Accelerate Decision Making with Faster Analysis and Data Search. Jon Aldred Director, Prenscia Product Management

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Accelerate Decision Making with

Faster Analysis and Data Search

Jon Aldred

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About Prenscia

Software Brands Training & Education

• Design for reliability • Design for durability • Fatigue theory

• Hands-on software

Prenscia helps engineers deliver durable and reliable products and avoid the cost of unexpected failures.

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Introduction to Aqira

Data Search: finding data on shared drives

nCodeDS: 10 to 100x faster signal processing

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Aqira helps

engineering teams with increasing workloads

of analysis to be more effective and efficient through

easily configured web apps.

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Aqira: Powerful democratization platform for durability engineering

Lab & Test Measurements

Data Sources Co lle ct Simulation Desktop Tools Anal yz e Server-side analysis Web-based access to nCode software & flows

Apps creation for process democratization

License token management

Data search and indexing G

lobal iz e streamed analytics Server Processing Connected Equipment

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Sound familiar?

“Today, more than 50% of the working

time of my team of dynamics experts

is wasted in data archeology and IT

chaos. We would select a supplier that

would enable us to find and use data

more efficiently across departments,

systems and data formats.”

Head of Vibrations and Dynamics measurement department

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• Finding data that has already been acquired to avoid expensive re-testing.

• Avoid wasting time looking for data.

• Connected equipment more common, but sensor data is stored and mostly not used at all.

• Able to handle the volume, variety and velocity of all the data sources.

• Making data accessible to more engineers.

• Not just finding the data, but the ability to gain insights from analytics and therefore derive value through the right decision.

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• Aqira can index data that is accessible to server, for example on network drives.

• Indexed data it is left where it is on network.

• Metadata is extracted from files that are recognized.

• Indexing can be automated using Python API.

• When data is indexed by Aqira, it can then be searched and analyzed by Aqira apps.

Aqira for Data Search

Terabytes of measured data, spreadsheets, images etc

company server network drives

Web browser

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• Data Search component enables app authors to add a search to find data as a part of an App.

• The search is constructed using a “Search Designer” view.

• Search parameters can be defined from designed inputs e.g. Custom Pages.

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• Search Results component enables interactive viewing of results.

• Filters can be used to refine displayed items.

• Measured channels can be directly displayed.

• Subsets of data can be selected for downstream processing.

• Works with files, tests or channels.

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• 180 proving ground data files from a new Sport Utility Vehicle

• 16 channels

• 2 drive types (2WD / 4WD)

• 2 weight conditions (GVW / Unladen)

• Data has been arranged on a shared drive in the following folder structure:

• Vehicle > Model Year > Drive Type > Weight Condition

• Data files are s3t and contain other metadata including a relative damage calculation on the strain channels

Example: Data Search in Aqira

Example data search: • Find data channels • For all 4WD, Unladen

condition vehicles

• Where test name contains ‘ChatterBumps’

• Where acceleration exceeds 5g

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Data format support for over 50 formats including:

HBM catman .bin, .mea

HBM Perception .pnrf

HBM Somat .sif, .sie

B&K .pti, .bkc

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Engineering Data Analytics – Challenges

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nCodeDS enables engineers who are overwhelmed by

measured sensor data to gain

actionable insights from high

performance data analytics.

nCodeDS is provided with Aqira for scalable deployment.

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Challenge

Increasing volumes of measured data

Test engineer is faced with processing more files, more channels, higher sample rates

Question

How can I get results faster from a large number of test & measurement data files?

nCodeDS Example 1—High speed processing of measurement data

.s3t

.bin

.bkc

.rsp

.sie Analysis process:

• Butterworth filtering

• Calculated channels

• Rainflow count

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18 Pr oc essi ng t im e ( s) (using 4 threads)

Answer

nCodeDS using highly parallelized streamed data

analytics.

Increase overall analysis throughput by:

• Increasing the number of processing CPU threads

• Processing multiple files simultaneously

Example

• Input file: 370 MB .s3t file

• 82 channels of 1.2 million data points each

nCodeDS Example 1—High speed processing of measurement data

Processing each file took:

• 54 seconds in GlyphWorks

• 5.2 seconds in nCodeDS

10 times

faster!

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Challenge

• A car manufacturer is developing ADAS and autonomous vehicle technology.

• A fleet of vehicles is providing large volumes of usage data every day in CSV files of time stamped data.

Questions

• How do I extract useful information from huge quantities of CSV files?

• Understand the severity and duration of braking events.

• How do I cope with poor quality data?

• Gaps and missing data • Non-numeric data

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Solution with nCodeDS

• Designed specifically for unevenly spaced data

• Cleans up NaNs, detects and correct anomalies • Calculates statistics, time at level histograms, etc.

• Processes non-equally spaced data without resampling • Directly reads wide range of CSV formats

• For processing these CSV files, nCodeDS is over 100 times faster than GlyphWorks. nCodeDS Example 2—Analytics from Large Quantities of CAN bus data

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21 Ve h speed Bra ke pedal Time (s)

1. Find every time the brake pedal is actuated and corresponding vehicle speed.

2. Calculate drop in vehicle speed for all braking events.

3. Build a histogram showing number of braking events by vehicle speed reduction.

4. Also, perform a joint distribution with duration of braking event and corresponding speed reduction.

Example : Select data of interest i.e. extract a subset that meets specified criteria

Speed Reduction N o. of br ak ing ev ent s Speed Reduction Event Duration Speed Reduction

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Data Processing Capabilities – Over 30 different processing nodes including:

Signal Processing: Arithmetic operations, digital filters, resampling, FFT

Anomalies: Limit detection, spike detection, removing non-numeric NaNs

Statistics: Overall and moving window statistics

Counting: Probability density, time at level, joint distribution

Data Manipulation: Column splitter, section extraction, data merge

Python scripting

Fatigue analysis

nCodeDS: Technical Details

File Format Support

• Wide range of engineering file format support • Decoding CAN data directly using dbc files • Flexible and efficient CSV file handling

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Introduction to Aqira

Data Search: finding data on shared drives

nCodeDS: 10 to 100x faster signal processing

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Thank You

www.hbkworld.com | © HBK – Hottinger, Brüel & Kjær | All rights reserved

Jon Aldred

| Director, Prenscia Product Management

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Do you have a question for the

Presenter? Visit the Guest Speakers

Virtual booth within the next hour

for an interactive Q&A session.

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