Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

伞帽用高铬铸铁在热强碱腐蚀环境下的磨损分析

针对氧化铝行业中常用的Cr28和Cr20高铬铸铁伞帽在相同工况条件下的磨损机理进行分析,并对比研究了实际生产中两种失效材料的成分、组织及性能。结果表明,伞帽部件在高温强碱腐蚀条件下受到外界冲刷时,磨损量由微切削磨损与变形磨损这两种机制共同决定。含铬量较高的Cr28高铬铸铁,其冲刷和抗腐蚀磨损性能均优于Cr20高铬铸铁。伞帽服役寿命主要受浆料和表层的铸铁材料两大因素影响。两种试验材料经淬火+回火处理后,基体组织中主要为回火马氏体+M₇C₃型碳化物+少量残留奥氏体,其中含铬量较高的Cr28高铬铸铁中共晶碳化物含量更高,且分布更加弥散,其平均硬度值为64.0 HRC,高于Cr20高铬铸铁的60.2 HRC。最终确定Cr28高铬铸铁作为伞帽材质更能满足氧化铝生产及设备检修周期的需要。     

Smart firefighting construction in China: Status,problems, and reflections

“Smart firefighting” construction as a part of the “smart city” has been a concern of the public security and fire agencies at all levels. In this study, the status, problems, and reflections of “smart firefighting” construction in China are discussed. A recent survey indicates that China has launched its smart firefighting construction and initially created a new perspective on its regional smart firefighting work based on three main aspects: intelligent disaster perception by Internet of Things (IoT) construction, intelligent disaster prevention by big data construction, and intelligent disaster disposal by emergency rescue platform construction. However, the current smart firefighting construction in China still has some prominent problems such as the data interconnectivity and normalized management of various platforms, the extensibility of smart firefighting platforms, and the intelligent level of smart firefighting researches, which need to be solved urgently. Therefore, we argue that smart firefighting construction in China should establish data interconnectivity, industrial normalized management, 2D/3D geographic information interaction and extension, high-integration fire protection theory, and many other aspects in the near future and truly realize firefighting visualization and efficient data applications for 4D time space. This study could provide valuable reference for smart firefighting and smart city construction.     

郴州某复杂硫化铅锌矿阶段磨浮试验研究

针对郴州某复杂硫化铅锌矿资源的性质特点,采用铅快速浮选后尾矿再磨再选的阶磨阶选工艺,较好地实现了铅锌的高效浮选回收。研究了磨矿方式、捕收剂和调整剂种类及用量等因素对该矿石浮选的影响,确定了最佳浮选条件,通过小型闭路浮选试验,得到了铅品位60.05%、回收率89.63%的铅精矿和锌品位47.01%、回收率88.14%的锌精矿。     

岩矿稀有金属化学分析技术浅析

分析当前较为常用的岩矿稀有元素分析技术方法,并对几种常见的稀有元素分析方法进行了阐述,希望为我国矿产勘探领域提供一些有价值的参考借鉴。     

Precipitating spinel into precursor glass and its assistance in crystallization

The crystallization phenomena of spinel in CaO-MgO-Al₂O₃-SiO₂-Fe₂O₃ glass have received much attention due to the particular role in preparation of glass-ceramic materials, which represent an effective option to manage hazardous waste. In this study, both preliminary spinel and secondary spinel were precipitated in the precursor glass. The formation of these spinel was meticulously assessed by a combination of X-ray diffractometry and scanning electron microscopy. The structure of the microenvironment in the precursor glass was characterized by Raman spectrums. These advanced techniques highlight the potential for one-step crystallization of the glass. The investigation, which focused on one-step crystallization, demonstrated the growth of pyroxene on spinel accompanying a migration of chromium. The results also show the microstructure of the obtained glass-ceramic was very dependent on the heat-treat temperature. This study not only unambiguously reveals the precipitation mechanisms of spinel but also provides more documentation for one-step crystallization in the glass-ceramics field.     

