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ANLY-601
Advanced Pattern Recognition

Spring 2018

L5b — Example sequential test

Detection of Marine Sensor Biofouling

Antonio Baptista, Cynthia Archer, Haiming Zheng

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Conductivity Sensor
Biofouling

CT1448, 9/28/01

CT1459, 9/30/02

CT1449, 8/28/01

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Conductivity Sensor
Biofouling

CT1448, 9/28/01

CT1459, 9/30/02

CT1449, 8/28/01

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Biofouling
Detection

• Detect onset of biofouling within several diurnal cycles

• Challenges

– Variability of fouling signature

– Very few examples of fouling onset (many days of

lost conductivity data, but few episodes) – can’t use

clean/fouled discriminators.

– Distinguish natural variation from sensor

degradation

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Detection Algorithm

,

,

( | )
( ) ln

( | )

fouled
Now

n n

n Now n n
clean

p x T fouled
h Now

p x T clean

 



Sequential likelihood ratio test

Now is current time

Now- is start of fouling event (unknown)

xn is salinity at time n

Tn is vector of local water temperatures at time n

 is detection threshold (set by specifying false alarm rate)

p(x|clean) is Gaussian with mean E[s|T] dependent on observed tidal variation of

local temperature (i.e. predict salinity from temperature) and variance var(s|T)

p(x|fouled) is Gaussian with mean decreasing linearly (slope m).

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Example of On-Line Detector Signals

Onset labeled by human7

Impact and Further
Development

• Initial detectors placed on-line in spring of 2001.

• Detectors eventually placed at all observing sites.

• Data Preservation

– Prior to the deployment (four years of data from 1997
through the summer of 2001), CORIE salinity sensors
suffered a 68% data loss due to bio-fouling.

– Post-deployment (spring/summer 2001 through February
2003), data loss due to bio-fouling dropped to 35%. This
includes delays in responding to the event detection.

– If all sensors were attended to immediately following a
detected event, the data loss would have dropped to
17%.

– DETECTORS CUT CORIE BIOFOULING-INDUCED
DATA LOSS IN HALF

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• One false alarm due to precipitation; one
unexplained false alarm.

• Seasonal changes in river/ocean
temperature profile require different
predictive models at different times of
year.
– We developed mixture models for prediction

that automatically adjust to current
temperature profile conditions in the river-
estuary-ocean system.

Impact and Further
Development

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References

Leen, T.K.; Archer, C; Baptista, A. Parameterized Novelty Detector for

Environmental Sensor Monitoring. Advances in Neural Information

Processing Systems 16, Thrun, Saul, and Scholkopf (eds.), The Mit

Press, 2004.

Archer, C; Baptista, A; Leen, T.K. Fault detection for salinity sensors in

the Columbia River Estuary. Water Resources Research, 39, 1060,

2003.

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