Wireless Sensor Network Structural Health Monitoring

Wireless Sensor Network Structural Health Monitoring Abderrazak Abdaoui

Lionel Fillatre

Eric Chˆatelet

Abderrahim Doumar September 2, 2010

1

Stating the problem

Large civil structures such as buildings and bridges form the backbone of our society and are critical to its daily operation. Inspectors typically assess them manually, but a networked computer system that could automatically assess structural integrity could measurably lengthen a structure’s lifetime, reduce its operational cost and improve the overall public safety.

1.1

Context

Structural health monitoring (SHM) is a highly active area of research devoted to developing the tools and techniques needed for automatic structural integrity assessments. This area consists of estimating the state of structural health, or detecting the changes in structure that affect its performance [1][2]. The two major factors which may be controlled in a structure are time-scale of change and severity of change. The time-scale is how quickly the change occurs, and the severity is the degree of change in the structure. Two major categories of SHM are disaster response (earthquake, explosion, etc.) and continuous health monitoring (ambient vibrations, wind, etc.). There are two SHM 1

approaches: direct damage detection (visual inspection, x-ray, etc.) and indirect damage detection (change in structural properties/behavior). Indirect detection, especially through vibration, is used.

1.2

Benefits from using Wireless Sensor Networks

SHM itself is not a new concept. The conventional method uses personal computers (PCs) wired to piezoelectric accelerometers. However, this method has drawbacks in that i) wires have to run all over the structure, so this method may disturb the normal operation of the structure, ii) the equipment’s cost is high, iii) installation is very expensive due to wiring, and iv) its maintenance’s cost. Recent years have seen growing interest in SHM based on wireless sensor networks (WSNs) due to their potential to monitor a structure at unprecedented temporal and spatial granularity. However, there remain significant research challenges in SHM with WSN. The use of WSN in SHM applications meet the stringent resource and energy constraints of WSNs. Compared to the conventional method, the use of wireless sensor network in SHM provides the same functionality at a much lower price which permits much denser monitoring. The advantage of WSN based structural health monitoring can be improved by microelectro mechanical systems (MEMS) accelerometers. Since the MEMS accelerometer is a silicon chip, it is very compact in size, consumes little energy, and is inexpensive. Without MEMS accelerometer, WSN’s small size, low power and low cost will be degraded.

1.3

Challenges to Wireless Sensor Networks

Usually, for structural health monitoring, we need to read acceleration signals down to 500 µG (1G is the gravity), at a frequency higher than 1 kHz synchronously at all nodes. In addition to these real-time high fidelity performance requirements there are other specific requirements : 2

ˆ High accuracy of sample which means the final reading needs to detect signals down

to 500 µG without significant distortion. Sources of distortion contain the noise floor of the system including accelerometer, amplifier, analog to digital converter, installation error, and temperature variation, etc. ˆ High-frequency sampling: this implies low jitter which represents the variation in

sampling intervals. ˆ Time synchronization: sampling needs to start at the same time on all nodes,

although the sampling should be done over multiple nodes across the entire network. Furthermore, this needs to be done in spite of differences in drift of each clock. Otherwise, shifts in signals between different nodes can give a distorted picture of the structure. ˆ Large-scale multi-hop network: in case where the structure spans a long distance,

it is impossible to cover the entire structure with single hop communication. So a large-scale multi-hop network is necessary to provide connectivity. ˆ Reliable command dissemination: if some nodes fail, data will be missed for those

points, which makes analysis very difficult or impossible. ˆ Reliable data collection: not only commands, but also data needs to be transferred

reliably. Missing samples make the analysis hard or even impossible.

