Performance Analysis and Enhancement of Deep Convolutional Neural Network - Application to Gearbox Condition Monitoring

Convolutional neural network has been widely investigated for machinery condition monitoring, but its performance is highly affected by the learning of input signal representation and model structure. To address these issues, this paper presents a comprehensive deep convolutional neural network (DCNN) based condition monitoring framework to improve model performance. First, various signal representation techniques are investigated for better feature learning of the DCNN model by transforming the time series signal into different domains, such as the frequency domain, the time–frequency domain, and the reconstructed phase space. Next, the DCNN model is customized by taking into account the dimension of model, the depth of layers, and the convolutional kernel functions. The model parameters are then optimized by a mini-batch stochastic gradient descendent algorithm. Experimental studies on a gearbox test rig are utilized to evaluate the effectiveness of presented DCNN models, and the results show that the one-dimensional DCNN model with a frequency domain input outperforms the others in terms of fault classification accuracy and computational efficiency. Finally, the guidelines for choosing appropriate signal representation techniques and DCNN model structures are comprehensively discussed for machinery condition monitoring

Locally-Enriched Cross-Reconstruction for Few-Shot Fine-Grained Image Classification

Few-shot fine-grained image classification has attracted considerable attention in recent years for its realistic setting to imitate how humans conduct recognition tasks. Metric-based few-shot classifiers have achieved high accuracies. However, their metric function usually requires two arguments of vectors, while transforming or reshaping three-dimensional feature maps to vectors can result in loss of spatial information. Image reconstruction is thus involved to retain more appearance details: the test images are reconstructed by different classes and then classified to the one with the smallest reconstruction error. However, discriminative local information, vital to distinguish sub-categories in fine-grained images with high similarities, is not well elaborated when only the base features from a usual embedding module are adopted for reconstruction. Hence, we propose the novel local content-enriched cross-reconstruction network (LCCRN) for few-shot fine-grained classification. In LCCRN, we design two new modules: the local content-enriched module (LCEM) to learn the discriminative local features, and the cross-reconstruction module (CRM) to fully engage the local features with the appearance details obtained from a separate embedding module. The classification score is calculated based on the weighted sum of reconstruction errors of the cross-reconstruction tasks, with weights learnt from the training process. Extensive experiments on four fine-grained datasets showcase the superior classification performance of LCCRN compared with the state-of-the-art few-shot classification methods. Codes are available at: https://github.com/lutsong/LCCRN

Structural, magnetic and thermal properties of one-dimensional CoFe2O4 microtubes

One-dimensional CoFe2O4 microtubes have been prepared via a simple template-assembled sol–gel method. Influence of calcination temperature on structural and magnetic properties, heat capacity and specific heating rate under radiofrequency field 295 kHz was studied. A CoFe2O4 spinel was the main phase in all samples. As the calcination temperature increased, the average crystal size increased from 34.1 to 168 nm and the specific surface area decreased from 85.7 to 8.5 m2 g−1. When calcined at 1073 K, porous microtubes with a narrow size distribution in the range between 2.0 and 2.5 μm and a length to diameter ratio exceeding 20 were obtained. The heat capacity of the microtubes calcined at 973 K was 140.81 J mol−1 K−1 at 395 K, being close to the theoretic value. The sample calcined at 973 K showed highest rate of 0.293 K s−1 mg−1

Controllable synthesis of one-dimensional isolated Ni 0.5 Zn 0.5 Fe 2 O 4 microtubes for application as catalyst support in RF heated reactors