基于MF-R和AWS密钥管理机制的物联网健康监测大数据分析系统

带有传感器的可穿戴式医疗设备不断生成大量数据，由于数据的复杂性，难以通过处理和分析大数据来找到有价值的决策信息。为了解决这个问题，提出了一种新的物联网体系结构，用于存储和处理医疗应用的可扩展传感器数据（大数据）。所提出的架构主要由两个子架构组成：Meta Fog重定向（MF-R）架构和AWS密钥管理机制。MF-R架构使用Apache Pig和Apache HBase等大数据技术来收集和存储不同传感器设备生成的传感器数据，并利用卡尔曼滤波消除噪声。AWS密钥管理机制使用密钥管理方案，目的是保护云中的数据，防止未经授权的访问。当数据存储在云中时，所提出的系统能够使用随机梯度下降算法和逻辑回归来开发心脏病的预测模型。仿真实验表明，和其他几种算法相比，提出的算法具有更小的误差，且在吞吐量、准确度等方面具有一定的优越性。     

Effect of the cultivation mode on red pigments production from Monascus ruber

In submerged cultures performed in chemically defined fermentation medium containing glucose and glutamate, the growth and production of water‐soluble red pigments and citrinin by the filamentous fungus Monascus ruber were studied under various carbon/nitrogen (C/N) ratios. The specific production of the red pigments was optimal at a glucose/glutamate ratio of about 10 and then steadily decreased at higher C/N ratio. In contrast, the production of the mycotoxin increased with increased C/N with an optimum in the range of 30–45. In a fed‐batch mode, it was also found that the production of pigments was not favoured in fed‐batch mode by feeding the medium with glucose while keeping the C/N ratio lower than 10. This low production likely resulted from concurrent high accumulation of L‐malic acid that was reported to inhibit this production. In contrast, this mode of cultivation was rather favourable for the production of the mycotoxin.     

Environmentally friendly synthesis and photopolymerization of acrylated methyl ricinoleate for biomedical applications

In this study, we aimed to develop an efficient synthesis and photopolymerization of acrylated methyl ricinoleate (AMR) for biomedical applications. During the first step of the synthesis, methyl ricinoleate (MR) and boric acid were esterified via azeotropic distillation in toluene. Afterward, MR–boric acid ester was acrylated with acrylic acid at 165 °C via a boric acid ester acidolysis reaction. The bulk photopolymerization of AMR was performed in the presence of the photoinitiator 2,2-dimethoxy-2-phenyl acetophenone (DMPA) under 365 nm UV irradiation. Even with the use of 0.4% DMPA, a 35% monomer conversion was achieved within 30 min. Moreover, AMR, the plant-oil-based monomer, was also copolymerized with N-isopropyl acrylamide to obtain thermoresponsive hydrogels on the glass surface for biomedical applications. The synthesized materials were characterized by Fourier transform infrared (FTIR) spectroscopy, ¹H-NMR spectroscopy, and thermal characterization via thermogravimetric analysis (TGA) and differential scanning calorimetry techniques. The surfaces were characterized by FTIR and Energy Dispersive X-ray (EDS) spectroscopy. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47969.     

基于SVM的煤与瓦斯突出预测模型及应用

为有效预测矿井内煤与瓦斯突出的危险程度,对其影响因素做了分析与探讨,分别构建了基于粒子群优化算法以及遗传算法支持向量机的煤与瓦斯突出预测模型,并且通过实例对两种模型预测的准确性进行了验证。分别利用单项以及综合指标、BP神经网络以及PSO-SVM模型、GA-SVM模型,对寺河煤矿二号井的突出区域进行预测比较。结果表明,PSO-SVM的预测模型不仅可以在小样本数据中预测出煤与瓦斯突出程度的大小,而且综合预测结果更加精确,其在解决矿井内煤与瓦斯突出的小样本数据中显示出更加强大、通用的性能。