Design of A WSN Strain Node with Self-repairing Ability for Structural Health

Monitoring

Shenfang YUAN*, Lei QIU*, Yao TONG* Nanjing University of Aeronautics and Astronautics, 29# Yudao Street, Nanjing China

The State Key Lab of Mechanics and Control of Mechanical Structures， The Aeronautic Key Lab for Smart Materials and Structures

Abstract. Wireless Sensor Network (WSN) technology is an important technology

applied to structural health monitoring (SHM) area to reduce system weight and

massive interconnections. Under many application scenarios of SHM, the wireless

sensor nodes have to work in nature harsh environment and to serve for a long term

which may cause failures of the node circuits. In order to ensure the reliable and low

cost operation of wireless sensor network in SHM applications, in this paper, a

reconfigurable hardware based self-repairing WSN strain node realization method is

presented based on FPAAs chip. Besides of measuring strain with a good precision,

the developed node can dynamically change its node configuration to repair the circuit

failures which cannot be done by ordinary fix-circuit WSN sensor nodes. The size of

the developed node is similar to ordinary WSN nodes and its energy consumption is

acceptable. Experiments show that the node can self-diagnose the failures and recover

to normal working state automatically. The research presented can improve the

stability of the WSN and reduce the maintenance cost of WSN applied in SHM area.

Introduction

Structural health monitoring (SHM) is an active area of research and practice in recent years. When implementing a cable-based SHM system for large engineering structures, massive interconnections from sensors to the centralized data server require complex configurations of hardware systems which cause heavy weight of the system and the decreasing of system reliability. To solve above problems, a plenty of literatures have been published regarding applying Wireless Sensor Network (WSN) technology to SHM [1-9].

In recent years, many wireless sensors have been developed for temperature, strain, impedance measurement or active monitoring, with powerful computational cores included, or to obtain the lowest power consumption [9-13]. Among the parameters monitored for SHM purpose, strain is one of the most important physical parameters that reveal a structure’s loading, boundary, fatigue and material conditions. Strain measurement, therefore, is an indispensable capability for structural health monitoring. Besides the foil strain gauge based wireless sensor node reported [13], special strain sensors, such as passive IDT strain sensor,

4th International Symposium on

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carbon nanotube-gold nanocomposite based strain sensor and etc., also have been developed by different researchers [11-16]. However All these researches concentrate on the realization of the strain measuring and wireless communication. None of them specially pan attention to the robustness of the sensor node itself.

In engineering applications of SHM technology, usually a number of sensor nodes are deployed across a large area needed. Some of these nodes have to work in harsh natural environment. The performance of the network suffers as the number of nodes grows, and a large sensor network quickly becomes difficult to manage. Thus, robustness of the WSN itself when applied is critical for successful applications.

Regarding the robustness of the WSN, some researchers have discussed the possibility to apply bio-inspired technologies to reorganizing the sensor networks including the artificial immune system, swarm intelligence, and the intercellular information exchange [17-19]. [17-18] proposes a framework for an evolvable sensor network architecture, investigated as part of the ESPACENET project, collocated at the University of Edinburgh, Essex, Kent and Surrey, UK. A plenty of literatures also report their researches on the re-configuration of the wireless sensor network when node is dead [20-21]. [20] presents a policy controlled self-configuration method in unattended wireless sensor networks. [21] reports a Lifetime extension method based on an intelligent redeployment method for surveillance wireless sensor networks. In the above research, the dead node is usually abandoned totally. In some applications of WSN, such as battlefield surveillance, planetary applications, dead nodes have to be abandoned because the main dead reason is energy exhausted problem and the batteries can not be changed or charged.

Under many application scenarios of SHM, the wireless sensor nodes can be replaced at a certain intervals or changed by energy harvesting devices [22-23]. Under these situations, battery is no longer a very critical problem. But these nodes have to work in harsh environment or to serve for a long term which may cause failures of the node circuits. Though the sensor network can still be reorganized when some nodes are dead, this causes additional maintenance cost and sometimes still decreases the function of the network. Under those situations that the sensor network can not be reorganized, the performance of the whole WSN will surely be degraded.

