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7. Intelligent rolling bearing compound fault diagnosis based on frequency-domain Gramian angular field and convolutional neural networks with imbalanced data.

Author

Zhang, Faye, Yao, Peng, Geng, Xiangyi, Mu, Lin, Paitekul, Phanasindh, Viyanit, Ekkarut, Jiang, Mingshun, and Jia, Lei

Subjects
CONVOLUTIONAL neural networks, ROLLER bearings, DATA distribution, DIAGNOSIS methods, GENERALIZATION
Abstract

The effective fault feature extraction is the core of rolling bearing fault diagnosis. However, rolling bearings usually operate in normal state and fault duration is very short, which will cause imbalance in fault diagnosis data, thus leading to difficulty in fault feature extraction and low diagnosis accuracy. Meanwhile, mutual interference between multiple fault responses will also lead to poor diagnosis performance. To solve these issues, a novel compound fault diagnosis method with imbalanced data based on frequency-domain Gramian angular field (FGAF) and convolutional neural networks optimized by instance normalization and efficient channel attention (IECNN) is proposed. Firstly, FGAF is adopted to map frequency-domain features of fault signals to the polar coordinate to obtain 2D FGAF feature spectrum. Secondly, an instance normalization module is established to reduce internal covariant shift caused by data distribution discrepancy and improve generalization ability. An efficient channel attention module is constructed to further excavate fault features and improve anti-interference ability. Finally, experiments are conducted under imbalanced dataset and imbalance intensified dataset, and the average accuracy of 99.91% and 99.92% were obtained, respectively, which shows the proposed method has better resistance to data imbalance. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Data-Driven Method for Predicting Remaining Useful Life of Bearings Based on Multi-Layer Perception Neural Network and Bidirectional Long Short-Term Memory Network.

Author

Tai, Yongfeng, Yan, Xingyu, Geng, Xiangyi, Mu, Lin, Jiang, Mingshun, and Zhang, Faye

Subjects
LONG short-term memory, REMAINING useful life, PARTICLE swarm optimization, PRINCIPAL components analysis, ROLLER bearings, ACCELERATED life testing
Abstract

The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee. In engineering scenarios, only a small amount of bearing performance degradation data can be obtained through accelerated life testing. In the absence of lifetime data, the hidden long-term correlation between performance degradation data is challenging to mine effectively, which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method. To address this problem, a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed. Firstly, a nonlinear health indicator (HI) calculation method based on kernel principal component analysis (KPCA) and exponential weighted moving average (EWMA) is designed. Then, using the raw vibration data and HI, a multi-layer perceptron (MLP) neural network is trained to further calculate the HI of the online bearing in real time. Furthermore, The bidirectional long short-term memory model (BiLSTM) optimized by particle swarm optimization (PSO) is used to mine the time series features of HI and predict the remaining service life. Performance verification experiments and comparative experiments are carried out on the XJTU-SY bearing open dataset. The research results indicate that this method has an excellent ability to predict future HI and remaining life. [ABSTRACT FROM AUTHOR]

Published
2025
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15. A Novel Bearing Fault Diagnosis Method Based on Multifeature Fusion Attention-Guided Mechanism With Noise Robustness

Author

Guo, Weichao, Zhang, Yilan, Peng, Chang, Geng, Xiangyi, Jiang, Mingshun, Zhang, Lei, Sui, Qingmei, and Zhang, Faye

Abstract

Fault diagnosis (FD) of rolling bearings is crucial for ensuring the reliability and safety of rotating machinery. However, substantial noise interference has caused significant difficulties in improving the robustness of FD. To address this issue, a multifeature fusion attention-guided mechanism with wide first-layer kernels convolutional neural network (MFA-WCNN) is proposed. The position information is introduced into the attention module by feature fusion to catch the relative position or other information of fault fluctuations segment in the whole cycle, which extracts more discriminative fault-related features from noise-contaminated signals. Concretely, a feature extraction module (CFE-Module) is proposed to utilize the different levels of features of rolling bearings, by constructing a convolutional neural network (CNN) with wide first-layer kernels to extract the position information from the low-level features. Furthermore, a feature learning adaptive adjustment module (FLA-Module) is constructed to extract advanced features containing semantic information, simultaneously ignoring the irrelevant information and noise in the fusion features. These two modules allow MFA-WCNN to extract and generate multilevel fusion features with position details and semantic information, which promotes the improvement of FD performance with strong noise. The evaluation experiments conducted on two testing platforms show that the network has excellent FD ability under strong noise and unknown noise.

