A Wireless Sensor Node for Structural Health Monitoring ... - Gerges Dib

ME FELLOWSHIP AWARD PAPER w x

A Wireless Sensor Node for Structural Health Monitoring using Guided Wave Testing by Gerges Dib*, Ryan Lattrel†, Lalita Udpa‡ and Guang Yang§

ABSTRACT

The use of wireless sensor networks in structural health monitoring can significantly increase safety and reduce manufacturing and maintenance costs. Wireless sensor networks typically use low footprint smart active sensor nodes that are permanently mounted on the structure, have the processing power to detect changes in the structure indicating impending hazards and communicate wirelessly with other sensor nodes or base stations when anomalies are detected. This paper presents the development and implementation of sensor node hardware and software for passive and active sensing of elastic guided waves. A distributed control algorithm is presented for controlling a wireless sensor network from a base station. The overall system is applied using a network of lead zirconate titante sensors mounted on a thin aluminum plate. Initial results of damage detection using the system demonstrate the feasibility of the approach followed. KEYWORDS: wireless sensor networks, guided wave testing, lamb waves, structural health monitoring.

* Nondestructive Evaluation Laboratory, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824; e-mail [email protected]. † Nondestructive Evaluation Laboratory, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824; e-mail [email protected]. ‡ Nondestructive Evaluation Laboratory, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824; e-mail [email protected]. § Nondestructive Evaluation Laboratory, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824; e-mail [email protected].

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Introduction Wireless sensor networks for structural health monitoring (SHM) enhance the ability to continuously monitor structures, enabling the detection of cracks, corrosion or other damage types at their onset. Such sensors can provide an early warning system that could prevent unanticipated disastrous events. Also, by having an early warning system, the factors of safety in structural design can be relaxed, allowing for the manufacture of lighter and cheaper structures. Wireless sensor networking also makes SHM more reliable by eliminating single points of failure inherent in traditional, cabled SHM systems. The architecture of a simple wireless sensor network for SHM is composed of two different entities: the sensor nodes and the base station (Figure 1). One base station communicates and controls the network of sensor nodes mounted on the structure. The base station has a gateway responsible for transferring data from the network to a PC and vice versa. The PC has a user interface where an administrator can control and configure the network or individual sensor nodes. It also has visualization and automated analysis tools indicating the presence of potential hazards in the structure. The sensor nodes are smart, low power devices equipped with signal conditioning and data acquisition devices, microcontroller and/or processor, digital memory, power supply

Figure 1. The schematic of a centralized wireless sensor network consisting of two entities: the sensor nodes and base station. The sensor nodes are mounted on the structure and connected to embedded or surface bonded transducers. The base station is a remote PC that collects data for processing from the sensor nodes.

