AnalatomA Structural Health Monitoring Company

Analatom LPR Corrosion Sensor

Analatom Thin Film LPR Corrosion Sensor made out of same material as structure being inspected

Wireless LPR & Strain Gauge Sensor Node

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

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Sensor

Sensor

Sensor

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Sensor

Node, with multiple sensor and sensor type interface, limited signal processing and memory

Node, with multiple sensor and sensor type interface, limited signal processing and memory

Node, with multiple sensor and sensor type interface, limited signal processing and memory

Node, with multiple sensor and sensor type interface, limited signal processing and memory

Node, with multiple sensor and sensor type interface, limited signal processing and memory

Fleet Data-bank & Prognostics System

Early Warning

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Sensor

Data Bank & Prognostics System CPU, receives data from multiple nodes using a

variety of communications.E.G. Wireless, Bus…

Analatom Sensor Node Controller Electronics PCB and Multiplexed

The demonstration unit operates as if the sensor node were connected directly to the PC via USB. The MaxStream units, which have a serial data interface, make it relatively easy to add

wireless functionality into the design.

Demonstration LPR Wireless ZigBee System

ZigBee Wireless Transceiver

Allows for easy integration with current microcontroller

Low-power modes of operation Mesh network capability

Wireless Network

Point to Multipoint Network

Point-to-point network

Mesh network

•In a mesh network, multiple nodes cooperate to relay a message to its destination.

•The mesh topology allows for continuous connections and reconfiguration around blocked paths by "hopping" from node to node until a connection can be established.

•The routing algorithm used attempts to ensure that the data takes the most appropriate (fastest) route to its destination

LPR Corrosion Sensor Range - Proximity to Corrosion

LPR Corrosion Sensor System Aerospace Application

Controller box, with data logging system & serial

interface

Interface Circuit

SENSOR

Cabling

•Measurement gives corrosion rate

•Software determines material loss and material thickness loss

Boeing Data (First time period, parts 6 to 6.5)

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10000

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6 6.05 6.1 6.15 6.2 6.25 6.3 6.35 6.4 6.45 6.5Time (arb)

Tem

p/R

H/C

orr

(al

l no

rm t

o 6

5k)

Temp (nom)

RH

CORR

I (µA)

V (V)

.001 .01 .1 1 10 100

.4 .3 .2 .1 0 -.1 -.2 -.3 -.4

Icorr

Vcorr

Tafel Plot measured for Stainless steel LPR corrosion sensor Icorr=1.9 µA Vcorr=0.12V

Extrpolated Cathodic current

Extrapolated anodic current

Steel LPR System Implementation

Concurrent Technologies Corporation and the US Army have been assessing Analatom LPR Corrosion Sensor System for Structural Health Monitoring of Army land based vehicles. Initial testing was to see if the system could pick up a corrosion rate of 0.1mm/year with 10.0% error for 1010 steel. The system passed with 0.6% error.

Rpave= 30,080

Icorr~0.26/Rp= 8.6436E-06

steel EW= 28 density= 7.87 g/cc

corr rate=Icorr K * EW/density * Area Al EW= 9 density= 2.7 g/cc

Fe corr rate= 0.100621641mm/year

Al corr rate=   

Steel LPR System Implementation(Continued)

Current Location of LPR on Okinawa

Army Corps Engineering Testing in Okinawa, Japan

Roof Installation

Corrosion rated Versus Time

LPR System Mechanical Sensor Packaging

Early version of LPR

Industrial LPR Sensors

LPR Corrosion Sensor Range - Proximity to Corrosion

Corrosion under paint

Corrosion under sealant

Results Corrosion Under Sealant

Data Imaging Experiment: Data Acquisition from Large Sensor Arrays with Multiple Nodes

Steel Plate with Corrosion Damage Steel Plate with 32 Sensors

Data Imaging Software and Representation

Steel Plate with Corrosion Damage

LPR Sensor Corrosion Rate Reading Nearest Neighbor Algorithm

Array Representation Nearest Neighbor Algorithm

Wireless Sensor Network Overview

Ability to collect and store large amounts of sensor data, to transmit the data wirelessly over a low-powered battery operated network, and to present the data to the user in an understandable and logical manner.

The master node will incorporate the latest Analatom sensing technology and low power wireless hop interfacing to allow additional sensors to be added to the system with minimal need for technical expertise.

A wireless network based on the ZigBee protocol will address the needs for a low-powered solution.

