asia pacific 2008 shm38

8/8/2019 Asia Pacific 2008 SHM38

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EnergyHarvesting,WirelessSHMandReportingSystem 2nd

AsiaPacificWorkshoponSHM,Melbourne,24December2008

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ENERGY HARVESTING, WIRELESS,

STRUCTURAL HEALTH MONITORING and REPORTING SYSTEM

S.W. Arms, C.P. Townsend, D.L. Churchill, J.H. Galbreath, B. Corneau, R.P. Ketcham, N. Phan*

MicroStrain, Inc., 310 Hurricane Lane, Williston, VT, USA

*Branch Head, Rotary Wing/Patrol Aircraft, NAVAIR Structures, Naval Air Systems Command, Lexington Park, MD

ABSTRACT

Energy harvesting, combined with wireless sensors, could greatly improve our ability to monitor and maintain critical

structures. This paper reports on the development of an integrated structural health monitoring and reporting (SHMR)

system for use on Navy aircraft. We have previously reported on energy harvesting, wireless sensor modules which

have passed MIL-STD-810F tests for vibration, shock, humidity, and temperature extremes. These modules were

integrated into the pitch link of a Bell M412 helicopter. In February 2007, the first successful flight test of an energy

harvesting wireless sensor was achieved. Pitch link loads were recorded and periodically transmitted into the cabin

during flight. The flight test proved that our e-harvesting load sensor can operate continually without batterymaintenance. The direct measurement of operational loads enables accurate fatigue tracking of these critical rotating

structures.

But not all sensors are wireless less wire is often appropriate, particularly when relatively high sample

rates and/or power levels may be required. Additional inputs to an integrated SHMR system may include data from the

vehicles hard-wired bus. Therefore, there is a need for a scalable, time-synchronized sensing system, capable of

supporting both wireless and hard-wired sensor networks. Our goal was to develop and test a versatile, fully

programmable SHMR system, designed to synchronize and record data from a range of wireless and hard wired sensor

networks.

Wireless sensors included strain gauges, accelerometers, and thermocouples. Hard-wired sensors included

gyroscopes, accelerometers, and magnetometers. Data from an embedded Global Positioning System (GPS) provided

position, velocity, and precise timing information. The inertial sensing suite provided vehicle orientation (pitch, roll,

and yaw) data. These data were collected at multiple sampling rates and time stamped and aggregated within a single

scalable database on a base station, termed the wireless sensor data aggregator (WSDA). The WSDAs processor

supports a Linux server, web interface, eight (8) Gigabytes secure flash memory, CAN, IEEE 802.15.4, Ethernet,RS232/422, and mobile phone. The data may be relayed over mobile phone networks to a secure server. Software to

access the aggregated data over the internet was developed, using the time stamp as a unifying reference for the various

types of sensor information.

The WSDA, in addition to providing a central location for collecting wireless and wired network sensor data,

also provided a beaconing capability to synchronize each sensor nodes embedded precision timekeepers. For testing, a

saw tooth analog voltage waveform was provided as an input to two wireless nodes to provide a reliable means of

determination of the systems timing accuracy. With the synchronization beacon provided at the start of a 2 hour long

test, and with two wireless nodes exposed to multiple temperature cycles of -40 to +85 degrees C, the system

demonstrated a 5 millisecond timing accuracy.

1. INTRODUCTION

Recent developments in combining sensors,

microprocessors, and radio frequency (RF)

communications holds the potential to revolutionize the

way we monitor and maintain critical systems.1

In the

future, literally billions of wireless sensors may

become deeply embedded within machines, structures,

and the environment. Sensed information will be

automatically collected, compressed, and forwarded for

condition based maintenance.

But the problem for the end user (and for our

environment), is that wireless sensors need energy to

operate, and batteries are a pain to maintain. A

solution to this problem is to harvest and store energy

from the environment using strain, vibration, light,

and motion to generate the energy for sensing and

communications. Combined with strict power

management, smart wireless sensing networks can

operate indefinitely, without the need for battery

maintenance.