and a radio. Multiple sensor nodes are deployed on the structure in predetermined positions, forming a wireless sensor network. The main power source in a sensor node is a battery. Additional power supplies that could harvest energy from the environment or from other nearby devices could be added, depending on the environment where the sensors are deployed. Because of power scarcity, the computational speed, wireless transmission power and data acquisition speed are limited. The presence of a microcontroller enables total control of the sensor node, rendering wireless sensor networks highly configurable and automated. This is achieved by programming the microcontroller using software. Detailed information about each functional module in a wireless sensor network can be found in another paper (Akyildiz et al., 2002). A second paper gives a general description of sensor node platforms and specifically describes 17 different hardware sensor nodes that were designed in academic labs and three types of commercial sensor nodes (Lynch and Loh, 2006). The sensor nodes described were employed and tested for collecting vibration measurements, mainly using accelerometers and strain gages. The use of sensor nodes for global-based damage detection is becoming very popular, but there are few sensor nodes that are compatible with local-based damage detection, and in particular guided wave SHM. Unlike globalbased damage detection such as modal analysis, guided wave SHM requires high data acquisition rates, where signal bandwidth could be up to 1 MHz. Moreover, guided wave SHM is an active inspection technique and requires an actuation interface. Researchers have developed a sensor node that could be interfaced with lead zirconate titanate (PZT) transducers (Liu and Yuan, 2008). They implemented dual controller architecture, a field-programmable gate array directly controlling the analog-to-digital converter (ADC) and storing samples in memory, and a microcontroller unit that controls other parts of the sensor node. This allows dedicated control of the sensing interface, increasing performance and minimizing power consumption. However, the sensor node described in an earlier work does not have an actuation interface. Researchers at the University of California San Diego developed a sensor node with the capability to actuate signals up to 1 MHz frequency and a sampling frequency up to 10 MHz (Musiani et al., 2007). This sensor node’s power is supplied solely by solar energy harvesting cells. Another outside study developed an intelligent standalone ultrasonic device with an ADC sampling rate of approximately 8 MHz and actuation capability of signals with 1 MHz frequency (Pertsh et al., 2011). The sensor node was tested by interfacing it with ultrasonic wedge transducers. However, to provide the high performance needed, the sensor node requires large batteries and need to have an overall large size compared to commercial sensor nodes. This paper aims at integrating wireless sensor nodes with guided wave testing (GWT) techniques using PZT piezoelectric wafers as transducers. This requires the design of a low

power, small size sensor node capable of acquiring highfrequency ultrasonic waves at a high resolution. This work is based on extending the hardware functionality of the mote platform initially developed at the University of California, Berkeley (Hill and Culler, 2002). The platform is designed to be modular, where the basic wireless module (microcontroller and radio) can be extended by connecting it to external sensing modules depending on the application. The use of GWT is attractive for SHM applications since such waves can be excited at one point and can propagate long distances with little attenuation (Alleyne and Cawley, 1992). This allows the inspection of large areas in a structure using a minimal number of sensing points. PZT piezoelectric wafers are used as transducers for the excitation and sensing of guided waves in the structure. PZT wafers are most commonly used in SHM because of their low cost, light weight, small size and the practicality of surface bonding or embedding them in a structure (Sirohi and Chopra, 2000). An outside work gives a detailed description of sensors and the other different components and theories required in guided wave SHM (Raghavan and Cesnik, 2007). The overall approach of a centralized wireless sensor network proposed in this paper is shown in Figure 1, which comprises multiple sensor nodes and one base station. In this setup, transducers are bonded or embedded in the structure and connected to a sensor node. The sensor node has three hardware modules: instrumentation and data acquisition circuitry, which transforms analog measurements from the transducer into a voltage signal that could be digitized or converts a digital signal into analog signal that is applied to the transducer for actuation; a processing module that coordinates the behavior of all the components of the sensor node, stores acquired measurements and performs basic signal processing to determine if any event has occurred; and a radio frequency interface for wireless communication. At the other end, the base station has a user interface that allows administrators to control and configure sensor nodes in the network. For example, the sensor node can be configured to act as an actuator or a sensor. Any event detected at the sensor node is transmitted to the base station. The base station then combines information in the data from different nodes to determine the presence and location of damage. The rest of this paper is organized as follows. The hardware development for the sensor nodes is discussed, followed by software development for the sensor nodes, the base station and the wireless communication in sections on sensor node application wireless communication. The functionality of the system is then validated and the power consumption and lifetime of the sensor node are analyzed in the section on system validation. Finally, the feasibility of using the sensor nodes for GWT of aluminum plates is demonstrated in the section on damage detection, followed by concluding remarks.

