Multi-Sensor Vibration Signal Based Three-Stage Fault Prediction for Rotating Mechanical Equipment

In order to reduce maintenance costs and avoid safety accidents, it is of great significance to carry out fault prediction to reasonably arrange maintenance plans for rotating mechanical equipment. At present, the relevant research mainly focuses on fault diagnosis and remaining useful life (RUL) predictions, which cannot provide information on the specific health condition and fault types of rotating mechanical equipment in advance. In this paper, a novel three-stage fault prediction method is presented to realize the identification of the degradation period and the type of failure simultaneously. Firstly, based on the vibration signals from multiple sensors, a convolutional neural network (CNN) and long short-term memory (LSTM) network are combined to extract the spatiotemporal features of the degradation period and fault type by means of the cross-entropy loss function. Then, to predict the degradation trend and the type of failure, the attention-bidirectional (Bi)-LSTM network is used as the regression model to predict the future trend of features. Furthermore, the predicted features are given to the support vector classification (SVC) model to identify the specific degradation period and fault type, which can eventually realize a comprehensive fault prediction. Finally, the NSF I/UCR Center for Intelligent Maintenance Systems (IMS) dataset is used to verify the feasibility and efficiency of the proposed fault prediction method.     

夏季东亚副热带反气旋进退活动的非线性机理讨论

采用三维强迫耗散非线性动力学模式,结合夏季东亚副热带地区实际的环流结构和天气特征,对东亚上空副热带反气旋的活动进行了研究和探讨,导出了系统稳定性的位涡拟能判据并据此进行了分析和讨论·所得判据可为分析和预报夏季东亚上空副热带高压的进退变化及相应的东亚天气提供参考依据·     

Anthraquinones from the Aerial Parts of Rubia cordifolia with Their NO Inhibitory and Antibacterial Activities

The present study aimed to identify the composition of the aerial parts of Rubia cordifolia L. A chemical investigation on the EtOAc extracts from the aerial parts of Rubia cordifolia resulted in the isolation of four new anthraquinones, namely Cordifoquinone A–D (1–4), along with 16 known anthraquinones. Their structures were elucidated on the basis of NMR and HR-ESIMS data. All isolates were assessed for their inhibitory effects on NO production in LPS-stimulated RAW 264.7 macrophage cells. Compounds 1, 3 and 10 exhibited significant inhibitory activities with IC₅₀ values of 14.05, 23.48 and 29.23 μmol·L⁻¹, respectively. Their antibacterial activities of four bacteria, Escherichia coli (ATCC 25922), Staphylococcus aureus subsp. aureus (ATCC 29213), Salmonella enterica subsp. enterica (ATCC 14028) and Pseudomonas aeruginosa (ATCC 27853), were also evaluated. Our results indicated that the antibacterial activity of these compounds is inactive.     

Thermal stress reduction of GaAs epitaxial growth on V-groove patterned Si substrates

We investigate the thermal stresses for GaAs layers grown on V-groove patterned Si substrates by the finite-element method. The results show that the thermal stress distribution near the interface in a patterned substrate is nonuniform,which is far different from that in a planar substrate. Comparing with the planar substrate, the thermal stress is significantly reduced for the Ga As layer on the patterned substrate. The effects of the width of the V-groove, the thickness, and the width of the SiO₂ mask on the thermal stress are studied. It is found that the SiO₂ mask and V-groove play a crucial role in the stress of the Ga As layer on Si substrate. The results indicate that when the width of V-groove is 50 nm, the width and the thickness of the SiO₂ mask are both 100 nm, the Ga As layer is subjected to the minimum stress. Furthermore,Comparing with the planar substrate, the average stress of the Ga As epitaxial layer in the growth window region of the patterned substrate is reduced by 90%. These findings are useful in the optimal designing of growing high-quality Ga As films on patterned Si substrates.     

A Method of Domain Dictionary Construction for Electric Vehicles Disassembly

Currently, there is no domain dictionary in the field of electric vehicles disassembly and other domain dictionary construction algorithms do not accurately extract terminology from disassembly text, because the terminology is complex and variable. Herein, the construction of a domain dictionary for the disassembly of electric vehicles is a research work that has important research significance. Extracting high-quality keywords from text and categorizing them widely uses information mining, which is the basis of named entity recognition, relation extraction, knowledge questions and answers and other disassembly domain information recognition and extraction. In this paper, we propose a supervised learning dictionary construction algorithm based on multi-dimensional features that combines different features of extraction candidate keywords from the text of each scientific study. Keywords recognition is regarded as a binary classification problem using the LightGBM model to filter each keyword, and then expand the domain dictionary based on the pointwise mutual information value between keywords and its category. Here, we make use of Chinese disassembly manuals, patents and papers in order to establish a general corpus about the disassembly information and then use our model to mine the disassembly parts, disassembly tools, disassembly methods, disassembly process, and other categories of disassembly keywords. The experiment evidenced that our algorithms can significantly improve extraction and category performance better than traditional algorithms in the disassembly domain. We also investigated the performance algorithms and attempts to describe them. Our work sets a benchmark for domain dictionary construction in the field of disassembly of electric vehicles that is based on the newly developed dataset using a multi-class terminology classification.     

