Rolling bearing fault convolutional neural network diagnosis method based on casing signal

Zhang, Xiangyang; Guo, Chen; Hao, Tengfei; He, Zhiyuan

doi:10.1007/s12206-020-0506-8

Cited by 29 publications

(14 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MLP has a good nonlinear learning ability, but the number of parameters that need to be adjusted in the process of model training is very large. The noise reduction methods based on CNN can effectively reduce the parameters of model training, further extract the deep-seated features of the image and reduce the occurrence of overfitting [ 71 , 72 , 73 ]. Among them, the more representative noise reduction methods based on CNN include encoder–decoder networks [ 74 , 75 ], nonlinear reaction–diffusion model [ 76 ], denoising convolutional neural networks (DNCNN) [ 77 , 78 ], etc.…”

Section: The Flow Of Fault Diagnosis Methods For Rotating Machinery U...mentioning

confidence: 99%

A Review of Fault Diagnosis Methods for Rotating Machinery Using Infrared Thermography

Wang¹,

Zhan²,

Bai³

et al. 2022

Micromachines

View full text Add to dashboard Cite

At present, rotating machinery is widely used in all walks of life and has become the key equipment in many production processes. It is of great significance to strengthen the condition monitoring of rotating machinery, timely diagnose and eliminate faults to ensure the safe and efficient operation of rotating machinery and improve the economic benefits of enterprises. When the state of a rotating machine deteriorates, the thermal energy that is much more than its normal operation will be generated due to the increase in the friction between the components or other factors. Therefore, using the infrared thermal camera to collect the infrared thermal images of rotating machinery and judge the health status of rotating machinery by observing the temperature distribution in the thermal images is often more rapid and effective than other technologies. Nevertheless, after decades of development, the research achievements of infrared thermography (IRT) and its application in various industrial fields are numerous and complex, and there is a lack of systematic sorting and summary of the achievements in this field. Accordingly, this paper summarizes the development and application of IRT as a non-contact and non-invasive tool for equipment condition monitoring and fault diagnosis, and introduces the basic theory of IRT, image processing technology and fault diagnosis methods of rotating machinery in detail. Finally, the review is summarized and some future potential topics are proposed, which will make the subject easier for beginners and non-experts to understand.

show abstract

Section: The Flow Of Fault Diagnosis Methods For Rotating Machinery U...mentioning

confidence: 99%

A Review of Fault Diagnosis Methods for Rotating Machinery Using Infrared Thermography

Wang¹,

Zhan²,

Bai³

et al. 2022

Micromachines

View full text Add to dashboard Cite

show abstract

“…e disadvantage of convolution neural network discussed before is that it is a supervised learning method. It is not suitable for the scene with only a small amount of marked data, but now we can generate the data of each type of fault through the improved generation countermeasure network and then combine the generated data with the real data to train the fault diagnosis model based on convolution neural network to improve the accuracy of the diagnosis model [19,20].…”

Section: Generate a Fault Diagnosis Model Combining Countermeasure Network With Convolution Neural Networkmentioning

confidence: 99%

Research on Fault Diagnosis Model of Generative Adss Based on Improved Semisupervised Diagnosis Algorithm

Yi¹

2021

Mobile Information Systems

View full text Add to dashboard Cite

With the advent of the era of big data and the rapid development of deep learning and other technologies, people can use complex neural network models to mine and extract key information in massive data with the support of powerful computing power. However, it also increases the complexity of heterogeneous network and greatly increases the difficulty of network maintenance and management. In order to solve the problem of network fault diagnosis, this paper first proposes an improved semisupervised inverse network fault diagnosis algorithm; the proposed algorithm effectively guarantees the convergence of generated against network model, makes full use of a large amount of trouble-free tag data, and obtains a good accuracy of fault diagnosis. Then, the diagnosis model is further optimized and the fault classification task is completed by the convolutional neural network, the discriminant function of the network is simplified, and the generation pair network is only responsible for generating fault samples. The simulation results also show that the fault diagnosis algorithm based on network generation and convolutional neural network achieves good fault diagnosis accuracy and saves the overhead of manually labeling a large number of data samples.

show abstract

“…In recent years, deep learning had been widely used in the field of fault diagnosis with its strong learning ability and feature extraction ability [18]. Deep autoencoder [19], deep belief network [20], convolutional neural network (CNN) [21], recurrent neural network [22] and other deep learning methods had been applied to fault diagnosis and achieved satisfactory performance.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of industrial robot based on dual-module attention convolutional neural network

Chen

Wang

et al. 2022

Auton. Intell. Syst.

View full text Add to dashboard Cite

Fault diagnosis plays a vital role in assessing the health management of industrial robots and improving maintenance schedules. In recent decades, artificial intelligence-based data-driven approaches have made significant progress in machine fault diagnosis using monitoring data. However, current methods pay less attention to correlations and internal differences in monitoring data, resulting in limited diagnostic performance. In this paper, a data-driven method is proposed for the fault diagnosis of industrial robot reducers, that is, a dual-module attention convolutional neural network (DMA-CNN). This method aims to diagnose the fault state of industrial robot reducer. It establishes two parallel convolutional neural networks with two different attentions to capture the different features related to the fault. Finally, the features are fused to obtain the fault diagnosis results (normal or abnormal). The fault diagnosis effect of the DMA-CNN method and other attention models are compared and analyzed. The effectiveness of the method is verified on a dataset of real industrial robots.

show abstract

Rolling bearing fault convolutional neural network diagnosis method based on casing signal

Cited by 29 publications

References 12 publications

A Review of Fault Diagnosis Methods for Rotating Machinery Using Infrared Thermography

A Review of Fault Diagnosis Methods for Rotating Machinery Using Infrared Thermography

Research on Fault Diagnosis Model of Generative Adss Based on Improved Semisupervised Diagnosis Algorithm

Fault diagnosis of industrial robot based on dual-module attention convolutional neural network

Contact Info

Product

Resources

About