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TABLE 1

Properties of the mote sensing module, processing module and radio frequency module Module

Component

Properties

Sensing Processing

Analog-to-digital converter Microcontroller Storage Radio

10-bit resolution, 273 ksps sampling frequency 7.37 MHz processing clock, digital input/output interface pins 8 kB RAM, 512 kB Flash memory IEEE 802.14.5 compliant, 250 kbps bit rate, 50 m indoor range, 300 m outdoor range

Wireless communication

Hardware Development

The hardware components and properties of the sensor node used in this work are summarized in Table 1 (Memsic, 2012). It operates on two AA batteries and was designed using hardware components with very low power consumption for maximizing the lifetime. The power limitation imposes constraints on the processing speed, since increasing the microcontroller’s clock frequency will increase power consumption. For the data acquisition, the sensor node has a 10-bit ADC, but the sampling frequency is limited to 273 ksps, which is not sufficient for sampling ultrasonic guided wave signals having bandwidth up to 500 kHz. The sensor node has a 51-pin connector that makes peripheral interfaces of the microcontroller available for connection to external boards. This connector was used to provide an extension circuit board for signal conditioning that would allow the acquisition of narrowband ultrasonic signals with a central frequency up to 1 MHz from the PZT wafer. The extension circuit board also provides an actuation interface that could convert a digital square signal into an analog signal for exciting the PZT wafer. The extension circuit board was designed using off-the-shelf circuit components and operational amplifiers. The extension circuit board connected to the sensor node is shown in Figure 2a. The block diagram of the extension circuit board is shown in Figure 2b. The extension circuit

(a)

board provided its own power source using four AAA batteries. A digitally controlled switch selects the actuation or sensing functionality of the PZT wafer by connecting it to the appropriate circuits. The switch position is controlled directly by the microcontroller using a single general-purpose input/output (GPIO) digital pin provided by the 51-pin connector (Atmel, 2012). The following subsections describe the sensing and actuation circuits in more detail. Actuation Circuit

When the switch is programmed to be in the actuator position, the actuation circuit will be directly connected to the PZT wafer. The input signal for the actuator circuit is provided via a GPIO pin. The GPIO pin is programmed to behave as an output pin, and the microcontroller can be programmed to provide a square wave of a given frequency and number of cycles, which is applied to a second order bandpass filter to limit the bandwidth of the square wave. The detailed circuit schematic for the bandpass filter is shown in Figure 3a. The filter was designed such that its center frequency could be varied to match the center frequency of the square wave input from the GPIO. The filter center frequency is controlled by a digital variable resistor, R (Analog Devices, 2012). It has 10-bit programmable wiper positions and a maximum resistance of 20 kW. The position of the rheostat wiper is selected digitally through a two-wire serial

(b)

Figure 2. The extension circuit board: (a) connected to the sensor node using the 51-pin connector (the sensor node has a total area less than 2500 mm2); and (b) block diagram showing the sensing and actuation modules and the switch that selects which module will be connected to the lead zirconate titanate wafer.

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interface, allowing the microcontroller to have direct control on the filter properties. The relationship between R1 and the filter center frequency, fc, is found by solving the transfer function of the filter circuit, and is given by Equation 1. (1)

R1 =

1 4.74 × 10

−14

fc 2 − 1.67 × 10−5

Since the maximum R1 value is 20 000 W, the minimum excitation frequency that could be supported is 38 kHz. Also, because of the limitation of the GPIO pin, resolution of the digital rheostat and bandwidth of the amplifiers used in the filter design, the maximum supported excitation frequency is approximately 500 kHz. The second stage in the actuation circuit is a non-inverting amplifier that amplifies the filtered signal and acts as a buffer between the PZT wafer and the bandpass filter. The voltage gain of the amplifier is controlled by another digital rheostat, R2. Sensing Circuit

When the switch is programmed to be in the sensor circuit position, the PZT is connected directly to the signal conditioning and sensing circuit. The first stage in the sensing circuit is a charge amplifier. The PZT wafer behaves as a capacitor when it vibrates, accumulating charge at its electrodes (Sirohi and Chopra, 2000).

