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• Employed at Motorola / Freescale Semiconductor from June 1980 to the present, where I’ve had multiple careers. Most recently: − SoC Integration / MCU Architecture − Sensors & Algorithms - basically, solving
systems level problems • I blog on sensor related topics at
http://blogs.freescale.com/category/sensors/ • [email protected]
• Download the Freescale Sensor Fusion Library for Kinetis MCUs from http://www.freescale.com/sensorfusion
Introduction
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Sensor Data Analytics
• Unleash the information contained in sensors data beyond tracking motion
− Analyzing sensors data to guide informed decision
• Create new user experiences and benefits
• Monetize the information contained in the data
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Creating Values from Sensor Derived Information
KegData Measures the amount of beer
in a keg and provides beer consumption analytics data to
beverage distributors
Adidas miCoach Smart Run Watch
The next aid for sports drafting
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Using Sensor Data to Guide Informed Decisions
No longer just motion tracking • New approach adding
revolutionary value to older applications
• Brand new economy with getting data sets
• Numerous sensor IoT applications that are unrealized today
• Data transmission must be secure
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.csv
.xml
Git repository
Create Run
database
table.mat
Extract Features
misc.mat
Visualization
Physics-based model
extraction
Unsupervised Machine Learning
Supervised Machine Learning
Model of your system In
tera
ctiv
e D
ata
Logg
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ISF-based embedded data loggers
Ultimately, bring your generated model back to run on the very hardware you used to collect data
Here is one possible workflow for Sensor Data Analytics
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You might broadly separate Sensor Data Analytics into two classes of sensor data:
1. That based on well known physical phenomena (machine condition monitoring is an example)
2. Data mining, in which we look for patterns in data without advance knowledge of what those patterns might be. We consider two types: ! Unsupervised Learning ! Supervised Learning
The techniques are applicable to a wide variety of applications – probably MUCH wider than is current practice!
Courtesy of Volvo Construction Equipment (mages.volvoce.com)
Sensor Data Analytics
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figure source; http://en.wikipedia.org/wiki/File:Centrifugal_Pump-mod.jpg
This machine includes: • rotating motor • centrifugal pump • linkage between the two
Each is subject to its own array of problems. These might include: • Bearing failures • load imbalance • shaft misalignment • looseness • gearbox faults • drive belts • resonance
Let’s look at machine condition monitoring first
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Gear Mesh Frequencies
A B
Machine “M”
• Gear A has 10 teeth • Gear B has 15 teeth • If gear A is driven at fA=10
revolution/second, then the gear teeth mesh at a 100Hz rate and gear B turns at (fA X 10/15 = 6.67 Hz
• We would expect peaks in the vibration FFT of machine “M” at 10, 100 and 6.67 Hz.
Suppose one of the teeth on gear B develops a defect. We would expect that to create sidebands about the gear meshing frequency.
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.csv
.xml
Git repository
Step 2: Create
Run database
table.mat
Step 3: Extract
Features
misc.mat
Step 5: Physics-
based model extraction
Step 6: Unsupervised
Machine Learning
Step 7: Supervised
Machine Learning
Model of your system In
tera
ctiv
e D
ata
Logg
er
ISF-based embedded data loggers
Step 8: Ultimately, bring your generated model back to run on the very hardware you used to collect data
Step 1: Log Data
Step 4: Visualization
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Rapid Prototype Your Firmware Using ISF R2.1 on FRDM Platforms™
PC with IDE and
customizable GUIs
Freescale Freedom Board
Serial Comms via USB/
OpenSDA
Embedded middleware (ISF) and application target the Kinetis™
processor family
Advantage: ⇒ Get something to evaluate fast ⇒ Identify and eliminate as many risk areas as possible
Arduino Expansion connectors
1.5”
Coming soon: Size Reduced OpenSDA board
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Example Data Logger
Use this section to specify your test environment (i.e. your stove)
What are the things I am measuring?
What hardware am I using to measure which “things”
Record data
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.csv
.xml
Git repository
Step 2: Create
Run database
table.mat
Step 3: Extract
Features
misc.mat
Step 5: Physics-
based model extraction
Step 6: Unsupervised
Machine Learning
Step 7: Supervised
Machine Learning
Model of your system In
tera
ctiv
e D
ata
Logg
er
ISF-based embedded data loggers
Step 8: Ultimately, bring your generated model back to run on the very hardware you used to collect data
Step 4: Visualization
Extract features from raw data
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Extracted Features
• Statistical moments 1. standard deviation 2. variance 3. skew factor (lopsidedness) 4. Kurtosis (short and fat or tall and skinny)
• FFT coefficients • range (max - min values) • crossing rate (the percentage at which the signal crosses
the mean value during a given period) • cross-correlation between horizontal and vertical
components of acceleration • entropy of raw values • entropy of some of the statistical measures above
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.csv
.xml
Git repository
Step 2: Create
Run database
table.mat
Step 3: Extract
Features
misc.mat
Step 4: Visualization
Step 5: Physics-
based model extraction
Step 6: Unsupervised
Machine Learning
Step 7: Supervised
Machine Learning
Model of your system In
tera
ctiv
e D
ata
Logg
er
ISF-based embedded data loggers
Step 8: Ultimately, bring your generated model back to run on the very hardware you used to collect data
Visualize the data
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There is no pot on burner A. What you see here is a result of activity on burner B.