2

Continuous vibration-based monitoring

This section gives a complete description of the application. Among the different available damage detection methods, the class of vibration-based damage detection techniques is one of the most popularly used methods to monitor the condition of structures. In [3], the authors give experimentation of WSN on a bridge in Postdam, NY USA (Figure 1). 3

Figure 1: Sensor locations for studying bridge vibration along the span. This bridge is a three simple span steel-girder bridge. About one third of the span at northern end of the bridge is easily accessible from the ground. This location has been used to characterize the traffic-induced vibration. The time series analysis of acceleration obtained by the accelerometer was double integrated and high-pass filtered with cutoff frequency of 1 Hz to obtain time histories of displacement in Figure 2(a) and amplitude spectrum in Figure 2(b). From this experiment, we can notice the following observations. First, the bridge vibrations can be characterized as highly non stationary with traffic excitation creating bursts of vibration that persist for several seconds and very low levels of vibration between the bursts. Second, we observe that the vibration levels at supports are minimal and reach the maximum at the middle of the span. Third, the natural frequencies of vibration excited by traffic will depend on structural geometry. Fourth, the amplitude of displacement at a certain frequency depends on a specific location along the girder and may require modal analysis [4] for optimal selection of fundamental frequencies used for harvesting. Another test of the bridge of Potsdam demonstrates the sensing and the data transmission operations of the bridge sensor. The wireless sensor node was packaged inside a steel enclosure and chained to the bridge. Wireless transmissions from the bridge sensor 4

were received by a data-logger device that stored both the temperature reading and the time stamp. The results of the test are summarized in Figure 3(a)-(c). The total number of transmissions per each day of monitoring is shown in Figure 3(a). However, Figure 3(b) gives the average number of transmissions per hour over a week of monitoring. The temperature readings captured during the week of monitoring are shown in Figure 3(c).

(a)

(b) Figure 2: (a) Time series and (b) frequency spectrum of girder displacement.

2.1

Acoustic emission analysis versus beamforming

Passive structural health monitoring techniques such those using strain measurements and modal analysis can be used to provide some indication of the health of a structure. However, the method of acoustic emissions offers the potential for damage localization 5

(a)

(b)

(c) Figure 3: (a) Number of transmissions per day of week, (b) Average number of transmissions per time of the day and (c) Temperature readings over a week of monitoring. and predictive capabilities. The method of acoustic emission is usually very costly compared to other forms of structural health monitoring because it requires very high sampling rates and, for localization analysis, extremely precise time synchronization between

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many sensors. Additionally, the results of acoustic emission analysis are often difficult to interpret and hard to relate to the health of the structure. Traditional analysis use either several sensors and “count” acoustic emissions or collect statistical measures of acoustic emissions which are very qualitative in nature or they rely on a very large array of at least eight sensors distributed over a large area in order to locate the sources of the acoustic emission analysis via triangulation [5]. Conventional acoustic emission source location methods use complicated analysis routines, rely on very accurate arrival time information from arrays of sensors physically located many meters apart, and require exact time synchronization of the data acquisition systems. Inaccurate detection of the arrival time of the direct P wave often causes the source location systems to fail. Additionally, small errors in arrival times and wave velocity estimates can be magnified by the effect of blind spots in the sensor array. On the contrary, beamforming methods [6] can be used to accurate azimuthal direction of arrival determination and for rough localization without the use of complicated analysis routines such as the picking of P-wave arrival times. Additional signal discrimination can be achieved through the use of array processing techniques. Finally, the beamforming approach relaxes the need for exact time synchronization between spatially distant sensor arrays, which allows wireless sensors to be used for acoustic emission applications. Wireless sensor networks associated with the method of acoustic emissions are described in [7]. These wireless systems are easy to install, easily scalable to the many different types and sizes of civil engineering structures and are more cost efficient than traditional wired systems. Acoustic emission beamforming is an analysis approach which is drastically different from traditional acoustic emission analyses.

2.2

Beamforming principle

Elastic waves emanating from a localized source such as a crack propagate outward with spherical wave fronts. At large distances from the source, these spherical waves can 7

be well approximated by plane waves. In beamforming array processing it is assumed that the wave field is constant across the wave front. Therefore, the signals received by neighboring sensors (in an array of n sensors), that has a sensor spacing which is small compared to the distance from the source to the sensors, will be delayed replicas of one another. The amount of the delay depends on the geometry of the array, the direction of the wave front and the velocity at which the wave front travels.