(a)

(c)

A charge amplifier is used to transform the charge into a voltage signal. The schematic for the charge amplifier is shown in Figure 3b. If the PZT wafer has a capacitance of Cp, the relationship between the output voltage of the charge amplifier and the PZT charge is given by Equation 2. (2)

H(ω ) = −

jωRCp 1 + jωRC

Equation 2 shows that the charge amplifier behaves as a high-pass filter with cutoff frequency wn = 1 / RC, and gain Cp / C. Thus, by decreasing the value of the feedback capacitor, C, relative to the PZT capacitance, Cp, the amplifier gain can be increased. It should be noted that R should be large to keep the cutoff frequency of the filter as low as possible. The second stage in the sensing circuit is the envelope detector, used to demodulate narrowband ultrasonic signals to a baseband frequency so that the bandwidth is small enough to be sampled by the ADC. The envelope detector implementation consists of two parts, as shown in Figure 3d. The first part is a full wave rectifier, which extracts the absolute value of the input signal. The second part is a third order Butterworth low-pass filter with a cutoff frequency of 60 kHz. The filter output is an envelope of the absolute value of the signal. The filter also acts as an antialiasing filter before

(b)

(d)

Figure 3. Circuit diagrams: (a) actuation circuit including a center frequency programmable bandpass filter and a gain programmable amplifier; (b) the charge amplifier with its input connected directly to a lead zirconate titanate (PZT) wafer; (c) digitally programmable threshold voltage generator; and (d) envelope detector with digitally programmable gain.


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sampling. The sensor gain can be modified by programming the wiper of the digital rheostat, R3, enabling the gain to vary between 0.5 and 26 dB. The third stage is the triggering circuitry, which is used when the sensor node is programmed to be in passive mode. The mote microcontroller has an embedded comparator that can trigger a software interrupt when an event is detected. One input of the comparator is connected to the output of the envelope detector, and the other input is connected to a programmable threshold voltage. When the signal envelope is larger than the threshold voltage, a software interrupt enables the mote ADC to start sampling. The programmable threshold voltage is implemented using a voltage divider, shown in Figure 3c. A Zener diode with a constant voltage of 1.225 V is used to provide a stable voltage to the voltage divider. A digital rheostat, R3, enables changing the threshold voltage in the software. The relationship between the threshold voltage and R3 is given by Equation 3. (3)

R3 =

100Vthresh 1.225 − Vthresh

The value of R3 can vary between 60 and 100 kΩ, using 1024 steps (10 bits). This allows Vthresh to vary between 0.7 and 610 mV with steps of approximately 0.6 mV. Software Development Software applications are installed into the sensor node’s microcontroller, allowing the developer to program the nodes and wireless sensor network. Software applications are built in hierarchal layers. Low-level layers describe how the microcontroller would communicate with peripherals such as the ADC, wireless radio and extension board; high-level layers include data analysis and processing algorithms. There are two different software applications in a centralized wireless sensor network: the sensor node application and base station application. Sensor Node Application

The software for the sensor node was written using a language developed specifically for the mote platform, but was extended to support multiple other platforms (Levis et al., 2005). The advantage of this language is that it is an open-source operating system freely available for the public, and is intended to maximize the performance of low power applications. It needs only 256 bytes of RAM to run and is written using nesC programming language, which is based on the C programming language but designed to be easier to write applications that are triggered by external environmental factors, which made it easier to program these applications (Gay et al., 2003). The mote application programs the microcontroller and manages the interaction with its peripherals, including the extension circuit board and wireless communication radio. The mote is programmed so that its state changes depending on commands received 1136


Power on

Sensor board readable

No

Node shutdown

Yes Transmit node info

Network request

Actuate mode Excitation signal sent

Idle listening

Sense enable mode Actuate command snooped

Passive sense mode

Vsense > Vthresh

No

Yes 100 samples stored and transmitted to base station

Analog-to-digital converter

Figure 4. The mote application state machine. After the mote powers up, if it is able to read the sensor board parameters, it goes into idle listening mode where it waits for commands from the base station that could alter its state.