To extend the life of todays rotary wing aircraft,

dynamic component removal, refurbishment and

replacement must be optimized. To accomplish this,

an accurate and up-to-date system must be developed


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to establish the current and past history of each fatigue

critical aircraft component. By directly tracking

loading histories, component fatigue rates are known,

enabling individual aircraft maintenance to be

optimized based on actual usage - rather than on

indirect estimates based on flight hours and flight

regimes.

El-Bakry of Airbus proposed the use of

passive RFID tags to facilitate aircraft componenttagging and tracking.2 Maley et al of the US Navy

emphasized the need for embedded, wireless sensors

capable of detecting and tracking the precursors to

crack initiation.3

Examples included strain sensitive,

printable, two dimensional bar codes4

and active

wireless strain sensors.5 The Navys long term vision

is to deploy distributed wireless sensor networks along

with RFIDs and bar codes to provide a wealth of usage

information about an entire aircraft structure.

Operating at microwatt power levels,

MicroStrains sensing nodes support a variety of

sensors, including conventional strain gauges, load

cells, pressure sensors, accelerometers, and

thermocouples. Overcoming the limitations ofbatteries through ambient energy harvesting has been a

natural evolutionary step in wireless technology

development. Using both piezoelectric and

electromagnetic energy harvesters, we have

demonstrated that sufficient energy could be harvested

to power a wireless strain sensor transceiver.6

These energy harvesting sensor systems have

been adapted to track damage on the rotating structural

components of helicopters, using wireless strain

gauges. An example of a critical component is the

control rod, or pitch link. Pitch links are traditionally

only monitored with slip rings during instrumented

aircraft flight testing (on one or two aircraft). Pitch

link loads in the Sikorsky H-60 have been found to

vary strongly with flight regimes: during pull-ups and

gunnery turns, the loads were measured at

approximately eight times that of straight and level

flight.7

Therefore, pitch link loads are a good indicator

of the rotating structures usage severity.

Wireless strain gauges placed strategically on

the pitch link enable the direct measurement of static

and dynamic axial loads, while canceling out bending

loads and thermal influences. We have demonstratedthat the operational strains in the pitch link will

generate enough power to allow continuous, wireless

operational load monitoring of this critical structure,

even during conditions of straight and level flight.5

In

the spring of 2007, the first successful flight test of our

energy harvesting wireless pitch link was performed on

a Bell M412 helicopter.8

In this paper, we provide a summary review of

our energy harvesting efforts, and we report on new

systems which expand on our previous work by

combining both wireless and hard wired data

acquisition. In order for these systems to accurately

track structural usage severity, it is critical that the data

from various wireless and hard wired sensors beaccurately time stamped. Le Cam has previously

reported on using an integrated GPS module on each

smart sensor to accomplish an absolute precision of 1

microsecond.9 Rather than include GPS modules (and

their antennas) at each node of the SHM system, we

chose to incorporate GPS within a central node, herein

referred to as the Wireless Sensor Data Aggregator

(WSDA) node. To maintain accurate time on the

remotely distributed sensor nodes, we chose to develop

a low power, temperature-compensated timing engine.

We also report on the capability to send data

to a remote server, using mobile phone networks to

enable automated data collection and reporting.

2. OBJECTIVES

Our objectives were to develop and test a versatile

SHMR system, designed to synchronize, record, and

report data from a range of wireless sensors (strain

gauges, accelerometers, thermocouples, etc), along

with data from GPS and an inertial sensing suite.

2.1 Concept of Operations

Wireless technologies for tracking the load history of

helicopter rotating components, combined with inertial

and global positioning system (GPS) information, can

be used to compute structural loads with improved

accuracy. The integration of these sensor systems will

lead to reduced cost flight testing, improved safety, and

enhanced condition based maintenance (Figure 1).

Ideally, the integrated structural health

monitoring system would report aircraft load history

data without human intervention. Data collected

during flight would be automatically recorded on

board, without wireless communications, since each

wireless load tracking node would be capable of

recording data within its local non-volatile memory.

After the aircraft lands, the on-aircraft base station

would query the network of wireless load tracking

nodes, and prepare data files for remote transmission

over the cellular or satellite connection. Data would

then be analyzed and maintenance instructions sent

back to aircraft technicians. Figure 2 provides an

illustration of this concept of operation.


