Metrology From Smart Probe to Smart Device

Transcription

1 Metrology From Smart Probe to Smart Device By Michael Brunzlik Smart IT Solutions For Your Business

2 BIO Formal Background in Electrical Engineering. Early technology adopter using Microprocessors for post measurement data presentation Real word interfacing Finished training at ESKOM in Test and Measurement Real time Data Acquisition for first of its kind 3-phase harmonic power measurement in the ESKOM distribution network. No prior concept of Quality of Power existed! International peer review towards results being legaly ready

3 BIO (Cont.) Process Control Projects Eskom Tower Testing Station Real-time hydraulic ram control Recording of Mechanical pipe stresses at the Palmiet River Pump Storage Energy Scheme Data collection from PLC s for real-time Closed Loop software model at Iscor s Corex Stainless Steel plant built by Voest Alpina RBCT bespoke solution for total plant control from high level graphical user interface pre MS Windows! Laboratory Instrument interfacing for data collection and management at Water Affairs Progression over 20 years to with LIMS and Monitoring

4 The Business Case Direct costs of Data are obvious Capital Investment Human component of training Operational costs Master data quality importance Initial cost of ensuring clean master data On-going costs of master data maintenance Poor master data quality leads to faulty managerial decision making and raised inefficiency costs ABB metric: Cost of Poor Quality (CoPQ)

5 ABB s CoPQ Metric

6 Four Dimensions of Data Quality (Ballou and Pazer ) accuracy, timeliness, completeness, and consistency.

7 Ranking of Data Dimensions Dimension Ranking (from most important) Accuracy 25 Reliability 22 Timeliness 19 Relevance 16 Completeness 15 Currency 9 Consistency 8 Flexibility 5 Precision 5 Format 4 Interpretability 4 Content 3 Efficiency 3 Importance 3 Sufficiency 3 Usableness 3 Usefulness 3 Clarity 2 Comparability 2 Conciseness 2 Freedom from bias 2 Informativeness 2 Level of detail 2 Quantitativeness 2 Scope 2 Understandability 2 Wand and Wang (1996)

8 Review of sources for above business case The Business Case for Building Efficiency.pdf Hidden costs of poor quality.docx Costs associated with poor data quality.pdf

9 Example: Bulk Egg Bulk Egg Pulp provider to catering and food manufacturing Egg Separating Pasteurising Extremely temperature sensitive process Below 62.5 Deg. C. and the bugs don t die! Above 64 Deg. C. and the egg starts cooking

10 Eggs (continued ) SANAS Accredited calibration entity called in as a routine 6 monthly check up Monitoring probe 1.8 Deg. C. too high Escalated problem to supplier Impossible declares supplier Bugs picked up during a microbiological test Whole batch recalled. Warnings issued!

11 Eggs (continued ) SANAS Accredited calibration entity #2 called in to verify first entity results Uses live display on probe web interface from mobile phone via the Internet Declares probe only has 0.2 Deg. C. error! Pasteuriser now operates at correct temperature No more bugs (nor any cooked egg!)

12 Smart Probes Developing trend is that probes are getting smarter Built-in Web server Provides ubiquitous access from any browser Realtime access to data integration in the probe Feeds into SCADA, Monitoring, Databases Using industry standard SNMP protocol

13 Temperature probe innovations Transistor Band-Gap phenomenon Directly proportional to temperature Highly stable Measurements against reference on same substrate Factory adjusted (calibrated) Accuracy: better than 0.5 C Polynomial curve fitting: better then 0.1 C

14 Benefits of probe innovations Cheaper to produce than ever before Installation complexity reduced No more drift No limits to access to data Access to data on the probe from anywhere on the globe (via Internet) Event driven data push via SNMP &

15 Pitfalls of probe innovation Data delivery channels not guaranteed That s it! and this problem has elegant solutions

16 Open Source Software Stack Open Source is the collaboration of many millions of people contributing to projects ~65% of all Web Servers ~80%+ of all servers Network Security Databases Java Google s Android (e.g Samsung Galaxy, etc.)

17 Recommended Software Cacti Nagios

18 Metrology and Open Source Cacti ( What is it? Multiple diverse data sources Versatile graph generator User management Template based

19 What is Cacti? Cacti is a complete frontend graphing tool, it stores all of the necessary information to create graphs and populate them with data in a database. Along with being able to maintain Graphs, Data Sources, and Round Robin Archives in a database, cacti handles the data gathering.

20 Multiple diverse data sources To handle data gathering, you can feed cacti the paths to any external script/command along with any data that the user will need to "fill in", cacti will then gather this data.

21 Versatile graph generator Cacti allows you to create almost any imaginable graph using all of the standard RRDTool (another Open Source package) graph types and consolidation functions. A colour selection area and automatic text padding function also aid in the creation of graphs to make the process easier.

22 User Management Due to the many functions of Cacti, a user based management tool is built in so you can add users and give them rights to certain areas of cacti. This would allow someone to create some users that can change graph parameters, while others can only view graphs. Each user also maintains their own settings when it comes to viewing graphs.

23 Template based Cacti is able to scale to a large number of data sources and graphs through the use of templates. This allows the creation of a single graph or data source template which defines any graph or data source associated with it. Host templates enable you to define the capabilities of a host so cacti can poll it for information upon the addition of a new host.

24 Metrology and Open Source Nagios (

25 Live Nagios Demo Demo of live access to a client site via secure Web access

26 Q & A Thank you.