wirelessly from the base station. The state machine for the mote application is shown in Figure 4. When the mote is powered up, it attempts to read the values of digital variable resistors in the extension board through the serial bus. There are four different variable resistors controlling four different parameters described in the previous section: actuation bandpass filter center frequency, actuation gain, sensing gain and sensing threshold voltage. If reading those values does not succeed, the mote will assume that the extension circuit board is not connected and will shut down. If the read is successful, the mote will go into idle listening mode, where it will wait to receive commands from the base station via the wireless communication channel. There are four different commands from the base station that could change the state of the mote, prompting it to take action. ● Network request command: the mote will transmit the values of the variable resistors to the base station. ● Actuate command: this command should also include data specifying the actuation frequency, actuation gain and the number of cycles required for the actuation signal. The mote then sets the variable resistor values that correspond to the frequency and gain requested. The mote will output a square wave with the given frequency and number of cycles at the actuation GPIO pin. If this is successful, an acknowledgement is sent back to the base station, and the mote goes back to the idle listening state. ● Sense enable command: this command prepares the sensor node for an active sensing inspection. The command includes data specifying the sensing gain value. The mote starts snooping the wireless channel for actuation commands transmitted to

●

other neighboring motes. When it detects an actuation command, it will enable its sensing circuit and the ADC, store 100 data samples, and then transmit them to the base station. The mote will then go back into the idle listening state. Passive sense command: this command puts the sensor node in passive sensing mode. The command also includes data specifying the sensing gain and threshold voltage value. The mote enables its sensing and triggering circuit, and updates them with the received values. Measurements from the sensing circuit are then continuously compared to the given threshold voltage. When the signal level becomes higher than the threshold voltage, the ADC is enabled, and 100 data samples are stored and transmitted to the base station. The mote will then go back into the idle listening state.

Base Station Application

The base station consisted of a PC with a user interface for viewing and sending commands to the active sensor nodes in the network. The user interface lists all the active sensor nodes in the network and their parameters. Commands are sent to each node or broadcasted to all the nodes, allowing a practical configuration of the wireless sensor network and the individual parameters of the motes. This design allows for both active and passive sensing. For passive sensing, a passive sense command is broadcasted to all the nodes in the network with a given threshold voltage and sensing gain, setting the required sensitivity. This allows passive monitoring of the structure for impact damage or acoustic emissions from growing cracks. To put the network in active sensing mode, a sense enable command is broadcasted to the network. An actuation command is transmitted to the desired sensor node. The user interface also plots the time signals received from the sensor nodes when an event is detected, allowing further signal processing for damage detection and localization. Wireless Communication The sensor node has a radio compliant with IEEE 802.15.4 (Table 1), operating in the unlicensed industrial, scientific and medical band between 2.405 and 2.480 GHz, with the ability to select between 11 different communication channels. This standard is similar to Wi-Fi but is modified to suit low data rate and low power applications (IEEE, 2003). It has only a 250 kbps bit rate compared to 11 Mbps in Wi-Fi. Because of the low data rate in the wireless communication physical layer, upper layer communication protocols that are common in Wi-Fi networks and the internet, such as the “Transmission Control Protocol/Internet Protocol,” are not a suitable option for wireless sensor networks (IETF, 1989). The operating system used had its own implementation of a lightweight and simple communication protocol for transmitting and receiving data packets. This protocol has very little overhead, maximizing the data rate throughput. It is a best effort protocol and thus packet loss could occur. To minimize the number of packets lost, the receiver is programmed to

send back an acknowledgment for every packet it receives. If the transmitter does not receive an acknowledgement, it will transmit the same packet again until it receives the acknowledgement. This helps decrease the amount of data loss when there is substantial traffic in the wireless channel. System Validation An experimental setup was used to verify the functionality of the sensor nodes and the ability to configure the wireless sensor network. The actuation circuit was tested first, and then two sensor nodes were configured from the base station and tested for the ability to generate and detect guided waves. Actuator Circuit Validation

