Joint study on health state assessment and degradation trend prediction of industrial equipment

Zhang, Yong; Gong, Zhongwang; Zheng, Ying; Xie, Lihua; Zhang, Ze; Liu, Zhenxing

doi:10.1360/sst-2021-0337

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Scholars at home and abroad have made relevant progress in this field. Zhang Yong [15] and others used the industrial Internet plus + Transfer learning technology to conduct real-time qualitative analysis (health status) and quantitative analysis (remaining service life) on the status of bearings and tools of key industrial components, to achieve health status assessment and degradation trend prediction of industrial equipment; Chen Jiaxian, Mao Wentao et al proposed a deep temporal feature transfer based bearing residual life prediction method for predicting equipment life.…”

Section: Development Of Fault Diagnosis Based On Deep Learningmentioning

confidence: 99%

Fault Diagnosis of Transfer Learning Equipment Based on Cloud Edge Collaboration + Confrontation Network

2023

Teh. vjesn.

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With the continuous improvement of product quality, production efficiency, and complexity, higher requirements are put forward for the reliability and stability of equipment, and the difficulty of real-time diagnosis of faults and functional failures is also increasing. The traditional fault diagnosis methods based on signal processing and Convolutional neural network cannot meet the requirements of on-site online real-time fault diagnosis of equipment. One is that the vibration signals on the industrial site are superimposed on each other, nonlinear and unstable and traditional feature extraction methods take a long time, resulting in unstable extraction results. Second, massive data and fault diagnosis algorithms need rich computing and storage resources. The traditional Convolutional neural network method conflicts with the real-time response requirements of fault diagnosis. At the same time, different models of fault diagnosis models have poor generalization ability, and the diagnostic accuracy is not high or even impossible to diagnose. To solve the above problems, this paper proposes a fault diagnosis method based on industrial Internet platform, which is equipment cloud edge collaboration + adaptive countermeasure network Transfer learning. On the edge side, the vibration signals collected from key components of the model are processed using empirical mode decomposition (EEMD) to solve the problem of signal nonlinearity and stationarity. In the cloud, EEMD signals of different models are decomposed into source domain and target domain for confrontation training, which is used as the input of the improved domain adversarial network model DANN (Domain Adversarial Neural Networks), so as to improve the accuracy of fault diagnosis of different models by using cloud computing power and the improved adversarial network Transfer learning algorithm. Through the analysis of experimental data, this paper verifies that the model after the confrontation network Transfer learning is more accurate than the traditional fault diagnosis method. Through the coordination of computing resources and real-time requirements, real-time diagnosis of cloud side collaborative bearing fault is realized.

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Section: Development Of Fault Diagnosis Based On Deep Learningmentioning

confidence: 99%

Fault Diagnosis of Transfer Learning Equipment Based on Cloud Edge Collaboration + Confrontation Network

2023

Teh. vjesn.

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“…Fault diagnosis methods of the rotating machine are developed in recent years, where the fault diagnosis method based on the vibration signal analysis is regarded as the most useful method for the mechanical faults [5] . The vibration based method usually extracts the fault features in the vibration signal by using the time domain [6], [7] and frequency domain method [8] , and then diagnoses the fault through the threshold comparison. Vibration intensity and kurtosis are the representative fault features in the time domain, and the vibration magnitudes at the fault frequencies in the spectrum are the characteristics in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Fault injection and diagnosis of the centrifugal fan

Tang

Guo

Wang

et al. 2022

J. Phys.: Conf. Ser.

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Centrifugal fan is widely used in industry, vehicle and ship application to provide air cycle power. To detect the fan fault at early stage, this paper explores the fault feature in the vibration signal and fault diagnosis method through the fault injection test, where impeller unbalance, rotor unbalance, and bearing fault are involved. Firstly, the fault mechanism is introduced; then, the fault injection methods of the three faults are introduced to obtain the faulty fans; finally, health and fault tests are performed, where the vibration sensors are employed and distributed on the fan in the axial and radial direction. Moreover, the acquired vibration data is analyzed by using time-domain and frequency-domain methods, and further the fault features between health and fault are compared and discussed. The analysis results indicate that the three faults can be detected through the vibration intensity and magnitude comparison at their individual characteristic frequencies in the spectrum.

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