| 1. |
- Lu, Junjing, et al.
(författare)
-
An improved sectional model to simulate multi-component aerosol dynamics in a containment of pressurized water reactor
- 2021
-
Ingår i: Journal of Aerosol Science. - : Elsevier BV. - 0021-8502 .- 1879-1964. ; 157
-
Tidskriftsartikel (refereegranskat)abstract
- Simulating an evolving aerosol population in a reactor containment is essential for estimating the radioactivity that is possible to leak to the environment. In this study, a sectional model is developed to simulate multi-component aerosol dynamics in the containment during severe ac-cidents of a pressurized water reactor by improving the widely used MAEROS (Multicomponent AEROSol) model. An important advantage of the improved model is its simplified calculation method by introducing a series of correction factors to the equation coefficients when the thermal boundary conditions and the aerosol particle density in the containment change continuously. In addition, the restriction of the maximum section number in the MAEROS model is removed. The reliability of the model is validated against four analytical solutions and three sets of test data. Moreover, the improvements in the model are also proven to be necessary to effectively capture the influences of thermal boundary conditions and aerosol particle density on aerosol dynamics.
|
|
| 2. |
- He, Zhiyi, et al.
(författare)
-
Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples
- 2020
-
Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 191
-
Tidskriftsartikel (refereegranskat)abstract
- Lack of typical fault samples remains a huge challenge for intelligent fault diagnosis of gearbox. In this paper, a novel approach named deep transfer multi-wavelet auto-encoder is presented for gearbox intelligent fault diagnosis with few training samples. Firstly, new-type deep multi-wavelet auto-encoder is designed for learning important features of the collected vibration signals of gearbox. Secondly, high-quality auxiliary samples are selected based on similarity measure to well pre-train a source model sharing similar characteristics with the target domain. Thirdly, parameter knowledge acquired from the source model is transferred to target model using very few target training samples. Transfer diagnosis cases for different fault severities and compound faults of gearbox confirm the feasibility of the proposed approach even if the working conditions have significant changes.
|
|
| 3. |
- He, Zhiyi, et al.
(författare)
-
Kernel flexible and displaceable convex hull based tensor machine for gearbox fault intelligent diagnosis with multi-source signals
- 2020
-
Ingår i: Measurement. - : Elsevier. - 0263-2241 .- 1873-412X. ; 163
-
Tidskriftsartikel (refereegranskat)abstract
- The methods based on traditional pattern recognition and deep learning have been successfully applied in gearbox intelligent diagnosis. However, traditional pattern recognition methods cannot directly classify feature tensors of multi-source signals, and deep learning networks hardly handle the classification of small samples. Therefore, for the gearbox intelligent diagnosis with multi-source signals, a novel tensor classifier called kernel flexible and displaceable convex hull based tensor machine (KFDCH-TM) is proposed. In KFDCH-TM, the kernel flexible and displaceable convex hull of tensor samples in tensor feature space is defined firstly. Then, an optimal separating hyper-plane between two kernel flexible and displaceable convex hulls is constructed. Meanwhile, feature tensors extracted from multi-source signals through wavelet packet transform (WPT) are used to diagnose gearbox fault by KFDCH-TM. The results of two cases demonstrate that KFDCH-TM can effectively identify gearbox fault with multi-source signals and has better robustness.
|
|
| 4. |
- Zhiyi, He, et al.
(författare)
-
Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder
- 2020
-
Ingår i: Measurement. - : Elsevier. - 0263-2241 .- 1873-412X. ; 152
-
Tidskriftsartikel (refereegranskat)abstract
- The collected vibration data with labeled information from bearing is far insufficient in engineering practice, which is challenging for training an intelligent diagnosis model. For this purpose, enhanced deep transfer auto-encoder is proposed for fault diagnosis of bearing installed in different machines. First, scaled exponential linear unit is used to improve the quality of the mapped vibration data collected from bearing. Second, nonnegative constraint is adopted for modifying the loss function to improve reconstruction effect. Then, the parameter knowledge of the well-trained source model is transferred to the target model. Finally, target training samples with limited labeled information are adopted for fine-tuning the target model to match the characteristics of the target testing samples. The proposed approach is applied for analyzing the measured vibration signals of bearings installed in different machines. The analysis results show that the proposed approach holds better transfer diagnosis performance compared with the existing approaches.
|
|