Till now, the WSN sensor node hardware is designed fixed. When one component of the sensor node fails, usually the whole sensor node has to be abandoned. If the node itself can be self-repaired, the robustness of the WSN applied will be greatly improved. No report has been found regarding the design on hardware based wireless sensor network node with self-repairing ability.

In this paper, a reconfigurable hardware based WSN strain sensor node with self-repairing ability is proposed for SHM purpose. FPAAs is adopted when the sensor module of the WSN node is designed. Special signal processing module is designed to distinguish abnormal circuit and to drive the FPAAs to self-repair the nodes. Using the method presented, when part of circuits in the sensor node fail, it is not necessary to abandon the whole node. The node can diagnose its own abnormal and repair it. This can greatly improve the robustness of the WSN and reduce the maintenance cost of WSN when it is applied in SHM.

The structure of this paper is as following: Session 1 gives the brief introduction of the research presented. Session 2 proposes the reconfigurable hardware based self-repairing

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WSN strain sensor node realization paradigm. In session 3, the design and implementation of the FPAAs based self-repairing strain sensor node is given in detail. Session 4 introduces the experimental demonstration of the failure diagnosis process and the self-repairing results. The energy consumption is also evaluated of the developed node and its life time is discussed. The conclusion is given at the end of this paper.

1. Reconfigurable Hardware based self-repairing WSN node realization paradigms

Figure 1 shows a typical WSN strain sensor node structure [13]. There are typically three modules in the node, including sensing module, signal processing module and wireless communication module. The sensing module provides high stability bridge voltage to the strain bridge circuit for the strain gauge and amplifies and filters the output of the bridge. The processing module includes an A/D converter, a micro-processor and its outside circuits. This part controls the work of the whole node and also processing the data from the strain gauges. The wireless communication module takes charge of the communication with other nodes or the base station.

Figure 1 A typical WSN strain sensor node structure

Figure 2 presents the paradigm to realize the WSN sensor node with self-repairing ability. Different from Figure 1, using this design, the main analog circuits of the WSN node are realized by FPAAs. A FPAAs is an integrated device containing configurable analog blocks (CAB) and interconnects between these blocks. These blocks can be selected, placed and wired together by software to construct complex analog circuits quickly and easily [24-25]. FPAAs may be current mode or voltage mode devices. For voltage mode devices, each block usually contains an operational amplifier in combination with programmable configuration of passive components. Since FPAAs provide a method for rapidly prototyping analog systems, it can be used to realize the WSN node’s analog circuits. Since one FPAAs has a lot of internal modules, during service, when certain part fails, the FPAAs uses its own other internal module to replace the failed circuit which is a kind of self-repairing function.

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Figure 2 The FPAAs based self-repairing WSN strain node realization paradigm

2. Implementation of a self-repairing WSN strain sensor node

According to the paradigm presented in Session 2, the hardware and software of the self-repairing node are designed and implemented.

2.1 The self-repairing WSN strain node hardware development

Figure 3 is the detail design of the FPAAs based self-repairing sensor node. The sensor board of the developed node supplies a stable bridge voltage and bridge circuit to the strain gauge. A high precision fixed-output voltage regulator LP2985 is used to provide the bridge voltage of 3.3V. To condition the output signal of the bridge, usually a conditioning circuit including an amplifier and a low-pass filter should be designed to condition the small signal output from the bridge. Different from ordinary WSN strain sensor node shown in Figure 1, the analog circuits are realized by modules in the FPAAs. There is no additional amplifier and filter circuit needed. The output from the FPAAs is connected to a microprocessor to do A/D convert.