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Figure 2. Concept

of operations for

automatedhelicopter

condition based

maintenance

3. METHODS

3.1 Wireless Strain Sensor Nodes

Each wireless sensor node includes an instrumentation

amplifier, a programmable gain amplifier with

programmable gain and offset adjust, and anti-aliasingfilter, 12-bit successive approximation analog to digital

(A/D) converter, embedded microcontroller

(processor), 2 MB of non-volatile memory (EEPROM),

and a programmable 2.4 GHz transceiver chip. Each

individual node is assigned a unique identification code

(RFID), which enables specific nodes on the network

to be address by the WSDA base station. Our current

wireless nodes system support both 16 bit addressesand 96 bit RFID codes. Broadcast addresses are

reserved for network wide command and control. A

block diagram of a strain sensing node is provided in

Figure 3.

Figure 1. Concept

for wireless energy

harvesting systems

for helicopter

flight test and in

service structural

loads tracking


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Figure 4.Integrated

wireless shear

pin (Shear-

Link) node,

shown at right,

adjacent to a

US quarter

dollar for size

The embedded firmware within each node supports an

open architecture command structure that enables

wireless control over each node in the network, or of

all nodes in the network. Node specific commands are

typically for wireless adjustment of gains, offsets, orshunt calibration of specific sensor channels with a

node. This flexibility enables support of a wide range

of Wheatstone bridge type sensors, including strain

gauges, accelerometers, pressure transducers, torque

sensors, load cells, and magnetometers. Energy is

conserved by multiplexing a pulsed, regulated

excitation voltage to the sensor bridge.6 The use of

high resistance bridge sensors is preferred, as this also

conserves energy by reducing the current drawn by the

sensors.

Temperature coefficients for these wireless

strain sensor nodes have been measured in our

laboratory at -.007%/degree C (offset) and

.015%/degree C (gain) when tested over a temperature

range of -40 to 125 degrees C. During these tests, the

strain gauge bridge was simulated by a bridge of fixed

precision resistors, with this resistor bridge located

outside of the environmental testing chamber.

An example of a fully integrated wirelessshear pin is provided in Figure 4. This wireless node

features strain gauges bonded within a tiny aperture,

which are connected internally to a miniaturized

wireless sensing node. This type of sensor can be used

to monitor shear loads on many structural connections

on and within an aircraft, including the landing gear.

3.2 Energy Harvesting

A key barrier to the widespread adoption of wireless

sensing networks has been the problem of keeping the

nodes batteries charged. MicroStrain, Inc. has been a

leader in adaptive energy harvesting electronics for

wireless sensor networks. Our electronics featuresmart comparators switches which consume only

nanoampere levels of current to control when to

permit a wireless sensing node to operate. This insures

that the energy checkbook is balanced, in other words,

the system waits until there is sufficient energy to

perform a programmed task. Only when the stored

energy is high enough will the nanoamp comparator

switch allow the wireless sensor to draw current.10

This is critical for applications where the ambient

energy levels may be low or intermittent. Without this

switch, the system may never successfully start up,

because stored energy levels may always remain

insufficient for the task at hand.11

Fully integrated energy harvesting wirelesssensors have been developed for the helicopter control

rod, or pitch link. Piezoelectric materials bonded

directly to the pitch link were used to harvest strain

energy for operation during flight. This work

demonstrated, for the first time, that an energy

harvesting wireless sensor for rotating helicopter

components could be operated indefinitely, using only

the strain energy of operation for power.

We have also reported on vibration energy

harvesters, which use piezoelectric materials and tuned

resonant structures, which were designed to resonate at

Figure 3. Block diagram of MicroStrains

wireless sensing node and base station

transceiver


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the predominant frequencies of the machine to which

they were affixed. These harvesters were

demonstrated, in Navy shipboard applications, to be

effective under ambient conditions of very low

vibration amplitude (30 milliGs) and low vibration

frequency (53 Hz).12

Embedded software was used to balance the

requirements of sampling and transmitting data against

the amount of energy that the harvester may begenerating in a given application. The embedded

software supported various operating modes for the

wireless sensor, which enabled the sensor node to adapt

its power consumption depending on the amount of

energy that was being generated.8

Solar energy harvesters have also been used

for civil structural monitoring applications. We

describe these applications in more detail in the

sections that follow.