The actuation circuit was designed to give the sensor node the capability to actuate narrowband signals. The excitation signal most commonly used for exciting guided waves is the Hanning windowed tone burst signal (Alleyne and Cawley, 1992; Wang et al., 2008). To investigate the performance of the actuator, the extension board was connected to the sensor node, and the actuator circuit input (the same as the output of

(a)

(b)

Figure 5. Graphs showing: (a) the actuation circuit output signal and the square wave provided at its input; and (b) the frequency spectrum of the square wave and of the signal at the output of the actuation circuit. The spectrum of a seven-cycle Hanning windowed tone burst is also shown for comparison.


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the GPIO pin from the sensor node) and output were connected to an oscilloscope. From the base station, an actuate command was transmitted to the sensor node with the parameters: 320 kHz frequency, 3.5 dB gain and two cycles. The resulting output signal of the actuation circuit and the square wave used as input are shown in Figure 5a. The frequency content of the two signals is shown in Figure 5b. It could be seen that half the output signal power was within a bandwidth of 75 kHz around a center frequency of 322 kHz. The output signal has a similar shape and frequency content as a seven-cycle Hanning windowed tone burst, whose spectrum is also shown in Figure 5b. Sensor Circuit Validation

The experimental setup shown in Figure 6 was used to test the performance of the sensor nodes for active GWT. Two PZT wafers were bonded to an aluminum plate using epoxy,

and each PZT wafer was connected to a sensor node. Sensor node 1 was used for actuation and sensor node 2 was used for sensing. The outputs of the charge amplifier and the envelope detector of sensor node 2 were connected to the oscilloscope. From the base station, a sense enable command was transmitted to sensor node 2 with the sensing gain parameter specified as 6 dB. Then, an actuate command with the same parameters used for the actuator validation was transmitted to sensor node 1. The resulting sensed signal and its envelope recorded on the oscilloscope, as well as the envelope data samples received at the base station, are shown in Figure 7. The envelope signals have been scaled to compensate for the sensing gain and attenuation of the low-pass filter in the envelope detector. The charge amplifier output was time shifted to compensate for the group delay of the low-pass filter. The envelope detector had good sensitivity to the data variation and was seen to detect all the peaks of the wave packets. The interpolated digital signal samples transmitted wirelessly to the base station closely matched the analog envelope signal from the oscilloscope. Power Consumption

Figure 6. Experimental setup for the validation of the sensing circuit. Two lead zirconate titanate (PZT) wafers with dimensions 8  7 mm were bonded to the aluminum plate. Sensor node 1 was connected to PZT 1 and sensor node 2 was connected to PZT 2. The outputs of the charge amplifier and the envelope detector of sensor node 2 were connected to the oscilloscope.

Figure 7. The guided wave signal and its envelope, as recorded on the oscilloscope, are shown in green and blue, respectively. The sampled envelope data that was transmitted to the base station and their interpolation is also shown. The S0 and A0 modes incident wave packets are indicated. All of the latter wave packets in the signal were due to reflections from the edges of the plate.

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One major challenge in wireless sensor networks is power constraint. Special attention is needed to keep power consumption to a minimum in order to extend the lifetime of batteries used. In the designed sensor node, six batteries, namely, two AA batteries solely powering the sensor node and four AAA batteries solely powering the extension board, were used. The drawn current was measured while the mote was operating in different modes. Table 2 shows the power consumption of the sensor node and the extension circuit board in each operating mode. ● Idle listening mode: all the amplifiers in the extension board were disabled, and the current drawn was due to quiescent current of the amplifiers and triggering circuit. The current consumed by the sensor node was mostly due to the wireless radio, which was in listening mode. ● Sensing enabled and passive sensing modes: the amplifiers in the sensing circuit were enabled and consumed most of the power. The sensor node wireless radio stayed in listening mode and drew most of the 17 mA current, while the microprocessor was in sleep mode and consumed negligible current. ● ADC enabled: the microprocessor woke up, consuming the extra current. However, the mote stayed in this mode for just a few hundred microseconds, and then reverted back to the idle listening mode. ● Actuate mode: the actuation circuit was enabled, and the sensing circuit was disabled. Since the actuation circuit had only two operational amplifiers, it consumed only 11 mA. The sensor node should have had its microprocessor enabled and thus consumed the extra energy. The mote stayed in the actuate mode for just a few microseconds to send the excitation signal and reverted back to idle listening mode.

