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According to internal sensor of blast furnace low life and high maintenance costs, proposed an indirect diagnostic method that can take advantage of the upper gearbox vibration sensors to collect data to analyze the lower gear box failure.
Analysis of the data collected by the vibration monitoring system measuring pointA2, the time-domain waveform is shown in Fig. 2.
Carry out the wavelet noise reduction processing for the original signal.
To electe the best results of the noise reduction effect, signal-to-noise ratio as the main criteria, take the largest group of the signal-to-noise ratio as the final noise reduction results.
Conclusion Diagnosis of low speed and heavy equipment, in particular the fault diagnosis of vibration signal of the blast furnace gearbox, low-speed parts of the lower drive chain failure will cause the superior drive train high-speed parts transmission instability, the data collected in the upper gearbox high-speed part of the measuring point will arise in fault characteristic frequency of the low-speed parts.

We should take full consideration to energy-saving emission reduction in the design of the projects, such as the utilization of solar energy，the use of lightweight thermal insulation wall, the energy-saving windows and doors, the walls and roof greening, etc.
So, promoting the development of green low carbon building is the major measurement to achieve energy-saving emission reduction, protect environment, improve people's livelihood, promote the healthy development of urbanization, and promote the progress of science and technology in the construction industry.
According to statistics data, about 50% of the energy was used in construction industry from all over the world, at the same time humans from the natural world generates 50% of its raw material is used to build the building and affiliated facilities thereof.
Conclusion Developing the low carbon building, advocating energy-saving emission reduction, it should not be only a slogan, but also need to be put into practice.
References [1] Department of China Academy of Sciences.Implement green building to promote energy-saving emission reduction and to improve the living environment[J] .CAS Members & Academic Divisions ,2011(4) [2]Baoxing LIU.

Conditions of the surrounding environment is improved .The data show that after incineration, the volume of sludge can be reduced up to 90%[[]Zhou Xiahai .
Meanwhile researchers also apply multiple technologies to achieve better reduction effect of sludge.
Sludge reduction effect can reach 50% to 80%.
So it formed a sludge reduction process the effect of OSA.
So it can separate the cellular catabolism and anabolism of mutual, accomplishing the sludge reduction [[]Zhong Xianbo,Ye Fenxia. study on the reduction technique of remaining sludge Aerobic - precipitation - hypoxia (OSA), Environmental science, 2006，24（6）：26-28. ].

The sub-micrometre size platinum particles via chemical reduction of hexachloroplatinic acid in aqueous solution was investigated by UV-Visible Spectroscopy, Transmission Electron Microscopy, X-ray diffraction and FTIR Spectroscopy.
The most popular route to prepare Pt particles is chemical liquid phase reduction of Pt salts [8,9].
Hydrazine hydrate [10], sodium citrate [11], and ascorbic acid [12-14] are often used as reduction.
The mechanism of the protection of PVP-K30 by which was used for stabilizing the platinum particles via chemical reduction of N2H4·H2O in aqueous solution was investigated by UV-Visible Spectroscopy (TU1901) and FTIR Spectroscopy (TENSOR27).
The electron diffraction pattern of the particles indicating that they were composed of platinum metal for the peaks position were consistent with the crystal datum of the platinum phase [18] and the significant broad peaks for such large particles provided an indication that the metal particles may be assemblies of much small Pt entities.

From the processing technological point of view, the shape and distribution of inner porosity of the powders agglomerates are of fundamental importance due to their effects on the reaction rates involving the present phases taking place into the reduction stage.
The importance of porosity within this material on the kinetics reduction can be explained not only by the interfacial adsorption of the gaseous reducing agent with the solid metal oxides present in the structure but mainly due to the heat transfer within the pores coupled with gas flows [5].
This process was accomplished until a considerable reduction in the microhardness diagonal penetration and then, a new microhardness near of the last field is carried out.
The 3-D serial section reconstruction microscopy studies were carried out according to the following steps: I) sequential two-dimensional images data acquisition of the studied field, II) alignment of the digital images; III) rendering and visualization of porous structure observed.
Some difficulties were observed during the metallographic characterization due to the softness of the agglomerate that can cause inaccurate results, however with careful control in the polishing step this shortcoming could be avoided and good images were obtained after continuous refinement of the data image processing.

Data augmentation is a technique that enables the diversity of data applied to training dataset images in order to prevent network from overfitting and memorizing the exact details from very limited number of datasets [6, 13,14] by randomly rotation (horizontally or vertically flipped).
From the result we can confirm that data augmentation can help network get a small increase of their accuracy by enabling diversity of training data and the classification accuracy obtained from BSE images higher than SE images.
Training progress of the best approach, DenseNet201 with data augmentation, shows the plot of accuracy curve of and loss curve against iteration.
Confusion matrix of the best approach, DenseNet201 with data augmentation, shows the number of samples for each class predicted by system.
A survey on Image Data Augmentation for Deep Learning, Journal of Big Data, Vol. 6 no.60 (2019).

Statistical Design of Experiments (DOE) Statistical design of experiments (DOE) is a widely known experimentation technique used in such cases, where experiments are carried out to determine the effect of an independent variable on a dependent variable and the relationship between them, using a regression model based on the experimental data.
Fig. 3(a, b) show the Normal Probability plot and Pareto chart of the standardized effects for the hardness data having a confidence level of 95%.
Fig. 3 (c, d) show the main effects plot for the mean values of hardness (HB) data in terms of Cold work prior SHT (CWBSHT), Cold work after SHT (CWASHT), Pre-aging Temperature (PA T0C), Pre-aging time (PA t h), Aging temperature (AT0C), Aging time (At h), Cold work after Aging treatment (CWAAT), Annealing Temperature (An.T0C) and Annealing Time (An.t min).
(2) (a) (b) (c) (d) (e) (f) Fig. 3 Factorial and ANOVA Plots (main effect plot and interaction effect plot) (a, b) Normal Probability plot and Pareto chart of the standardized effects for the hardness data having a confidence level of 95%.
(e, f) Interaction plot for the mean values of hardness (HV) data.

Capacitance data pre and post metallization annealing (PMA) at a selected range of temperatures.
The distorted curves in the unannealed C-V data suggest the existence of fixed oxide charge (Qf).
Corresponding behaviour is also observed in the conductance data shown as a function of temperature in Figure 4.
G/ω-V data for the unannealed sample.
The corresponding G/ω data after annealing measured at 1MHz is shown in figure 5.

The media plan data are less precise / less satisfying customer.
Lack of data management on the process of media plan.
Data collection for this study analysis using primary and secondary data.
After primary and secondary data collected, data will be processed by regression analysis and the validity and reliability.
Management data is stored neatly and safely.

Throughout the modeling process, experimental flow stress data are used.
Other material data needed for simulations (E-modulus, density, Poisson's Ratio, etc.) are computed using the JMatPro software.
For purposes of employing measured data (which inherently show noise and oversampling) in more sophisticated models or as a CAE input, the raw data are manipulated in EDA software [7].
The new, resampled data can now be used in modeling, for example, as suitable data for finding the Johnson parameters needed for damage simulations.
In general, EDA permits more data from different measurements or simulations to be imported, manipulated and compared in this way.