3.2.1 Tracking Helicopter Component Loads with

Energy Harvesting Wireless Sensors

The US Navy, through its SBIR program, hassupported MicroStrains development of wireless

sensor nodes that use piezoelectric materials to convert

cyclic strain and vibration into power. One compelling

application is in monitoring the critical rotating

structures of helicopters. The direct measurement of

loads on these structures will allow enhanced

maintenance, which will greatly reduce operational

costs. Better loads tracking also has the potential to

save lives through improved performance and safety.

We have focused our initial efforts on the

helicopter control rod, or pitch link. The pitch link is

responsible for controlling the rotors angle of attack as

the rotor rotates through the air. As mentioned

previously, pitch link loads vary strongly with aircraft

flight regimes, reaching much higher loads (6X) during

maneuvers as compared to straight and level flight.7

Therefore the pitch link is an excellent indicator of

vehicle usage severity, and can provide critical data for

improved condition based maintenance. A photograph

of the microelectronics module developed under the

Navy SBIR program for the pitch link application is

provided in Figure 5.

Working in collaboration with BellHelicopter/Textron (Fort Worth, Texas), the first

successful flight test of our energy harvesting wireless

sensor node was performed in March 2007. We

instrumented the pitch link of a Bell Model 412

helicopter with our energy harvesting sensor node,

along with piezoelectric materials and a full strain

gauge bridge, which cancelled thermal and bending

influences while amplifying tension and compression

loads (figure 6).

The flight test showed that our energy

harvesting strain and load sensor will operate

continually, without batteries, even under low energy

generation conditions of straight and level flight. By

continuously monitoring the strains on rotating

components, our wireless nodes can record operational

loads, compute metal fatigue, and estimate remaining

component life. These techniques can also be applied

to other applications, such as monitoring large civil

structures.

3.2.2 Monitoring Large Bridge Spans with Solar

Powered Wireless Sensors

Recently, sudden structural failures of large bridge

spans, such as the Interstate 35W Bridge in

Minneapolis, and the Chan Tho Bridge in Vietnam

have resulted in the tragic loss of lives and of loved

ones. Three years ago, the Federal Highway

Administration reported that ~20 percent of the USinterstate bridges (nearly 12,000 bridges) were rated as

deficient. Developing and deploying cost efficient

methods for monitoring bridges - and for determining

which bridges require immediate attention - should be

an important priority for the United States and the

world.

Wireless sensor networks have the potential to

enable cost efficient, scalable monitoring systems that

could be tailored for each particular bridges

requirements. Eliminating long runs of wiring from

Figure 6. MicroStrains Energy Harvesting, Wireless

Loads Tracking Pitch Link installed on Bell M412

Figure 5. Microstrains Energy Harvesting Wireless

Pitch Link Load Sensing Node (patent pending)


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each sensor location greatly simplifies system

installation and allows a large array of sensor nodes to

be rapidly deployed.

We have recently supported two major

wireless installations which are actively monitoring the

structural strains and seismic activity of major spans.

Leveraging energy harvesting technology supported by

the US Navy, these wireless sensor networks are

powered by the sun, and therefore do not requirebattery maintenance.

MicroStrain has previously described battery

powered wireless strain sensors for structural health

monitoring.13,14,15

One example is the Ben Franklin

Bridge, which spans the Delaware River from

Philadelphia, PA to Camden, NJ. The monitoring

system was accessed remotely over commercial

cellular telephone networks, and sensor data were

provided to the customer via secure access to a web-

based server. The wireless nodes measured structural

strains in the cantilever beams as passenger trains

traversed the span. Measurements taken over several

months time were used to document the bridges

cyclic structural strains under contract from theDelaware River Port Authority (DRPA).

For the Ben Franklin Bridge, at the locations

tested, the measured strains and calculated stresses

were far below the endurance limit. Repeated cyclic

stress above the endurance limit results in the gradual

reduction of strength, or fatigue. Therefore, bridges are

designed to operate at stress levels below this limit.