TABLE 2

The current drawn by the mote and extension circuit board for different states of the sensor node Mode Idle listening Sensing enabled Passive sensing Analog-to-digital converter enabled Actuate

Mote current

Extension board current

17 mA 17 mA 17 mA 24 mA 24 mA

0.5 mA 25 mA 25 mA 25 mA 11 mA

Using the values in Table 2, the average lifetime of the batteries could be calculated. If a sensor node is used mainly for passive sensing, it will spend most of its time in the passive sensing mode. The extension circuit board operated on four AAA batteries; each typically has 1200 mAh of energy stored. The extension board continuously draws 25 mA, and the energy stored in the batteries will then last up to eight days. The mote operates on two AA batteries; each typically stores 2700 mAh of energy. The mote batteries will then last 13 days if the mote is continuously in the passive sensing mode. If a sensor node is used mainly for active sensing, the mote will spend most of its time in idle listening mote. It goes into actuate mode, sensing enabled mode or ADC enabled mode for just a few microseconds, but it will go back to the idle listening mode once a test is over. Based on this, the extension circuit board batteries can potentially last more than a year, but the mote batteries will last 13 days since the radio is continuously listening for commands. The battery life in the mote can be extended using duty cycling in the radio, decreasing its power consumption by 90% (Buettner et al., 2006). Damage Detection The feasibility of using the sensor nodes for damage detection in an aluminum plate by GWT is demonstrated in this section. When elastic waves propagate in a plate with thickness smaller than the wavelength, guided waves are formed by the continuous reflections from the free surfaces of the plate. Those guided waves are called lamb waves and their propagation is governed by the Rayleigh-Lamb dispersion equations (Viktorov, 1967). The dispersion equations show that the propagating waves are highly dispersive and that, at any given frequency, there are at least two propagating modes: antisymmetric (A0) and symmetric (S0). Several signal-processing algorithms for damage detection exist in the literature. This section adapts a baseline subtraction technique for damage detection and localization (Croxford et al., 2007). To demonstrate the feasibility of this approach with lamb wave signals, a set of four aluminum plates, each with a notch of varying depth, was first used for comparison purposes. Figure 8 shows how the reflection from an anomaly changes with changing anomaly depth.

Figure 8. Obtained signal for four plates. One plate had no anomalies, and three plates had an anomaly with varying depth.

For testing with the wireless sensor nodes, the experimental setup shown in Figure 6 was firstly used to obtain a baseline signal when no damage was present. The actuation signal had a center frequency of 320 kHz, where the S0 mode had a so-called sweet spot (Giurgiutiu, 2002). This means that the PZT wafer had a maximum S0-to-A0 strain response ratio. Operating at the sweet spot eliminated the complications that could arise in signal processing due to the presence of multiple reflections from anomalies and edges due to multiple propagating modes. Using the results obtained in the sensor circuit validation section shown in Figure 7, it was seen that the incident S0 mode amplitude was much higher than that of the A0 mode. The group velocities of the A0 and S0 modes at 320 kHz were calculated using the time of arrival of the incident waves. Knowing that the distance between the two PZT sensors was OCTOBER 2013 • MATERIALS EVALUATION

