2007 Urban Remote Sensing Joint Event

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Wireless MEMS –Sensor Networks for Monitoring

and Condition Assessment of Lifeline Systems

Masanobu.Shinozuka, Maria Feng, and Ayman

Mosallam

Department of Civil and Environmental Engineering

University of California, Irvine

CA 92697, USA

shino@uci.edu

Pai Chou

Department of Electrical Engineering and Computer Science

University of California, Irvine

CA 92697, USA

Abstract— This paper summarizes and combines the recent

papers by two of the authors and demonstrates a potential use of

a sensor-based real-time monitoring and condition assessment

system for urban lifeline infrastructure. Rapid detection of

damage caused by natural and manmade hazards enables an

efficient and effective emergency response minimizing human

and property losses as well as societal disruption. In this paper,

using a small scale model of water pipeline network as an

example, we will demonstrate a monitoring system consisting of a

wireless network of power-efficient sensors for a rapid

identification of the extent and location of pipe damage

immediately after a disastrous event. In this particular example,

we take advantage of sharp transient change in the water head

due to the damage. The result suggests that a simple inverse

analysis can locate the damage in a pipe segment between two

neighboring sensors among the pervasively installed along a

pipeline at which the absolute values of water head are observed

to be local maxima. Separate experiment and analysis show that

the sharp transient change in water head in the pipe flow induces

a correspondingly sharp change in the acceleration of pipe

vibration on the pipe surface. This fact is conventionally used for

damage identification in this study.

I. INTRODUCTION

Urban water delivery network systems, particularly the underground components such as pipeline networks, can be damaged due to earthquake, pipe corrosion, severely cold weather, heavy traffic load on the ground surface, and many other man-made or natural hazards. In all these situations, the damage can be disastrous: water leakage at high pressure may threaten the safety of near-by buildings due to scouring of their foundations; flooding could create major traffic congestion if pipe ruptures under a busy street; and above all, after a severe earthquake, pipe damage may result in reduction in the water head, degrading post-earthquake firefighting capability of the community, while at the same time force the human consumption of water to drop below unacceptably low level. Yet, the current technology is not capable of accurately identifying the location and extent of the damage easily or quickly, especially immediately after a major earthquake. As a

result, even if full resources are available for damage repair, failure to locate damage can still lead to loss of post-earthquake firefighting capability, widespread human suffering and outbreak of diseases after a major earthquake.

II. DAMAGE DETECTION AND LOCALIZATION OF PIPE

NETWORK

A. Hydraulic Transients

A hydraulic transient represents a temporary, often violent, change in flow, pressure, and other hydraulic conditions in a water delivery system from an original (first) steady state to a final (second) steady state the system achieves after the effect of the disturbance that caused such a transient is absorbed into the second state. The disturbance includes such events as a sudden valve closure or opening, a pump stopping or restarting depending on power supply, and pipe damage or break leading to water leakage. In fact, as described in more detail in what follows, it is envisioned that the sudden change of such pressure will generate a measurable pressure wave and can be used for detection and localization of pipe damage. If the magnitude of this transient pressure is beyond the resistant capacity of complex system components such as elbows, it can induce disastrous effects on the water system. Therefore, it is important to numerically simulate the transient behavior of the water system under various adverse scenarios in order to understand the magnitude of these effects. In this study, the industry-grade computer code HAMMER (Haesead 2003) is employed to generate time histories of key hydraulic parameters (primarily water head and flow rate). The analysis is carried out for a hydraulic system as shown in Fig. 1 which appears in HAMMER User’s Guide as an example for demonstration of transient effects. As shown in Fig. 1, this water system consists of two reservoirs, one pump, one valve, thirty-eight nodes and 54 pipe links covering an area approximately 40 km x 60 km. The initial hydraulic boundary conditions are such that Reservoir 1 supplies water to the network and the water is consumed in a steady state at consumption nodes.

2007 Urban Remote Sensing Joint Event

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In this simulation analysis, we consider two scenarios, Scenarios 1 in which a break occurs in pipeline 111, and Scenario 2 in which two ruptures occur in the network, one in pipeline 111 and another in pipeline 24. Figs. 2 and 3 plot time histories of water head at Joint 9 under Scenarios 1 and 2, respectively. It is noted that the water head shown in Figs. 2 and 3 is inclusive of the elevation effect. The time history begins with a pressure value representing the initial steady state and converges downward to the value associated with the second steady state as the system complete its adjustment to the change.