From the information automatically collected by the

wireless strain sensors, DRPA engineers concluded that

cyclic stress fatigue due to train crossings was not a

problem.16

MicroStrains first solar powered bridge

installation was recently made in Corinth, Greece.

This system uses arrays of wireless tri-axial

accelerometer nodes to monitor the spans background

vibration levels at all times. Each node and solar panel

are packaged within watertight enclosures for outdoor

use. In the event that seismic activity is detected at any

one of the nodes, the entire wireless network of nodes

is alerted, and data are collected simultaneously from

the entire network. Photographs of this bridge and the

wireless G-LINK nodes as installed in Corinth are

provided in Figures 7 and 8.

The second solar powered installation is on

the Goldstar bridge in New London, Connecticut, in

collaboration with John DeWolf, Ph.D. of the

University of Connecticut. This system monitors not

only vibration, but also the strains and temperatures

from key structural elements of the span Intended forlong term monitoring, these new installations overcome

the limitations of older types, which required that the

wireless nodes batteries be replaced or recharged

periodically. Maintenance of batteries is simply not

practical on bridges, where sensor nodes must be

placed on, under, and within the structure in locations

which may be extremely difficult to access.

The Connecticut Goldstar bridge program is a

long term project developed to learn how bridge

monitoring systems can be used for evaluation of in-

service behavior, for long-term structural health

monitoring of each bridge, and for assisting the

Connecticut Department of Transportation to manage

the States bridge infrastructure.

17

Figure 7. Solar powered wireless G-Link seismic

sensors on Corinth Bridge, Greece

Figure 8. Solar powered wireless G-Link seismic

sensors on Corinth Bridge, Greece. High gain antenna

(top left) was used to provide a wireless communications

ran e of 150 meters.


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Figure 9. Inertial Sensing Suite includes orthogonal

arrays of temperature compensated gyros,

accelerometers, and magnetometers, along with an

embedded microprocessor.

3.3 Inertial Sensing Suite

The inertial sensing suite (ISS) consists of orthogonal

arrays of microelectromechanical systems (MEMS)

based angular rate sensors, accelerometers, and

magnetometers. Each of these MEMS sensors is fully

temperature compensated to minimize their individual

gain and offset errors, with the compensation

coefficients burned into the units on-board non-volatile memory (EEPROM). During factory

calibration, each ISS is also corrected for its unique

mechanical misalignment among the nine MEMS

sensors as well as its unique magnetic signature to

correct for distortions of Earths field as detected by

the three magnetometers. These alignment and

magnetic correction matrices are also burned into each

units EEPROM. Sculling and coning errors are

minimized by using a dedicated, 16 bit resolution,

sigma-delta A/D converter. The dedicated A/D

converters sample each MEMS sensor at a rate of

2 KHz, with the data are recorded at a rate of 200 Hz,

which removes unwanted dynamic transients - such as

may arise from vehicle vibration - from the orientationcalculations.

The data from all nine sensors are used to

compute orientation, which may be reported in Euler,

Quaternion, or Matrix form. The structural monitoring

system records and time stamps these data using a

digital serial interface. A photograph of our inertial

sensing suite is provided in Figure 9.

3.4 Wireless Sensor Communications

Radio communication between the wireless sensor

nodes and base stations is based on the IEEE 802.15.4

standard in the 2.4 GHz band. One of the major

advantages of this frequency band is that it may be

used, license free, worldwide. The transceiver we are

currently using is available commercially throughTexas Instruments, model CC2420 (Austin, Texas).

There are 16 distinct communications channels in this

band, which may be software selected. As shown in

Figure 10 below, channels 15, 20, 25, and 26 are non-

overlapping with 802.11b in North America, and

channels 15, 16, 21, and 22 are non-overlapping in

Europe.

The CC2420 radio transceiver has features

which are well suited to our energy harvesting wireless

sensor node applications. This transceiver can be

powered up very quickly (~0.7 milliseconds) which

conserves energy, since the boards main

microprocessor powers up the radio transceiver only

when appropriate.As we have previously described (8), the

energy consumed by the radio can be further conserved

by reducing the number of packets sent per second.