Figure 4: Beam forming example with the beam at top trace of an artificial acoustic emission source (extracted from [8]) The beamforming array output is a function of the delays which are a function of expected properties of the stress wave field; the beamforming array output can be adjusted algorithmically to accept waves arriving at a given direction and at a given speed. This is known as “steering” the array to a direction or “focusing” it on a given wave velocity. Figure 4 shows beamforming example recorded by several acoustic sensors for one acoustic emission source. 8

3

Implementation of WSN in SHM

In the following, we will focus attention on the implementation of a wireless sensor network in a bridge in order to monitor it and then secure its users. The choice of each element of the network, eg. sensors, cluster head, protocol, communication technology, etc., will be presented and justified. To begin this description, let us present a brief overview of some sensors known in structural health monitoring.

3.1

The Sensor mote

Figure 5: Example of sensor MOTE with USB 2.0 interface. In structural health monitoring and any other applications, a sensor node, also known as a ’mote’ (chiefly in North America), is a node in a wireless sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network. Usually each sensor node has to collect and digitize data from different sensors, to store sensor data, to analyze data with simple algorithms, send and receive selective and relevant data to and from other nodes as well as the cluster head (central unit) and to work for an adequate time period without power supply. Specifically, a sensor consists of a CPU or DSP with small memory, power unit, analog to digital conversion module and some sensors. Figure 5 give an example of sensor mote based on USB2.0 interface which is used in labs. A first hardware prototype of a wireless sensor system was available in summer 2005. 9

Unit

Value

Bandwidth

kHz

3 ÷ 100 (max)

Sensitivity

mg

x

Signal to noise ratio

µg/Hz

x

A/D Conversion

Bit

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Table 1: Specifications of sensors for acoustic Emission techniques In the following years, hardware and software were first developed to allow the measurement of humidity, temperature and steel strain as well as acceleration for dynamic analysis. Figure 6 gives another example of MEMS sensor applied for bridge monitoring as an accelerometer.

3.2

Temperature and Humidity Sensor (MEMs)

SHT15 digital humidity and temperature sensors are the high-end version of the re-flow solderable humidity sensor series with a cutting edge measurement accuracy. As every other sensor type of the SHTxx family, the capacitive humidity sensor is fully calibrated and provides a digital output. Every sensor is individually tested upon quality and accuracy compliance where the main properties are as follows ˆ 2 sensors for relative humidity and temperature, ˆ Precise dew-point calculation possible, ˆ Measurement range : 0-100% RH (Relative Humidity %), ˆ Absol. RH accuracy: +/- RH (10 . . . 90% RH), ˆ Repeatability RH: +/- 0.1%RH, ˆ Temp. Accuracy : +/- 0.4 ‰5-40 ‰,

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Figure 6: Accelerometer MEMs

Figure 7: Commercial Temperature and humidity MEMs sensor. ˆ Calibrated & digital output (2-wire interface), ˆ Fast response time 10 m

ultra-low

Table 4: Performance characteristics of different candidates for wireless communications. considered as more critical environmental measurements over the others [9]. To obtain sufficient amount of data, for the structural analysis, the acceleration measurements should be obtained with high sampling frequency, while strain, displacement temperature and wind speed are taken as single data sample as the average over a short period. For the acceleration, the measuring periodicity is very high and for the strain, the periodicity is comparatively low. The high precision of the collected data is another issue of SHM systems. For example, the commercial accelerometer “ADXL 202 E” which is used to measure large vibration (from earthquake) has the range from -2g to +2g with a resolution of 200 µ g, while the accelerometer “1221L” which is employed to measure the miniature vibrations (ambient source) has the range from -0.1g to +0.1g with the resolution of 30 µg. For this requirements the sample of a measurement should be presented using at least 16 bits (28