B. Damage Detection

A method of damage detection and localization, including the identification of malfunctioned equipment, is described here that is based on the comparison of the hydraulic parameters (the water head in this case) before and after the event. For the primary purpose of a rapid detection and localization, it is most effective to catch the sign of change at the outset of the event. Fortunately for a sudden change such as a pipe break and pump stoppage, the response of the network is rapid particularly in the neighborhood of the source. This suggests that some measurable signature that indicates the rapidity of this change can be used for this purpose. One convenient quantity that serves this purpose is the absolute water head gradient as defined and referred to as D below.

12

12

tt

HHD = (1)

Here H2 and H1 are the water head of a node at the time t2, t1 respectively and t2-t1 = 0.2 second in this study.

During the steady state normal operation, D is usually negligibly small. In this paper, the water head gradient measured at the observation nodes are integrated into the GIS platform for real-time visualization and for other advantages.

Figure 2. Water Head Time History at Joint 9 under Scenario 1

Figure 3. Water Head Time History at Joint 9 under Scenario 2

430

440

450

460

0 10 20 30 40 50 60

Ti (S d)

Wate

r H

ead

(m

)

430

440

450

460

0 10 20 30 40 50 60

Time (Second)

Wate

r H

ead

(m

)

2007 Urban Remote Sensing Joint Event

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B A

Figure 4. Contour Map of D (Absolute Maximum Water Head Gradient) due to a Pipe Break in P111

A

B

C D

Figure 5. Contour Map of D (Absolute Maximum Water Head Gradient) due to two Pipe breaks one on P111 and another in P24

In Fig. 4, we can observe D values at the two end nodes A

(Joints 9) and B (Node 11) of Pipe 111 are spatially local maxima to identify the location of pipe break in this link. Similarly, we identify in Fig. 5 the locations of pipe break one each in Pipes 111 and 24 by recognizing two pairs of local maxima of D values at A (Joint 9) and B (Joint 11) for Pipe 111 and at C (Joint 18) and D (Jointv19) for Pipe 24. The procedure for the damage identification thus boils down to the identification of the pipes in the map (as Figs. 4 and 5) bounded by a pair of joints with locally maximum D values. This selection can be achieved digitally in near real time. It is important to recognize that the water pressure can only be measured directly by a pressure gauge invasively introduced into the inside of the pipe. Hence, utility allows measurement of the water pressure only in absolutely necessary circumstances. Therefore, it is highly unlikely that densely populated invasive pressure gauges are used even for the

purpose of rapid identification of pipe damage. We will use noninvasive accelerometers to measure the pipe vibration on the surface of the pipe at the manhole and other locations accessible by utility personnel. The sensors we use in the laboratory and field studies are accelerometers and acoustic emission devices that are installed on the pipe surface to maintain a contact between their sensor head and the pipe surface. This research uses the assumption that the change in the acceleration in the hoop direction on the surface of water pipe can be used in place of D valve for the purpose of damage detection. This assumption is shown to be reasonable in Section V Preliminary Experiment. It is important to stress that acceleration change is used to develop contour maps similarly to Figs. 4 and 5. Then, damaged links are found from the map following the same rationale. It is in this sense that we say the acceleration change is used in place of D value.

2007 Urban Remote Sensing Joint Event

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III. PROPOSE DAMAGE INDNTIFICATION METHODOLOGY

A. Non-Invasive Detection

In this section, a damage identification method based on wireless MEMS-sensor network is proposed which is different from the Water Head Gradient-Based approach mentioned above in that the proposed method measures the acceleration change at pipe surface and therefore it is not invasive. To accurately locate the damage at a reasonable cost over a vast lifeline network, we must, however, support adjustable monitoring granularity through trade-offs among deployment density, sensor accuracy, wireless communication range, and costs.

B. Real-Time Monitoring

Increasing demand for rapid damage detection and assessment necessitates real-time monitoring capabilities. Real-time monitoring poses many challenges to designing sensor nodes, including fast communication links, fair and efficient media access protocols (MAC), and low-latency routing protocols. In our monitoring system, we use two different wireless interfaces. For short range communication, the Eco node uses a 2.4GHz custom radio with a data rate of 1Mbps. For longer range communication, DuraNode uses the 802.11b WiFi interface, whose maximum data rate is 11Mbps. Also, Eco and DuraNode use Time Division Multiple Access (TDMA) and 802.11b MAC, respectively, as their MAC protocol. Comparing with other wireless sensor platforms such as MICA2, Telos, and Stargate, Eco and DuraNode have higher or equivalent radio performance of in terms of maximum bit rate and radio range when used with the proper antenna.