A mixed-signal microcontroller from Texas Instruments, the MSP430 microcontroller, is chosen. Built around a 16-bit CPU, the MSP430 is designed for low cost, and specifically, low power consumption. The TI CC2420 RF transceiver is chosen for the wireless communication design. CC2420 is a true single-chip 2.4 GHz IEEE 802.15.4 compliant RF transceiver designed for low-power and low-voltage wireless applications which has been reported in many WSN node design.

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Figure 3 Circuit design of the FPAAs based self-repairing node

In the implementation, the FPAA chip AN231E04 from Anadigm company is chosen because its small chip size, low energy consumption and enough input and output I/O. The AN231E04 operates with a 3.3 volt power supply with typical power in the 125 mw range, is of particular interest to this design. The AN231E04 is packaged in a 44-pin QFN (quad flat pack, no-lead) package- 7×7×0.9 mm, ultra thin. A key feature of the AN231E04 is that it can be reconfigured during operation – dynamically – by a microprocessor. The AN231E04 consists of a 2×2 matrix of fully configurable analog blocks, surrounded by programmable interconnect resources and analog input/output cells with active elements. Configuration data is stored in an on-chip SRAM configuration memory. Additionally, an SPI-like interface is provided for simple serial load of configuration data from a microprocessor. Figure 4 shows the internal structure of the AN231E04. AnadigmDesigner2 software is adopted to reconfigure the AN231E04 chip. Figure 5 shows the analog input channel configured in the AN231E04 including an amplifier and a filter.

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Figure 4 Internal structure of the AN231E04

Figure 5 Circuit configuration of the FPAAs to form the analog circuits of the node

I/O PINS CLOCK

PINS

POWER

PINS

MCU

COMMUNICATE

PINS

I/O PINS

6

In the design in this paper, two modules, the amplifier and the low pass filter, are chosen to demonstrate the self-repairing concept. Two typical failure modes are described by Table 1. Two switches are adopted here to intentionally make the failures when designing the node hardware. Shown in Figure 3, Switch 1 is used to cut the connection of AMP_1(amplifier 1 realized in the FPAAs) to the microprocessor and connects the 3.3 v provided by a AMS1117 chip which is a fixed voltage regulator to the ADC2 of the microprocessor to simulate the amplifier saturation failure. Switch 2 is used to cut the connection from Filter_1(filter 1 realized in the FPAAs) to ADC0 of the microprocessor to produce the filter connection failure.

Table 1 Failure modes

Failure index Failure modes Circuit status

1 Amplifier failure Amplifier saturation

2 Low-pass filter failure Filter connection failure

Figure 6(a) shows the sensing and processing board of the self-repairing node developed. Figure 6(b) is the picture of the developed node. The size of the developed node is 7×4.5×2cm which is just an ordinary WSN node’s size.

(a) Sensing and processing board

(b) The developed node

Figure 6 FPAAs based self-repairing WSN strain node developed

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2.2 Failure diagnosis and reconfigure hardware based self-repairing process

In this design, the strain measuring range is ±3000με. This range is designed to correspond to 0.8v-2.5v output of the sensor board by adding an input bias voltage to the amplifier. When the amplifier saturates, it outputs a saturation voltage of 3.3v. When the filter circuit has connection failure, its output voltage is 0v. Both above outputs are abnormal outputs and can be distinguished by the microprocessor. Since the outputs of the amplifier and filter circuits are connected with different A/D inputs of MSP430, The software distinguish different module failures by reading different A/D converter outputs. Figure 7 shows the flow chart of the failure diagnosis and reconfigure hardware based self-repairing process. LCCb is the pin of the AN231E04. It indicates the successful finish of the dynamic configuration process when its value is 0. Figure 8 shows the different circuit configuration of the FPAAs during repairing process. Figure 8(a) is Circuit 2 configuration which is used when the amplifier of original circuit has failure. Figure 8(b) is Circuit 3 configuration which is used when the filter circuit has failure. Table 2 shows the different circuit connections configured during the whole process.

