Automatically Detect. Using Data Analytics to. matters in the data from. Automatically find what. Equipment Systems

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1 Using Data Analytics to Automatically Detect Issues, Trends, Faults and Anomalies in Equipment Systems Automatically find what matters in the data from equipment systemss and smart devices Remote December 2013

2 Our world contains billions of smart devices

3 Producing vast qua ntities of data every second

4 To create value from our devices we need to find what matters in that data

5 By detecting patt terns that represent deviations, anomalies, faults, and opportunitie es for savings

6 Too Much Data!!!

7 Analytics is the Key Analytics software autom matically looks for issues (things that matter) in our data. Equipment faults, deviations from expected performance, actual results vs goals or benchmarks, etc Unlike efficiency measures that involve the installation of major capital equipment, analytics works with the data available from existing sources Relatively easy to add to what we have

8 The Bigger Picture: Analytics is changing our world It can change the way we manage our systems too!

9 Gather data and find patterns that represent the issues that matter Easy to get started what data do you have? Device data Facility data Utility data Weather data Production data Connect to available data sources Aggregate e and normalize available data Detect patterns that represent issues

10 Automatically fi ind patterns and correlations in device data

11 The Result: Know what your systems are really doing Automatically scans your data to find what matters Automatically generates views on issues detected Convert expert domain knowledge to rules your value continues to build Explore relationships and correlations lti you would not have otherwise seen The Spark detail page shows everything related to the occurrence of an issue

12 Examples: Analytics in Action

13 Vehicle/Fleet Tracking Concept: Collect and analyze vehicl GPS data to determine operational issues Examples of potential analytics: Vehicles traveling outside of fencepost area Vehicles stopped for tooo long Speed in excess of regula ations e

14 Vehicle Tracking Example

15 Security Concept: Collect and analyze security system data for patterns that representthreats threats or improp per conditions Analytic example: Combine video analytic detected events with other dt data. Example: a bag lft left unattended d at a door. Expected response is dispatch of guard to area to confirm Analytic looks for correlation of responses to events, response rates, time to re esponse etc.

16 Cold Chain Management Concept: Need to insure that food, drugs (and other perishables) have been maintained at correct temperatures if not they must be discarded by regulation Monitor temps, compressors, open doors times, etc. Track patterns and magnitude of deviation faults time is a major factor This is a growing garea of regula tory compliance Analytics simplifies and reduces cost of analysis

17 Tracking Data Usage in M2M Applications Concept: Cell phone connected devices report data back to a central server. Each site needs to have a data plan. Data plans are sized based on initial expectations Buying more data capacity than needed can be very expensive Going over your plan allotmentt can be even more expensive But manually analyzing usage across thousand of devices is very difficult and expensive Analytics enabled a partner to analyze actual data needs and choose a more cost effective plan

18 Analytics vs Alarms vs An alarm is when you are on the gurney inthe ER Analytics are the lab tests you take every year to stay out of the ER Alarms require that you fully understand the issue ahead of time Analytics find patterns & issues you couldn t have foreseen Controller based alarms only deal with control system data Analytics combine operational, energy, production, facility and corporate data to show patterns and correlations across your portfolio of device data Correlation examples equipment type, age, material, vendor, weather effects, production factors, etc Analytics replace the majority of non productive alarms with insights that explain what is happening an nd why

19 Summary Analytics Value Proposition You can t control what you don t measure Analytics enables us to know how your equipment systems are actually operating Easy to get started. W hat data do you have? Use available data real time and/or batch Its time to generate value from our data!

20 Find wha t matters kf foundry.com