Published
2023
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18. Label-Free Immunosensor Based on Optical Fiber Fabry–Perot Interferometer

Author

Geng Xiangyi, Tianyi Dong, Qingmei Sui, Xiaohui Liu, and Mingshun Jiang

Subjects
Optical fiber, Materials science, business.industry, 010401 analytical chemistry, Single-mode optical fiber, 02 engineering and technology, 021001 nanoscience & nanotechnology, Interference (wave propagation), 01 natural sciences, 0104 chemical sciences, law.invention, Interferometry, Wavelength, Optics, law, Silanization, Electrical and Electronic Engineering, 0210 nano-technology, business, Instrumentation, Fabry–Pérot interferometer, Photonic-crystal fiber
Abstract

A label-free immunosensor based on optical fiber Fabry–Perot interferometer (FPI) is proposed. The FPI is fabricated by splicing both ends of a short section of hollow-core photonic crystal fiber to single mode fibers (SMFs) and cleaving the SMF pigtail to proper length. Then, the end surface of sensor is silanization modified and immobilized with goat anti-rabbit immunoglobulin G (IgG) by covalent binding to detect the specific rabbit IgG. The antibody immobilization and antigen binding will change the thickness of sensing layer, leading to the interference spectrum change of the FPI sensor. The responses of the sensor to immune reaction are analyzed theoretically and demonstrated experimentally. Results show that after antibody immobilization, the wavelength increases by 60 pm and the fringe contrast decreases by about 1 dB. And for antigen binding, the wavelength shows an obvious increase by 190 pm and the fringe contrast decreases by 2.15 dB. The wavelength shift during the antibody-antigen binding process conforms to the immune reaction kinetics. With the advantages of compact structure, good performance, capacity for real-time and label-free detection, easy fabrication, and low cost, the proposed optical fiber FPI sensor shows wide application prospect in label-free immunosensing.

Published
2016
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19. Optical fibre Fabry–Perot relative humidity sensor based on HCPCF and chitosan film

Author

Mingshun Jiang, Qingmei Sui, Xiaohui Liu, and Geng Xiangyi

Subjects
Materials science, Optical fiber, business.industry, 010401 analytical chemistry, Single-mode optical fiber, 01 natural sciences, Atomic and Molecular Physics, and Optics, 0104 chemical sciences, law.invention, 010309 optics, Wavelength, Interferometry, Optics, Interference (communication), law, 0103 physical sciences, Relative humidity, business, Refractive index, Fabry–Pérot interferometer
Abstract

An optical fibre Fabry–Perot interferometer (FPI) sensor for relative humidity (RH) measurement is proposed. The FPI is formed by splicing a short section of hollow-core photonic crystal fibre(HCPCF) to single mode fibre and covering a chitosan film at the end of HCPCF. The refractive index of chitosan and film thickness will change with ambient RH, leading to the change in the reflected interference spectrum of FPI. RH response of the FPI sensor is analysed theoretically and demonstrated experimentally. It shows nonlinear response to RH values from 35 to 95%RH. The interference fringe shifts to shorter wavelength as RH increases with a maximum sensitivity of 0.28 nm/%RH at high RH level. And the fringe contrast also decreases as RH increases with an available maximum sensitivity of 0.5 dB/%RH. The sensor shows good stability and fast response time less than 1 min. With its advantages of compact structure, good performance, simple and safe fabrication, the proposed optical fibre FPI sensor has gre...

Published
2016
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20. Development of an FBG Sensor Array for Multi-Impact Source Localization on CFRP Structures

Author

Qingmei Sui, Xiaohui Liu, Yaozhang Sai, Geng Xiangyi, Lei Jia, and Mingshun Jiang

Subjects
Materials science, MUSIC algorithm, 02 engineering and technology, lcsh:Chemical technology, Biochemistry, Signal, Article, Analytical Chemistry, fiber Bragg grating, multi-impact localization, Optics, 0203 mechanical engineering, Fiber Bragg grating, Sensor array, Shannon wavelet transform, Demodulation, Multiple signal classification, lcsh:TP1-1185, Electrical and Electronic Engineering, Instrumentation, business.industry, Shannon wavelet, 021001 nanoscience & nanotechnology, Atomic and Molecular Physics, and Optics, 020303 mechanical engineering & transports, Ultrasonic sensor, Development (differential geometry), 0210 nano-technology, business
Abstract

We proposed and studied an impact detection system based on a fiber Bragg grating (FBG) sensor array and multiple signal classification (MUSIC) algorithm to determine the location and the number of low velocity impacts on a carbon fiber-reinforced polymer (CFRP) plate. A FBG linear array, consisting of seven FBG sensors, was used for detecting the ultrasonic signals from impacts. The edge-filter method was employed for signal demodulation. Shannon wavelet transform was used to extract narrow band signals from the impacts. The Gerschgorin disc theorem was used for estimating the number of impacts. We used the MUSIC algorithm to obtain the coordinates of multi-impacts. The impact detection system was tested on a 500 mm × 500 mm × 1.5 mm CFRP plate. The results show that the maximum error and average error of the multi-impacts’ localization are 9.2 mm and 7.4 mm, respectively.

Published
2016

25. Acoustic emission location based on diamond FBG sensor array

Author

隋青美 Sui Qingmei, 肖 航 Xiao Hang, 吕珊珊 Lv Shanshan, 姜明顺 Jiang Mingshun, 张法业 Zhang Faye, 曹玉强 Cao Yuqiang, and 耿湘宜 Geng Xiangyi

Subjects
Materials science, Acoustic emission, Space and Planetary Science, Acoustics, engineering, Aerospace Engineering, Diamond, Electrical and Electronic Engineering, Fbg sensor, engineering.material, Atomic and Molecular Physics, and Optics
Published
2017
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