For our wireless helicopter pitch link applications,

strain data were logged at a specified rate (64 Hz), and

once 100 samples were acquired, the system

transmitted these data. By buffering time stamped data

and transmitting these data in a single packet, the

digital process overhead associated with the framing

and checksum bytes was minimized.

Our protocols for wireless communications

are scalable. By combining time division multiple

access (TDMA), carrier sense multiple access

(CSMA), and frequency division multiple access

(FDMA), we can support a large number of wireless

sensor nodes. For example, assuming a network ofwireless strain nodes were configured to sample a tri-

axial strain gauge rosette at a rate of 5 samples per

second, this system will support up to 600 distinct

wireless nodes (1800 strain gauges) using only TDMA

and CSMA techniques on a single radio

communication channel. By adding radio transceiver

chips within the WSDA base station, the system will

theoretically support 16 of these strain sensing

networks, or as many as 1800 strain gauges*16 radio

channels = 28,800 individual strain gauges.

An important component on each wireless

node is an independent, precision, nanopower real time

clock (RTC), with a +/- 3 part per million (PPM) time

reference. The real time clocks on all wireless andwired sensor nodes are synchronized at the beginning

of a test to the base stations time reference, using a

wireless beacon to communicate that reference. The

WSDA base station uses a built-in hard-wired Global

Positioning System (GPS). In case GPS is not

available, the WSDA uses its internal +/- 3 PPM real

time clock as the timing reference to insure

synchronization of all the remote sensor nodes to the

WSDAs clock. A detailed description of the timing

engine methodology is provided in the next section.


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Figure 11. MicroStrains Wireless Sensor

Data Aggregator (WSDA)

3.5 Sensor Data Aggregation

The wireless sensor data aggregator (WSDA) is

responsible for data collection from several different

buses and networks, including the CAN bus, USB, and

802.15.4 wireless networks. A photograph of this

device is provided below in Figure 11.

Important design criteria for the WSDA were:

Open architecture operating system Provides time synchronization platform Data saved in scalable sensor database,

allowing multiple sensor types to be archived

in a single file

Data collected at multiple rates aggregatedinto single data base Data sorted based on parameters such as time

stamp or sensor type

Multiple bus interfaces supported: CAN IEEE802.15.4 Ethernet RS232/RS422 (HUMS)

One of the primary challenges for this

distributed multi-network topology is synchronizing all

data acquisition points, hereafter referred to as nodes,

throughout the entire system. Figure 12 provides a

block diagram of the WSDA and associated hard-wiredand wireless nodes. The WSDAs functional blocks

include the GPS receiver, timing engine,

microprocessor core running Linux 2.6, CAN bus

controller, and wireless controller. Time

synchronization for hard wired and wireless nodes was

accomplished by periodic beaconing of a reference

timing signal, as described in the sections that follow.

Figure 10. IEEE 802.15.4 and IEEE 802.11b channel selections in the 2.4 GHz frequency band


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3.6 Temperature Compensated Timing Engine

The data aggregator and wireless sensor nodes feature

a highly-accurate, temperature compensated timing

engine. The temperature compensation method relies

on an onboard temperature sensor, calibrated software

look-up tables, and an oscillator tuning mechanism to

maintain accurate frequency vs. temperature. The

timing engine is used on the data aggregator to provide

a stable 1 Hz reference for the synchronization beacon.

On the wireless sensor nodes, the same timing engine

is slightly modified to provide adjustable output from

1 to 4096 Hz, which is used to drive a sensor-sampling

interrupt on the host processor.

3.7 Wireless Synchronization Beacon

Timing drift among wireless sensor nodes can be

corrected by a centrally broadcast synchronization

beacon. There are several requirements for practical

implementation: First, the wireless network controller

must be capable of transmitting a single broadcast ormulti-cast addressable packet that can be received by

all nodes simultaneously. The IEEE 802.15.4 radio

offers a broadcast addressing feature that satisfies this

requirement. Second, the timing for the

synchronization packet transmission and reception

should be as fixed and deterministic as possible. On

the data aggregator, packet transmission is initiated by

a hardware interrupt, which is driven by a precise

timing source (GPS or aforementioned timing engine).