One-dimensional isolated Ni0.5Zn0.5Fe2O4 microtubes have been prepared via a template assisted sol–gel method. Temperature dependence of the structural and magnetic properties was studied via XRD, N2 adsorption, SEM, TEM, and VSM. An increase in calcination temperature from 873 to 1273 K caused a decrease in the specific surface area from 80.7 to 17.0 m2/g due to an increase of the grain size from 25.3 to 112 nm. All samples demonstrated anomalous coercivity behavior due to mechanical stresses acting on their domain walls. The porous microtubes calcined at 1073 K have a mean external diameter of 3.7 μm with a length-to-diameter ratio exceeding 12. The microtubes calcined at 973 K have the highest coercivity of 88.1 Oe and demonstrated the largest specific heating rate of 4.36 W/g in a radiofrequency field at 295 kHz

Effects of acid hydrolysis waste liquid recycle on preparation of microcrystalline cellulose

Large amounts of acidic waste are produced on the industrial scale during hydrolysis of partially amorphous cellulose to produce microcrystalline cellulose (MCC). The essential disposal and treatment of this highly acidic liquid wastes the acid feedstock and increases the production cost. To maximize the use of acid without sacrificing the MCC product quality, this project reports a successful attempt to recycle the acid hydrolysis waste liquid, focusing on the impact of waste recycling on MCC morphology and reducing sugar in the hydrolysate. The results showed that when the waste liquid is recycled 1-5 times, no metal accumulation occurred while cellulose particles remained intact, maintaining their shape and size. Their extent of crystallinity remained nearly constant, even increasing slightly with up to three cycles. The concentration of reducing sugar showed growth when recycling the waste liquid up to three times, although not quite to the levels that would allow for its cost-effective fermentation. The acid amount to be added at the start of each cycle was near 50% of that used on the first stage

Anomalous superconducting proximity effect of planar Pb-RhPb2 heterojunctions in the clean limit

Interest in superconducting proximity effect has been revived by the exploitation of Andreev states and by the possible emergence of Majorana bound states at the interface. Spectroscopy of these states has been so far restricted to just a handful of superconductor-metal systems in the diffusion regime, whereas reports in otherwise clean superconductor-superconductor heterojunctions are scarce. Here, we realize molecular beam epitaxy growth of atomically sharp planar heterojunctions between Pb and a topological superconductor candidate RhPb2 that allows us to spectroscopically image the proximity effect in the clean limit. The measured energy spectra of RhPb2 vary with the spatial separation from proximal Pb, and exhibit unusual modifications in the pairing gap structure and size that extend over a distance far beyond the coherence length. This anomalously long-range proximity (LRP) effect breaks the rotational symmetry of Cooper pair potential in real space and largely deforms the Abrikosov vortex cores. Our work opens promising avenues for fundamental studies of the Andreev physics and extraordinary states in clean superconducting heterojunctions.Comment: 8 pages, 4 figure

Composition and Performance of Nanostructured Zirconium Titanium Conversion Coating on Aluminum-Magnesium Alloys

Nanostructured conversion coating of Al-Mg alloy was obtained via the surface treatment with zirconium titanium salt solution at 25°C for 10 min. The zirconium titanium salt solution is composed of tannic acid 1.00 g·L−1, K2ZrF6 0.75 g·L−1, NaF 1.25 g·L−1, MgSO4 1.0 g/L, and tetra-n-butyl titanate (TBT) 0.08 g·L−1. X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared spectrum (FT-IR) were used to characterize the composition and structure of the obtained conversion coating. The morphology of the conversion coating was obtained by atomic force microscopy (AFM) and scanning electron microscopy (SEM). Results exhibit that the zirconium titanium salt conversion coating of Al-Mg alloy contains Ti, Zr, Al, F, O, Mg, C, Na, and so on. The conversion coating with nm level thickness is smooth, uniform, and compact. Corrosion resistance of conversion coating was evaluated in the 3.5 wt.% NaCl electrolyte through polarization curves and electrochemical impedance spectrum (EIS). Self-corrosion current density on the nanostructured conversion coating of Al-Mg alloy is 9.7×10-8A·cm-2, which is only 2% of that on the untreated aluminum-magnesium alloy. This result indicates that the corrosion resistance of the conversion coating is improved markedly after chemical conversion treatment