Minghui Lu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Minghui Lu has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Chao Lu
Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchang, China
1 shared publication Chuanyu Lu
Nanchang Hongkong Univ, Key Lab Nondestruct Testing, Minist Educ, Nanchang 330063, Jiangxi, Peoples R China
1 shared publication- Mengwei Liu
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
1 shared publication - Shengchao Mei
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
1 shared publication - Wen Wang
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
1 shared publication - Xueli Liu
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
1 shared publication
Latest Publications
- 1 April 2021
- Published by British Institute of Non-Destructive Testing (BINDT) in Insight - Non-Destructive Testing and Condition Monitoring
- Vol. 63 (4) , 219-228
Ultrasonic feature imaging of a multi-layered structure beyond a thin, highly reflective layer using a convolutional neural network
A helicopter propeller is a kind of multi-layered composite material bonding structure. Ensuring that composite structures are free from defects can reduce the risk of in-service failure and hence improve safety. As a common non-destructive testing (NDT) technology, ultrasonic testing is often used in the inspection of composite structures. However, a composite structure made of multiple thin-layer materials bonded together can cause a serious aliasing problem for echo signals when inspecting with ultrasound. In this study, the frequency-domain characteristics of an aliasing echo signal were analysed using the spectrum of the acoustic pressure reflection coefficient. Furthermore, the time-frequency joint analysis results of the echo signal were obtained using a continuous wavelet transform. Finally, the obtained time-frequency features of the echo signal were used to classify and image with a convolutional neural network (CNN). The results revealed that, as opposed to the direct imaging of the time- and frequency-domain features, the time-frequency wavelet map of a thin-walled multi-layered structure that was classified and imaged with a CNN exhibited greater clarity and better defect recognition ability. In addition, the training time of the CNN was 17 s and the classification accuracy of the verification set was high, reaching 97.8%.
- 1 April 2021
- Published by British Institute of Non-Destructive Testing (BINDT) in Insight - Non-Destructive Testing and Condition Monitoring
- Vol. 63 (4) , 219-228
Ultrasonic feature imaging of a multi-layered structure beyond a thin, highly reflective layer using a convolutional neural network
A helicopter propeller is a kind of multi-layered composite material bonding structure. Ensuring that composite structures are free from defects can reduce the risk of in-service failure and hence improve safety. As a common non-destructive testing (NDT) technology, ultrasonic testing is often used in the inspection of composite structures. However, a composite structure made of multiple thin-layer materials bonded together can cause a serious aliasing problem for echo signals when inspecting with ultrasound. In this study, the frequency-domain characteristics of an aliasing echo signal were analysed using the spectrum of the acoustic pressure reflection coefficient. Furthermore, the time-frequency joint analysis results of the echo signal were obtained using a continuous wavelet transform. Finally, the obtained time-frequency features of the echo signal were used to classify and image with a convolutional neural network (CNN). The results revealed that, as opposed to the direct imaging of the time- and frequency-domain features, the time-frequency wavelet map of a thin-walled multi-layered structure that was classified and imaged with a CNN exhibited greater clarity and better defect recognition ability. In addition, the training time of the CNN was 17 s and the classification accuracy of the verification set was high, reaching 97.8%.
- Open Access
Development of a High Stability Pd-Ni Alloy Thin-Film Coated SAW Device for Sensing Hydrogen
A Pd-Ni alloy thin-film coated surface acoustic wave (SAW) device is proposed for sensing hydrogen. The Pd-Ni thin-film was sputtered onto the SAW propagation path of a SAW device with a delay line pattern to build the chip-sized hydrogen sensor. The prepared sensor chip was characterized by employing a differential oscillation loop. The effect of the Pd-Ni film thickness on sensing performance was also evaluated, and optimal parameters were determined, allowing for fast response and high sensitivity. Excellent working stability (detection error of 3.7% in half a year), high sensitivity (21.3 kHz/%), and fast response (less than 10 s) were achieved from the 40 nm Pd-Ni alloy thin-film coated sensing device.
Open AccessDevelopment of a High Stability Pd-Ni Alloy Thin-Film Coated SAW Device for Sensing Hydrogen
A Pd-Ni alloy thin-film coated surface acoustic wave (SAW) device is proposed for sensing hydrogen. The Pd-Ni thin-film was sputtered onto the SAW propagation path of a SAW device with a delay line pattern to build the chip-sized hydrogen sensor. The prepared sensor chip was characterized by employing a differential oscillation loop. The effect of the Pd-Ni film thickness on sensing performance was also evaluated, and optimal parameters were determined, allowing for fast response and high sensitivity. Excellent working stability (detection error of 3.7% in half a year), high sensitivity (21.3 kHz/%), and fast response (less than 10 s) were achieved from the 40 nm Pd-Ni alloy thin-film coated sensing device.