IV. WIRELESS SENSOR NODES DESIGN

Both Eco and DuraNode can support the proposed real-time monitoring damage localization methodology. Although both of them can collect tri-axial acceleration data and transmit wirelessly, they are totally different platforms with complementary features, as shown in Table I.

TABLE I. COMPARISON OF ECO AND DURANODE

Eco DuraNode

Size (mm) 13 x 11 x 8 140 x 80 x 20

Sensor One H34C Three SD1221,

Gyroscope

Power Consumption (mW) Max. 100 Max. 1000

Max. Air Data Rate (bps) 1M 11M

Battery 30mAh Li-

Polymer 4000mAh Li-Ion

Wired Interface Serial, SPI Fast Ethernet, Optical

Wireless Interface 2.4GHz Custom Radio

WiFi / 2.4GHz Radio

Radio Range (m) 10 ~ 20 200 ~ 300

Cost ($) @ 1000 50 400

Eco is ultra-compact, low power, low cost, and is suitable

for dense deployment with a short wireless range. In contrast, DuraNode is equipped with three high-end, high-accuracy

accelerometers and a long range wireless interface (802.11b),

in addition to the same type of radio as Eco. At the same time, DuraNode consumes over ten times the power as Eco. We take

advantage of their characteristics and deploy a mix of these two

types of sensor nodes by adapting the choice to the specific

requirements on the spatial granularity of the water delivery

network. Two sensor nodes can communicate each other via

the available 2.4GHz wireless radio or RS232 serial interface.

This combination is expected to make the proposed real-time

monitoring methodology accurate and cost effective. For the

detail, see Reference [5].

A. DuraNode

Figure 6. Photos of DuraNode

Fig. 6 shows the picture of the DuraNode hardware. It consists of two boards: main board and daughter board. The main board has everything a wireless sensor node may need, including microcontrollers, sensors, and a wireless communication interface. On the other hand, the daughter board, as shown in Fig. 6(b), has only a microcontroller but two wired communication interfaces, namely Fast (10/100 Mbps) Ethernet and Optical. Another daughter card can provide the wireless link to a short-range network of Eco nodes. In addition, DuraNode can also use both wired and wireless interfaces in dual mode.

B. Eco

The Eco sensor node shown in Fig. 7 consists of four subsystems:MCU/Radio, Sensors, Power, and expansion port.

Figure 7. Photos of Eco Sensor Node

V. PRELIMINARY EXPERIMENTS

To show the effectiveness of our MEMS sensor-based real-time monitoring system, we set up a small water delivery network using 1-inch diameter PVC pipes. We installed four DuraNodes onto the network and collected vibration data in real-time. This section describes the details of our experimental setup and presents the 3-axial vibration data collected under two different levels of water pressure.

(a) On the Finger

(b) Top View

2007 Urban Remote Sensing Joint Event

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A. Experimental Setup

The water delivery network consists of seven pieces of 62-inch PCV pipes (with a 1-inch diameter), one PCV pipe cap, and one valve. As shown in Fig. 8 (b), we construct a rectangular-like pipe network, whose lengths on the two sides are 124 inches and 62 inches. Also, one valve is installed in the middle of the network (marked as VALVE in Fig. 8b) and one Cap (marked as CAP in the same figure) on the top-left corner of the network. The VALVE and CAP function as possible damage locations in the pipe network. We can increase the water pressure inside the pipe network by injecting water at WATER INPUT in the experiment. At low water pressure, the VALVE remains closed as it initially is. This status represents “No Damage in the Network”. However, when the pressure increases so that it is high enough to force the VALVE open, the pipe between nodes A and B is network can be considered “Damaged”.

In this experiment, CAP is kept closed throughout. Four DuraNodes are installed onto the pipe network, as shown in Fig. 8 (b) They keep transmitting 3-axial vibration data to a host computer via an access point in real-time. The sampling rate is set to 1KHz.

