Jorge Ortiz. Metadata verification Scalable anomaly detection.
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Transcript of Jorge Ortiz. Metadata verification Scalable anomaly detection.
![Page 1: Jorge Ortiz. Metadata verification Scalable anomaly detection.](https://reader036.fdocuments.net/reader036/viewer/2022062800/56649e035503460f94aee393/html5/thumbnails/1.jpg)
Metadata Verification and SBS
Jorge Ortiz
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Outline
Metadata verification Scalable anomaly detection
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Chiller
Pump
Chiller
Pump
AHUSF EF
Vent Vent
Zone
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Chiller
Pump
Chiller
Pump
AHUSF EF
Vent Vent
Zone
System
Space
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Types of relationships
Geometric Placement, associations
Functional Temperature, pressure, flow, etc.
Semantic Electrical device taxonomy Ownership
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Metadata management pipeline
Current Our work
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Geometric Verification
Are the geometric (spatial) associations correct?
Are all the sensors with the same spatial grouping in the same location? Sensors can be moved or replaced Contractor mislabels point in BMS
How can the sensor data guide this process?
SODA4R520__ART
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Similar Trend in Data Streams
Sensor streams driven by same phenomena
Common trend ineffective at uncovering relationships
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No Discernible Correlation Pattern in Raw Traces
Each row/column is a location in the building Each location has
one or more sensors
Cell (i,j) is the average device pairwise correlation between sensors at locations i and j
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Empirical mode decomposition Approach used for finding underlying
data trends Algorithm for decomposing signals in
the time domain of non-stationary, non-linear signals Similar to FFT, PCA but yields
characteristic time and frequency scales Output “Intrinsic mode functions”
Combination of underlying signal in the same time scale
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Devices in the same room
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Devices is different rooms
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Broader validation
Compare the EHP to 674 other sensors:
EMD helps us to discriminate un/related sensors
**Suggests Geometric Verification is possible**
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Functional Verification
Mislabeled “type” information of a data stream
Fault detection Strip, bind, and search process
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Buildings Generate Lots of Data Difficult for building managers to know where
to start to look for problems Which devices? Locations? Patterns? Time interval?
Key Observation Devices are used simultaneous in the same way Typically usage times/patterns are tightly un/coupled
▪ Example:▪ Lights and HVAC during the day
Basic assumption Normal usage is efficient.
Pairwise correlation analysis of sensor traces Uncover usage relationships between devices
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Strip and Bind
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Searching for Outliers
Construct reference matrix for each time-reference interval
For new data points, compute l
Identifying outliers Median Absolute Deviation
p=4
, b=1.4826
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Results
High power usage Alarms corresponding to
electricity waste Lower power usage
Alarms representing abnormal low electricity consumption
Punctual Short increase/decrease in
electricity consumption Missing data
Possible sensor failure Other
unknown
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Alarms in Eng. Bldg 2 @Todai
AC On All Night Lights On All NightAC Not On DuringOffice Hours
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Alarms in Cory Hall
Possible Chiller dysfunction Change in power usage pattern
Simultaneous heating and cooling
Normal
18 days, 2500 kWh
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Impact
2 research papers in collaboration with U. of Tokyo Internet of Thing Workshop @IPSN 2012 IPSN 2013 (April)
Web tool that finds anomalies from data uploads Upcoming release
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Future Work
Value verification Model-based verification, model
validation Standard representation with
embedded confidence parameters for MPC