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Data structuration

Supervision LAB / ERPPLC Fichiers Excel

modélisation

extraction

automatisation

validation

Data extraction

Data restitution

Data Transfert

Data Transfert

PLC

Supervision

Excel

LAB / ERP

files.mybraincube.com

Convert into cs

v

(transfo

_xls_csv

)

IP transfert

SFTP on port 22 or 443

Firewall

Your jailled folder

All csv files ready to

sent

All the tool you need, IP Transfert, Transfo_xl_csv and this presentation are available on: http://www.ipleanware.com/dl/

Data restitution Real signal

2 hours

Valu

esReal variable variations

For real signal you might have Several types of

restitution

or

Valu

es

2 hours

Numerical Discrete or binary

Data restitution regular registration

Valu

es

One point every 5 minutes

Valu

es

One point every 5 minutes but filtrered

filter

Take care of the filter because all data are not extracted

High / regular frequency registration (process data)

One point every 5 minutes

Valu

es

Numerical data :

Discrete or binary data :

Valu

es

Value at precise time when status changes

Data restituted by the system between precise data musn’t be extracted

Data restitution non regular registration

Low frequency and/or non regular registration (quality data)

Valu

es

One value every 30 minutes during 2 hours

Valu

es

Data are interporlated between change (dangerous for braincube)

What do we do with these type of extraction :- the average over a period ?- to fill the values between changes?

9:1

7 9:3

2 9:5

4

Separate files for different data typeOrganization

Team…

Process

EventsBreakStop

Clothing …

Genealogy

Quality

Weather

Traceability

Separate files for different frequencies

High frequency registrationFor example : Process data

Low frequency and/or non regular registration For example : Quality data

Event dataWeather dataGenealogy dataOrganization dataTraceability data

Matrix files

Advantage : small files

Disadvantage : extraction might be more complicated

File example with data every 5 minutes:

sample value every 5‘or average value over 5'

Tag name

Values

List files

Advantage : easy extraction

Disadvantage: large files

File example with data every 5’:

Value every 5’ for high frequency registration (or at precise timestamp for non regular registration)

1st Column = Timestamp2nd Column = Tag name3rd Column = Value

Data files Files names : name_of_the_file_YYYYMMDD_HHMMSS

Files format: a) .csv with delimiters like « ; »b) .txt

Example : reporting_shift_20110525_134520 .csv

for the reporting_shift file of the 25th of May 2011 at 13:45:20

It is forbidden to use any special characters (é ~ ç ?...) or spaces

The frequency of extraction will be defined with production people.High frequency (ie: minute) allows a real-time jobs analysis

Mandatory No special characters or space in file names A file per type of data (process data, events, …) Each type of files contain always the same tags Never change the date format in the files You need to know if filters are applicated on the values before extraction Send files with recovery when all data don’t arrived at the same time (data will be overwrited by the last received)Never Change the file format.If the delimiter is « ; » note that you will need to eliminate the ; in text cells. Validate files over a deined period of time with IP Leanware before to run the historical extraction, this will include:

a) File nameb) File formatc) List of tagsd) Date format in the file