The 802.15.4 carrier sense multiple access (CSMA)

function is disabled to ensure that the beacon packet is

transmitted immediately. On the receive side, the

chosen 802.15.4 radio uses a hardware state machine toautomatically process, decode, and error check all

incoming packets before sending an interrupt request to

the host processor. With the packet transmission and

reception functions handled in hardware, the

communication latency is minimal and relatively

deterministic.

P CorerunningLinux 2.6

CANController

WirelessController

RS-232 Node

CAN NodesCAN NodesCAN NodesCAN Nodes

Wireless NodesWireless NodesWireless NodesWireless Nodes

RS-232 Node

WirelessController

Wireless NodesWireless NodesWireless NodesWireless Nodes

USB Node

USB Node

GPSReceiver

TimingEngine

Wireless Synch Beacon

CAN Synch Mechanism

SYNCH CLK and TRIG

Figure 12. Block Diagram for wireless and hard-wired (networked) SHM System


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Figure 13. Set of two wireless accelerometer nodes

(G-LINK), mounted on a rigid aluminum plate

3.8 Wireless Digital Communications Latency

To characterize this single packet communication

latency, a digital oscilloscope was used to measure the

time between the completion of packet transmission on

the wireless network controller, and completion of

packet reception on the wireless sensor node. Due to

the relatively short (


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Figure 14. Timing synchronization without beaconing and with exposure to cycles of extreme

temperatures of -20 to 60 degrees C; timing accuracy result: 39 milliseconds over ~13 hours

time (s)

acceleration(g)

Synchronization Test without Beacon39 ms drift between Node #1 and Node #2

Test Duration = 13.3 hoursTemp Cycled from -20 C to 60 C, 50 times

49499 49499.25 49499.5 49499.75 49500 49500.25 49500.5 49500.75 49501 49501.25 49501.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

Node #1 Z-axis

Node #2 Z-axis

3.10 Remote Data Reporting

To enable report data reporting, the WSDA includes an

internal cellular telephone modem and, will, in the

future, support an external satellite modem. The

remote cellular (GSM/GPRS) terminal interface uses a

PPP protocol for secure transfer of data to a remoteserver. We selected a cellular modem based on a trade

study, and chose the GSM/GPRS modem because it

offered the widest global cellular coverage. We have

also identified an embeddable satellite modem card that

utilizes the Inmarsat Broadband Global Area Network

(BGAN). This network provides the best combination

of worldwide coverage and high data rate at reasonable

cost per byte of data transferred.

Data sets stored on the WSDA were

transferred to our web server for development, test, and

demonstration purposes. A flash and HTML web

interface was created to enable ongoing, completely

independent structural monitoring by MicroStrainsOEM customers. In order to provide secure access to

the data stored on our remote server, a password

protected user management and authentication process

was implemented. A dedicated server hosting

company was selected to provide a scalable, reliable,

and secure dedicated server to support the Navy in the

future.

4.0 RESULTS

For the long term (13 hour duration) tests, and subject

to 60 cycles of temperatures ranging from

-20 to 60 degrees C, with a timing beacon sent at theonset of the test only, the synchronization accuracy was

measured at 39 milliseconds (Figure 14). This timing

offset constitutes a relative (node-to-node) drift rate of

0.8 parts per million (ppm), where drift =

0.039 seconds / 48000 seconds.

In contrast, the 13 hour test with 60 second

timing beacon produced no discernible timing offset

between the two wireless nodes (Figure 15). In theory,

with a beacon interval of 60 seconds, the maximum

timing offset should be 60 seconds multiplied by the

maximum drift rate. Using the relative drift ratemeasured from the first test, this would amount to

60 seconds x 0.8 ppm = 48 microseconds. With a

sample rate of 128 Hz, the timing resolution of the test

was inherently limited to 7.8 milliseconds. In this case,

we were unable to detect so small a magnitude of the

timing offset, which we estimate to be less than

50 microseconds.