Sensor on stove top adjacent to the burner. 9” diameter stock pot with 2” of water.
3rd sensor is attached to one of the pot handles
Visualization: 3 Predictors X 3 Sensor Locations
Script = sda_step4b_plot3x3_predictors.m
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Visualization: Features vs Time
Helpful when you have a long run traversing across multiple system states.
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.csv
.xml
Git repository
Step 2: Create
Run database
table.mat
Step 3: Extract
Features
misc.mat
Visualization
Step 5: Physics-
based model extraction
Step 6: Unsupervised
Machine Learning
Step 7: Supervised
Machine Learning
Model of your system In
tera
ctiv
e D
ata
Logg
er
ISF-based embedded data loggers
Step 8: Ultimately, bring your generated model back to run on the very hardware you used to collect data
Consider physics-based models
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A physical model is better if you can Accelerometer Vector Magnitude vs Time
Cavitation starts
We start to get film boiling
Film boiling dominates (rolling boil)
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When we looked at the standard deviation of the data, we discovered the stove’s heating cycle
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.csv
.xml
Git repository
Step 2: Create
Run database
table.mat
Step 3: Extract
Features
misc.mat
Visualization
Step 5: Physics-
based model extraction
Step 6: Unsupervised
Machine Learning
Step 7: Supervised
Machine Learning
Model of your system In
tera
ctiv
e D
ata
Logg
er
ISF-based embedded data loggers
Step 8: Ultimately, bring your generated model back to run on the very hardware you used to collect data
Unsupervised machine learning
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Unsupervised learning can identify clusters
But cannot identify what the clusters represent
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.csv
.xml
Git repository
Step 2: Create
Run database
table.mat
Step 3: Extract
Features
misc.mat
Visualization
Step 5: Physics-
based model extraction
Step 6: Unsupervised
Machine Learning
Step 7: Supervised
Machine Learning
Model of your system In
tera
ctiv
e D
ata
Logg
er
ISF-based embedded data loggers
Step 8: Ultimately, bring your generated model back to run on the very hardware you used to collect data
Supervised machine learning
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Supervised Learning starts with data corresponding to known states
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Here we have used Support Vector Machines (SVM) to find hyper-planes to divide the clusters.
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Here we have used Logistic Regression on the same data set
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A classic error is not taking enough data
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Compare the two side by side
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.csv
.xml
Git repository
Step 2: Create
Run database
table.mat
Step 3: Extract
Features
misc.mat
Visualization
Step 5: Physics-
based model extraction
Step 6: Unsupervised
Machine Learning
Step 7: Supervised
Machine Learning
Model of your system In
tera
ctiv
e D
ata
Logg
er
ISF-based embedded data loggers
Step 8: Ultimately, bring your generated model back to run on the very hardware you used to collect data
Doing something useful with the result
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function [class, score1, score2] = score_svm(dataIn)
scale1=1.000000;
Beta1=[-1.797829; 2.370778; 0.614247];
bias1=4.575018;
scale2=1.000000;
Beta2=[-0.334549; 1.026571; 0.663425];
bias2=-0.748881;
mean_data=[4.046597e-01, 1.241677e-02, 1.997011e-02];
stddev_data=[5.116909e-02, 8.566602e-03, 2.431111e-02];
X = dataIn-mean_data;
X = X ./ stddev_data;
score1 = (X/scale1)*Beta1 + bias1;
score2 = (X/scale2)*Beta2 + bias2;
if ((score1<0)&&(score2<0))
class=1;
elseif ((score1>=0)&&(score2<0))
class=2;
elseif ((score1>=0)&&(score2>=0))
class=3;
else
class=4;
end
end
Model generated via support vector machines
Results are identical to those reported when the model was generated.
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Summary “Sensor Data Analytics” is not a tool you simply run; it is a set of techniques you apply, coupled with a workflow to guide your efforts. Sensor data analytics allow engineers to rethink conventional devices and make them simpler and safer to use and/or offer greater benefits to the quality of life of consumers. Sensor data analytics can make new information available in real time to improve operational efficiency Machine learning techniques are now in the mainstream. Tools are good, and improving. http://blogs.freescale.com/tag/sensor-data-analytics/
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Secure Embedded Processing Solutions for the Internet of Tomorrow.
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