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130 mm, the A0 mode group velocity (cA0) was found to be 2550 m/s, and the S0 mode group velocity (cS0) was 4690 m/s. Next, the plate in the experimental setup of Figure 6 was substituted by another plate with the same dimensions but having a rectangular notch introduced as an anomaly. Two other PZT wafers were bonded at the same positions used in the healthy plate. The aluminum plates used were 600  600 mm and 2.54 mm thick. The size and position of the notch relative to the sensor nodes are shown in Figure 9a. Sensor node 1 was sent an actuate command, and sensor node 2 recorded the response and transmitted it to the base

(a)

station. The signal envelope from the plate with the damage and the baseline envelope are plotted in Figure 10. The difference of the two signals normalized by the healthy plate signal envelope is also shown. To calculate the difference signal and find the probable presence and location of damage, the following simple procedure was used: ● The absolute value of the difference between the healthy plate and damaged plate envelopes was calculated. ● For each sample point in the difference signal, if the corresponding sample value of the damaged plate envelope was above a threshold value of 50 mV, the difference signal sample point was normalized by the corresponding healthy plate data sample. Thresholding removed the possibility of getting large peaks when the healthy plate envelope normalization value was near zero. The normalization procedure gives greater tolerance to variations in the signal due to differences in the PZT wafer bonding in the two plates and due to synchronization errors, quantization errors and jitter in the ADC. ● The time of arrival of the reflected wave was calculated as: t = tdamage – tactuation, where tactuation is the peak value of the actuation signal. From Figure 5a, it was found that tactuation = 7 µs, and from Figure 10 it was found that tdamage = 76.5 µs. Thus, t = 69.5 µs. ● The distance that the S0 wave packet traveled from PZT 1-to-damage and damage-to-PZT 2 was d = cs0t. Reflections of the A0 mode or mode conversion of the S0 mode to A0 at the damage were not considered, since it was assumed that the PZT wafer response would be too small for such reflections to be considered. Only the S0 mode reflections were considered. Using this assumption, the distance traveled was calculated based on the velocity of the S0 wave. The probable location of the damage could then be found by drawing the ellipse with foci being the positions of PZT 1 and PZT 2, and a major axis length of d. The ellipse is shown in Figure 9b, and it is seen that the ellipse passes through the damage location.

(b) Figure 9. Diagrams showing: (a) a 600  600 mm aluminum plate with thickness of 2.54 mm used for damage detection, along with the positions of the two lead zirconate titanate wafers and the notch (the rectangular notch size was 18  3 mm and was 0.6 mm deep); and (b) the determined probable locus of the damage position.

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Figure 10. The baseline and damage plate sensed signal envelopes. The normalized difference between the two signals is also shown.

This procedure allowed detection of damage and the probable location of damage using data from multiple sensors. When data from three or more sensor nodes was available, the base station could aggregate the data to form an image indicating the presence of damage in the plate. Data fusion schemes that allow the construction of such images from multiple sensors have been investigated by others (Su et al., 2009). Conclusion Traditional SHM techniques employ sensor nodes for globalbased damage detection accelerometers and strain gages. This paper presents a wireless sensor network scheme with sensor nodes with active sensing that are compatible with local-based damage detection. This paper presents the hardware design extension for a commercial sensor node used for the acquisition of guided wave signals from PZT wafers. Unlike globalbased damage detection such as modal analysis, guided wave SHM requires high data acquisition rates, where signal bandwidth could be up to 1 MHz. A complete approach for the use of wireless sensor nodes for guided wave SHM was presented, including the data acquisition and actuation hardware design, embedded software design, wireless networking and the application of signal processing algorithms for damage detection using wireless transmitted data. The wireless sensor network operation was demonstrated with two sensor nodes in the network. Extension of this work is underway to make the network scalable to tens of sensor nodes. This would require implementation of a set of wireless networking protocols on top of the active message protocol used in this paper. Future work also includes development of a data collection protocol to ensure that not all sensor nodes will be transmitting at the same time, and data hopping protocols to ensure that sensor nodes that are farthest away from the base station can still communicate with it. REFERENCES

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