fields.

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A Multi-Source Weighted Deep Transfer Network for Open-Set Fault Diagnosis of Rotary Machinery

Cited by 86 publications

References 41 publications

A Robust Residual Shrinkage Balanced Network for Image Recognition from Japanese Historical Documents

A Robust Residual Shrinkage Balanced Network for Image Recognition from Japanese Historical Documents

Clustering Federated Learning for Bearing Fault Diagnosis in Aerospace Applications with a Self-Attention Mechanism

AI-enabled industrial equipment monitoring, diagnosis and health management

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