create knowledge from your best experiences

1. create knowledge from your best experiences

2. Data structuration modélisation Data restitution extraction PLC Supervision Fichiers Excel LAB / ERP validation Data extraction Data Transfert automatisation

3. Data Transfert files.mybraincube.com IP transfert Yourjailledfolder All csv files ready to sent Firewall SFTP on port 22 or 443 PLC Supervision Convertinto csv (transfo_xls_csv) Excel LAB / ERP All the toolyouneed, IP Transfert, Transfo_xl_csv and thispresentation are availableon: http://www.ipleanware.com/dl/

4. Data restitution Real signal Discrete or binary Numerical Values Values or 2 hours 2 hours Real variable variations For real signal youmight have Several types of restitution

5. Data restitution regular registration High / regularfrequency registration (process data) Numerical data : Values filter Values One point every 5 minutes One point every 5 minutes but filtrered Take care of the filterbecause all data are not extracted Discrete or binary data : Values One point every 5 minutes

6. Data restitution non regular registration Lowfrequency and/or non regular registration (quality data) Values Values 9:17 9:32 9:54 One value every 30 minutes during 2 hours Value atprecise time whenstatus changes Values • What do we do withthese type of extraction : • the average over a period ? • to fill the values between changes? Data are interporlatedbetween change (dangerous for braincube) Data restituted by the system betweenprecise data musn’tbeextracted

7. Separate files for different data type Organization Team … Process Genealogy Quality Traceability Events Break Stop Clothing … Weather

8. Separate files for different frequencies • High frequency registration • For example : Process data • Lowfrequency and/or non regular registration • For example : Quality data • Event data • Weather data • Genealogy data • Organization data • Traceability data

9. Matrix files • Advantage : small files • Disadvantage : extraction mightbe more complicated • File examplewith data every 5 minutes: Tag name sample value every 5‘ or average value over 5' Values

10. List files • Advantage : easy extraction • Disadvantage: large files • File examplewith data every 5’: 1st Column = Timestamp 2ndColumn = Tag name 3rdColumn = Value Value every 5’ for highfrequency registration (or atprecisetimestamp for non regular registration)

11. Data files • Files names : name_of_the_file_YYYYMMDD_HHMMSS • Files format: • .csv withdelimiterslike « ; » • .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 willbedefinedwith production people. High frequency (ie: minute) allows a real-time jobs analysis

12. Mandatory • No special characters or space in file names • A file per type of data (process data, events, …) • Each type of files containalways 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 delimiteris « ; » note thatyouwillneed to eliminate the ; in textcells. • Validate files over a deinedperiod of time with IP Leanwarebefore to run the historical extraction, thiswillinclude: • File name • File format • List of tags • Date format in the file