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Figure 15. Timing synchronization with beaconing and with exposure to cycles of extreme

temperatures of -20 to 60 degrees C; timing accuracy result: no discernible timing difference

These results prompted us to perform the saw

tooth waveform tests, in order to better characterize the

systems true timing accuracies that could be expected

with beaconing. For the first 2 hour room temperature

test, with beacon correction provided only at the start

of the test, and with the strain nodes exposed only to

room temperatures, yielded a timing offset of

325 microseconds between the two wireless strain

nodes. This timing offset is barely visible in Figure 16;constituting a relative (node-to-node) drift rate of 45

parts per billion (ppm), where drift = 325 microseconds

/ 7200 seconds.

For the second 2 hour test, again, with beacon

correction provided only at the start of the test, and

with the strain nodes cycled from -40 to +85 degrees C,

produced a timing offset of 5.71 milliseconds between

the two nodes, as shown in Figure 17. This constitutes

a drift rate of 793 parts per billion.

An additional 2 hour test was performed (over

temperatures of -40 to +85 degrees C) using a lower

frequency saw-tooth wave voltage input of 1Hz, in

order to check our prior result. This additional short

term test yielded a timing offset between the two SG-LINK nodes of 5.04 milliseconds, or 700 parts per

billion, which agreed closely with our prior result.

time (s)

acceleration(G)

Synchronization Test with BeaconBeacon Resynch Interval = 60 s

Test Duration = 13.3 hoursTemp Cycled from -20 C to 60 C, 50 times

50060 50060.5 50061 50061.5 50062 50062.5 50063 50063.5

-1.5

-1

-0.5

0

0.5

1

1.5

Node #1 Z-axis

Node #2 Z-axis


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Figure 16. Timing synchronization w/ beacon sent once

at start of 2 hour room temperature test: 325 microseconds

Figure 17. Timing synchronization w/ beacon sent once

at start of 2 hour exposure to -40 to +85 degrees C: 5.7 milliseconds


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5.0 CONCLUSIONS

Combining advanced microelectronics with sensors,

energy harvesting methods, power management, data

collection, and web-based data distribution is a

powerful solution for structural health monitoring

(SHM) problems. Energy harvesting represents an

exciting development, with the potential to eliminatebattery maintenance for hard to access sensor nodes.

A wireless sensor data aggregation node

(WSDA) has been developed which is capable of

collecting time stamped data from both wired and

wireless sensor networks and sending these data to a

secure server on the internet.

Precision time keepers, embedded within each

sensor node, are synchronized to the WSDA using a

beaconing method to provide a periodic timing

reference. Synchronization of the sensor network

provides several key advantages, including enhanced

scalability of wireless communications, and the ability

to store all sensor data in a single time stamped

parametric database.With no beacon except at the start of a 13

hour long test, the system maintained a timing

synchronization of 39 milliseconds, while exposed to

continuous, 8 minute duration thermal cycles ranging

from -20 to 60 degrees C. This level of timing

synchronization may be adequate for many SHM

applications. The system achieved a timing

synchronization accuracy of ~5 milliseconds with a

timing beacon sent every 2 hours. This accuracy

improves when the thermal environment is stable. The

timing accuracy is also improved by sending the

beacon more frequently. For flight tests that require a

synchronization of sensor data to sub-millisecondaccuracies, a conservative approach would be to

provide a synchronization beacon every 20 minutes.

For those applications where the timing

synchronization may not need to achieve sub-

millisecond accuracies, all wireless sensor radio

communications may be turned off completely during

flight. The wireless nodes would then be used in data-

logging mode only, which conserves power and

eliminates all propagation of 2.4 GHz radio energy

from the nodes during flight. Time stamped data can

be collected from the wireless nodes after the flight is

completed.

The system is capable of remote reporting

using mobile phone networks, with satellite reportingcurrently under development. These capabilities,

coupled with appropriate wireless security methods,

will enable critical structural sensor data to be managed

remotely, securely, and automatically.

6.0 ACKNOWLEDGEMENTS

MicroStrain, Inc. gratefully acknowledges the support

of the US Navys SBIR program and the Office of

Naval Research BAA program.

7.0 REFERENCES

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th-May 4

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