Identification of Biomarker Volatile Organic Compounds Released by Three Stored-Grain Insect Pests in Wheat

Monitoring and early detection of stored-grain insect infestation is essential to implement timely and effective pest management decisions to protect stored grains. We report a reliable analytical procedure based on headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS) to assess stored-grain infestation through the detection of volatile compounds emitted by insects. Four different fibre coatings were assessed; 85 µm CAR/PDMS had optimal efficiency in the extraction of analytes from wheat. The headspace profiles of volatile compounds produced by Tribolium castaneum (Herbst), Rhyzopertha dominica (Fabricius), and Sitophilus granarius (Linnaeus), either alone or with wheat, were compared with those of non-infested wheat grains. Qualitative analysis of chromatograms showed the presence of different volatile compound profiles in wheat with pest infestation compared with the wheat controls. Wheat-specific and insect-specific volatile compounds were identified, including the aggregation pheromones, dominicalure-1 and dominicalure-2, from R. dominica, and benzoquinones homologs from T. castaneum. For the first time, the presence of 3-hydroxy-2-butanone was reported from S. granarius, which might function as an alarm pheromone. These identified candidate biomarker compounds can be utilized in insect surveillance and monitoring in stored grain to safeguard our grain products in future.     

Dynamic crossover in [VIO~(2+)][Tf_2N~-]_2 ionic liquid

Ionic liquids usually behave as fragile liquids,and the temperature dependence of their dynamic properties obeys supper-Arrhenius law.In this work,a dynamic crossover is observed in([VIO²⁺][Tf₂N-]₂) ionic liquid at the temperature of 240-800 K.The diffusion coefficient does not obey a single Arrhenius law or a Vogel-Fulcher-Tammann(VFT) relation,but can be well fitted by three Arrhenius laws or a combination of a VFT relation and an Arrhenius law.The origin of the dynamic crossover is analyzed from correlation,structure,and thermodynamics.Ion gets a stronger backward correlation at a lower temperature,as shown by the fractal dimension of the random walk.The temperature dependence function of fractal dimension,heterogeneity order parameter,and thermodynamic data can be separated into three regions similar to that observed in the diffusion coefficient.The two crossover temperatures observed in the three types of data are almost the same as that in diffusion coefficient fitted by three Arrhenius laws.The results indicate that the dynamic crossover of[VIO2+][Tf2 N-]2 is attributed to the heterogeneous structure when it undergoes cooling.     

A low-threshold and high-power oxide-confined 850-nm AlInGaAs strained quantum-well vertical-cavity surface-emitting laser

A low-threshold and high-power oxide-confined 850-nm AlInGaAs strained quantum-well (QW) vertical-cavity surface-emitting laser (VCSEL) based on an intra-cavity contacted structure is fabricated. A threshold current of 1.5 mA for a 22 μm oxide aperture device is achieved, which corresponds to a threshold current density of 0.395 kA/cm². The peak output optical power reaches 17.5 mW at an injection current of 30 mA at room temperature under pulsed operation. While under continuous-wave (CW) operation, the maximum power attains 10.5 mW. Such a device demonstrates a high characteristic temperature of 327 K within a temperature range from -12℃ to 96℃ and good reliability under a lifetime test. There is almost no decrease of the optical power when the device operates at a current of 5 mA at room temperature under the CW injection current.     

A carbon–LiFePO<Subscript>4</Subscript> nanocomposite as high-performance cathode material for lithium-ion batteries

A cathode material of an electrically conducting carbon–LiFePO₄ nanocomposite is synthesized by wet ball milling and spray drying of precursor powders prior to a solid-state reaction. The structural characterization shows that the composite is composed of LiFePO₄ crystals and 4.8 wt.% amorphous carbon. Galvanostatic charge/discharge measurements indicate that the composite exhibits a superior high energy and high cycling stability. This composite delivers a discharge capacity of 159.1 mAh g⁻¹ at 0.1 C, 150.8 mAh g⁻¹ at 1 C, and 140.1 mAh g⁻¹ at 2 C rate. The capacity retention of 99% is achieved after 200 cycles at 2 C. The 18,650 cylindrical batteries are assembled using the composite as cathode materials and demonstrate the capacity of 1,400 mAh and the capacity retention of 97% after 100 cycles at 1 C. These results reveal that the as-prepared LiFePO₄–carbon composite is one of the promising cathode materials for high-performance, advanced lithium-ion batteries directed to the hybrid electric vehicle and pure electric vehicle markets.