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Data collection.
Attribute data measurement from the inspection of porous defects shows the measurement data at position 1.
Fig. 5 The result of the attribute data measurement system analysis Variable data measurement system data was collected by the ANOVA method as shown in Fig. 6.
Fig. 6 The result of the variable data measurement system analysis Analyze Phase.
The data set is normally distributed, Versus Fits graph shows the data set has stability of variance, Versus Order is distributed in a certain pattern, and the residuals have similar values.

The process is repeated until all input data points are assigned to an output neuron.
Through using the successful constructed data and design experience within the database, it will exactly acquire the initial data without additional design time and cost.
In step2, the FCM is utilized to cluster the data with the given number of clusters.
Fig. 5 shows the clustering result distribution status of the data in step 2.
Clustering result distribution status of the heat exchanger data in stage 2.

Point Cloud Data Acquisition and �oise Reduction.
Data acquisition is the first step in RE.
The measured data from CMM (model ATOSⅡ400) are saved as a general format and then imported into a data preprocessing program.
In the processing, noise points in point cloud data will be eliminated by noise reduction processes.
Reconstruction of Point Cloud Data.

The EUROCONTROL study found a benefit of 1.6 to 2.3 percent reduction in fuel burn, and 0.7 to 1.0 percent reduction in emissions of nitrogen oxides (NOx) as a result of implementing RVSM.
To this end a large body of computerized flight data recorder [7] (CFDR) data was obtained for a variety of commercial aircraft.
Available CFDR Data Aircraft Type Engine Type A320-214 CFM56-5B4-2 B757-200 RB211-535C B767-300 CF6-80C2 ARJ85 LF 507-1F The CFDR data were quite extensive in the breadth of variables contained, with 103 directly measured or derived values.
This accuracy was previously found to be within ±14% of corresponding NASA data with 1σ confidence [8].
categoryid=702&pagetype=90 [7] Polymeris, J., Swiss Flight Data Monitoring, CH-8058 Zurich

Smartphones consume energy to manage their functions, such as receiving and transmitting data, while access networks and core network equipment consume energy to process these signals.
Methodological Approach The methodological approach adopted for the optimal sizing of the PV/Diesel/Battery hybrid system follows the following steps: · Analysis of energy requirements: Collection and processing of consumption data from the targeted BTS site to develop an electrical consumption load profile over a one-year period
System optimization flowchart Parameters of the various energy components The tables below present the data, parameters and constraints associated with the various components of the system under study.
This solution thus contributes to the objectives of energy transition and greenhouse gas reduction.
Comparison of results obtained with existing site energy data The power supply for the BTS site under study consists of a 20 KVA diesel generator, six 3000W rectifier converters and 24 lead-acid batteries 2V, 1200Ah.

The data acquisition system is composed of four modules: the transducers (the force transducer for the z direction, the force transducer for the x direction consisting of tensometric devices HBM 350XY11) placed on the two rings of the dynamometric table, the signal conditioning modules (MB-38 produced by Keithley Instruments Inc.), the analog – digital conversion device (KPCI 3108, Keithley Instruments Inc.) and a software package that controls the acquisition system and processes the collected data (TestPoint).
The system used to measure the strain and thickness reduction was an optical system Argus by GOM.
Fig. 5 and 6 presents the variation of thickness reduction th for cases Fem9 and Exp9.
Thickness reduction variation th for case Fem9 Fig. 6.
Thus, strain and thickness reduction rise with the decrease of vertical step.

Compression tests then were subjected to various conditions of deformation temperature, reduction ratio and holding time.
A higher reduction ratio also increases the recrystallized regime due to a rise of stored energy for recrystallization.
And the fine precipitations consist mainly of Nb and the coarse precipitations consist mainly of Ti as presented in EDS data of Fig. 5 (c-1) and (f-1).
And EDS data of the precipitations in (c-1) Nb added alloy and (f-1) Ti+Nb added alloy.
Because the samples are deformed at very high temperature over 1180 °C and low reduction under 50 % relative to general cold rolled samples which are deformed at room temperature and high reduction over 70 %.

In order to achieve the objective of this research, the following steps were taken: (1) Preparation/administration of questionnaires; (2) Data collection for a 5-year period (2002-2006); (3) Identification of performance measures from the information produced; (4) Plotting of Graphs and utility curves of the performance measures.
Data Classification and Analysis Data on production and maintenance activities obtained from 2002-2006 are: (1) Production Output; (2) Operating Time; (3) Downtime; (4) Total Maintenance Cost; (5) Planned Maintenance Hours; (6) Total Maintenance Hours.
The following performance measures were identified on the basis of available data: equipment availability, routine service worked, cost of maintenance hour, maintenance cost component, and cost of reduction.
Computation of Performance Measures The performance measures that were computed on the basis of available data obtained from the manufacturing company are (1) Equipment Availability (EA); (2) Emergency Failure Intensity Ratio (EFIR); (3) Cost of Maintenance Hour (CMH); (4) Routine Service Worked (RSW); (5) Maintenance Cost Component (MCC); (6) Cost of Reduction (COR).
Cost of Reduction (COR): The year 2005 shows a relatively good performance of COR.

Dynamic predictive maintenance model based on data-driven machinery prognostics approach W.Z.
In this paper, a data-driven machinery prognostics approach is developed to describe machine degradation.
First, as the collected monitoring data usually contains multidimensional data sets, feature extraction is applied to obtain dominant information.
Here, and could be deduced from the history maintenance data of the system.
Comparison between the actual data and the estimated data of the 500 sets of samples In the predictive maintenance model, the related cost factors are: and .

In this study, we describe a principle for the reduction of blanking vibration when using a servo press, and we propose an active vibration control method for the reduction of blanking vibration.
The purpose of this study is the reduction of blanking vibration using a servo press.
We determined the position of the counter pulse based on the data of the try blanking.
The parameters of a dynamics model were determined as follows instead of the data of an experimental machine: ms=2500kg，mc=600kg，kc=454×106N/m，ks=2.5×106N/m，cs=119×102N･s/m，cc=328×103 N･s/m.
Fig. 13　Reduction rate of vibration in blanking by active vibration control.