Wireless Implementation

Electroplating Zinc Coated LPR Experiment Setup

4 plates with LPRs are stored inside in a 100% humidity chamber

1

2

3

4

6

5

7

8

Zinc Electroplated LPR Experiment

Plate 1 has defects on one of the corners and is submerged in the water. LPR 2 is barely touching the saline water.

Plate 2 has no defects and has one corner submerged in the water. LPR 4 is barely touching the saline water.

Plate 3 has no defects and barely above the saline water.

Plate 4 has no Zinc coated and hanging above the water.

2 1 4 3

8 75 6

0

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1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

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LPR 1

LPR 2

LPR 3

LPR 4

LPR 5

LPR 6

LPR 7

LPR 8

Experiment Result Zinc Plated Sensors

On the LEFT: On LPR 5, the Zinc coated plate is barely touching the saline water. Zinc coated LPR 5 still picking up some corrosion from the plate.

On the RIGHT: LPR 2, 4, 8 is on the bottom of the plate touching the water showing a lot of corrosion.

Tocircuitboard

Zinc Plated LPR Sensor Array with the top slotted polyimide layer

• The bottom polyimide layer serves as the carrier and to isolate the sensors and interconnects from the cables.

• The top polyimide layer electrically isolates the sensors and interconnects from the cables.

Integrated LPR Sensor Design to Address Application Specific Requirements

Noise reduction

Neural Network

clustersclassify predict

Obtain features of interest

Self organizing mapsAdaptive Neural NetworksRecurrent neural networksAutoregressive Neural networks

Anomaly detectionCrack growth

Acoustic emission

Data Mining, Diagnostic / Prognostic Modeling

Creation of a ‘VIRTUAL SENSOR’

Definition : A modeled representation of a “non-existent (virtual) sensor” signal.

Uses all local, coincident sensors to understand and model its own normal & abnormal behavior and acceptable boundaries.

By modeling Variable 1, 2, 3, and etc. AS A GROUP, we can infer the current value and state of the Engine_RPM as a proxy for UAV component 1.

Sen

sor

data

for

com

ple

te m

issi

on

Pro

cess

Mod

ele

r“N

eura

l Ne

twor

k”

Variable 1

Variable 2

Variable 3

Variable 4

Variable 5

Variable …

FAC 1

FAC 2

FAC 3

Engine RPM

UAV component

model 1

“A Virtual Sensor”

Deviation Detection Using Anomaly Module

Created a Neural Network model to detect abnormal deviations in behavior of Engine_RPM activity across past, recent missions.

Analyzed the history, sequence and severity of ‘alerts’ that contributed to the Engine_RPM failure.

Isolated patterns of persistent abnormal behavior leading up to an Engine_RPM failure.

Identified (and ranked) classes and types of sensor alerts that

contribute to UAV Component 1 events.

Vehicle #1, Engine ID ‘333’All missions database

Anomaly(deviation detection)

Mission1

Mission4

Mission3

Mission2

Re

sulti

ng U

AV

co

mpo

nent

m

odel

UAV component series signatures

A deviation or Anomaly beginning to form from

previous missions

Anomaly looks for trends

Anomaly detectionNeural Network

Looks at all previous missions

LPR Sensor System Bridge Cable Installation

5 4

32

1

Corrosion Sensors

5 4

32

1

Corrosion Sensors

• The LPR sensor location in any cable cross section will allow for corrosion in the cable to be displayed in a number of different formats. Example given is with a circular center section and four quadrants

• The thickness loss reading recorded at each sensor can be interpolated to adjoining sensor. In this manner if for example a breach in the cable wrraping is present in the area of quadrant 2, a map can be build up of corrosion rate in the cable.

Corrosion Rates from Bridge Wire Strand Mock-up

Corrosion Rate of Strand Mock-up

0

0.05

0.1

0.15

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0.35

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0.45

5.00 25.00 45.00 65.00 85.00

Time (Minutes)

Co

rro

sio

n R

ate

(m

m/Y

ea

r)

Series1

Series2

Series3

Series4

Series5

Cable Health Monitoring

ACOUSTIC MONITORINGNumber of broken wires

CABLE STRENGTH MODEL

LPR SENSOR READINGS

)( 2tN )/(,)( 2 yrmmt

))(),(( 22 tNtfStrength

Time-Degradation of Cable Strength

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0 0.01 0.02 0.03 0.04 0.05

Cable Deformation (%)

Ca

ble

Str

en

gth

(K

IPS

)

Strength at Time T1

Strength at Time T2

Analatom FHWA LPR Sensor System Cable Monitoring

http://www.exn.ca/dailyplanet/view.asp?date=4/3/2006 and click on "Building suspense in NY"

Open-ended Solution

Analatom LPR with Microflex