The CAP is used to vary the overall water pressure inside the pipe network. By manually setting to three different states, (CLOSE, HALF-OPEN, OPEN), we can adjust the maximum water pressure inside the pipe network (HIGH, MEDIUM, LOW).

Figure 8. Experimental Setup

B. Results

The results are 3-axial vibration data from four DuraNodes. Recording of the data begins when we start injecting water to the pipe network, and stop after a few tens of second when the VALVE is forced to open. Also, we repeat the same experiment at various levels of water pressure (HIGH, MEDIUM, LOW). In this section, we present only the X-axis vibration data in the high pressure condition (Fig. 8). Each graph in Fig. 9 shows the vibration data from each of the four DuraNodes labeled A, B, C, and D. The sudden change of vibration in each graph is developed when the VALVE is forced open by increasing water pressure.

Upon closer examination of Fig. 9, we find that the amplitude of each peak is different: Nodes A, B, C, and D have amplitudes of 0.5g, 0.28g, 0.35g, and 0.75g, respectively. These differences can be used to locate the damage in the pipe network.

This result shows that the sharp change in acceleration is recorded in real-time by all the four DuraNode and transmitted to host computer at the same time. It is important to note that the accelerations recorded before and after the opening of the valve is not only constant but also identical in this case. This verifies that change in the water pressure due to damage can be identified by the change in acceleration on the pipe surface not invasively. Having mentioned this, however, we are yet to find the way in which the observed values of amplitude of change in acceleration can identify the damage location.

Figure 9. Acceleration Data of 4 DuraNode under High Pressure

VI. CONCLUSION AND FUTURE WORK

Obviously, some more experiments must be carried out under more well equipped laboratory conditions for quantified detection of damage location. In addition, future work includes field experiments on a water network that is more realistic in scale than the one tested in the preliminary experiments described here. We plan to install Ecos and DuraNodes on a subset of a regimal water supply network such as City of Westminster and Irvine Water Ranch District systems. This will be done in conjunction with their existing SCADA measurement locations as available.

DuraNod

e

DuraNod

e

Water

Pipe Networ

k

Water Pipe

Network

DuraNode

DuraNode

DuraNode

DuraNode

(a) Photo of Water Pipe Network

CAP

Node B

Node ANode D

Node C

Valve

Water Input

62”62”

62”

31”

9.5”

62”

Diameter of the pipe: 1 ”

CAP

Node B

Node ANode D

Node C

Valve

Water Input

62”62”

62”

31”

9.5”

62”

Diameter of the pipe: 1 ” (b) Dimension of Water Pipe Network and locations where 4

DuraNodes are installed

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The main technical challenge will be to install these sensor nodes on the pipe surface. Observing that there are a large number of hydrants in these systems, it appears best to install them on the pipe at the hydrant locations.

ACKNOWLEDGMENT

This study was done under National Science Foundation Grant # CMS 0509018 and Grant # CMS 0112665. Their supports are immensely appreciated.

REFERENCES

[1] M. Shinozuka and X. Dong, “Evaluation of hydraulic transients and damage detection in water system under a disaster event,” 3rd US-Japan Workshop on Water System Seismic Practices, Kobe, January 26-28, 2005.

[2] M. Shinozuka, C. Park, P. Chou, and Y. Fukuda, “A sensor network for real-time damage location and assessment,” 3rd International Workshop on Advanced Smart Materials and Smart Structures Technology (ANCRiSST), Lake Tahoe, USA, May 29-30, 2006.

[3] M. Shinozuka and X. Dong, “Damage detection and localization for water delivery systems,” 5th International Workshop on Structural Health Monitoring, Stanford University, Stanford, CA, September 12-14, 2005, pp. 1267-1273.

[4] M. Shinozuka, C. Park, P. Chou, and Y. Fukuda, “Real-time damage localization by means of MEMS sensors and use of wireless data transmission,” SPIE Conference on Smart Structures & Materials/NDE, San Diego, CA, February 26 – March 2, 2006.

[5] C. Park and P. Chou, “Eco: ultra-wearable and expandable wireless sensor platform,” Third International Workshop on Body Sensor Networks, April 3-5, 2006. MIT Media Lab.

[6] C. Park, P. Chou, and M. Shinozuka, “DuraNode: wireless networked sensor for structural health monitoring,” 4th IEEE International Conference on Sens, Irvine, CA, Oct. 31 - Nov. 1, 2005.