Figure 7 Failure diagnosis and self-repairing process

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Table 2 Circuit configurations of FPAAs based self-repairing WSN node

Circuit 1 Circuit 2 Circuit 3

I1 P/N to AMP_1

AMP_1 to O2P

I3 P/N to Filter_1

Filter_1 to O7P

I1 P/N to AMP_2

AMP_2 to O2P

I3 P/N to Filter_1

Filter_1 to O7P

I1 P/N to AMP_1

AMP_1 to O2P

I3 P/N to Filter_2

Filter_2 to O7P

(a) Circuit 2: Using CAB3 to realize a new amplifer

(b) Circuit 3: Using CAB4 to realize a new low-pass filter

Figure 8 Different circuit configurations of the FPAAs during repairing

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3. Self-repairing ability demonstration experiment

Demonstration experiments are performed to evaluate the strain measurement, the self-repairing ability of the developed node. The repairing time and node energy consumption are also discussed.

3.1 Experimental system

A uniform strength beam is adopted. One end of it is fixed. Load caused by weight is applied on the other end. A strain gauge is arranged on this beam and it is connected to the bridge circuit of the self-repairing nodes. A demonstration software is developed based on Labview to control the sensing process, transfer data from the node to the computer based base station and also show the data received on the computer screen in real time. The experimental system is shown in Figure 9. A P-3500 Portable Strain Indicator from INTERTechnology INC. is used to calibrate the WSN sensor node.

Figure 9 Self-repairing node function evaluation system

3.2 Self-repairing ability evaluation

To demonstrate the whole working process of the developed WSN nodes including normal working state and the self-processing state, an experiment process is designed as: a. normal

(b) Base station and demonstration software interface

(a) Strain monitoring setup

FPAA based Self-repairing node

Cantilever Beam

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working process; b. amplifier saturation and self-repairing process; c. filter connection failure and self-repairing process. From data sent from the nodes to the base station, the working process of the sensor nodes can be easily demonstrated. Figure 10 shows the output from the FPAAs based self-repairing node. At the first stage, the node works normally. When different weights are applied on the uniform strength beam, the node monitors different strain consequently. 0, 1kg and 2.5kg weights are applied separately, corresponding to 0, 342με and 855με strain caused on the beam and 1.65v, 1.75v and 1.89v outputs from the sensor nodes. At the second stage, the amplifier saturation failure is made intentionally by moving Switch 1. In this case, the software in the node diagnoses this failure and start self-repairing process. From the output of the node, it can be noticed that the node first outputs a saturation voltage of 3.3v. After the software diagnoses this failure and self-repairs the node, the output of the node decreases to normal state. At the third stage, Switch 2 is used to cut the connection of the filter to the A/D chip. Under this situation, the outputs of the nodes first decreases to 0v. Then after the software diagnoses this failure and self-repairs the node, the output of the node increases to the normal state.

The experiments show that the nodes developed can self-diagnose the failures and recover to normal working state automatically.

Figure 10 Output of the FPAAs based self-repairing node during experiment

3.3 Stain measurement function evaluation before and after repairing

The performance of strain measurement of the developed node is also compared in the normal working state and after the repairing process. Calibration tests are performed before and after the repairing process. The calibrating data obtained are shown in Table 3 and Table

20 40 60 80 100 1200

0.5

1

1.5

2

2.5

3

Time(s)

Vol

tage

(V)

20 30 40 50

1.6

1.7

1.8

1.9

2

Time(s)

Vol

tage

(V)

90 100 110 120

1.6

1.7

1.8

1.9

2

Time(s)

Vol

tage

(V)

Normal working

Saturation andself-repairing

Filter failure andself-repairing

Normal working

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4 respectively where the strain values are given by the P-3500 Portable Strain Indicator to calibrate the output voltages measured by the node.

The performance parameters compared including: repeatability R

, hysteresis H

and

static precision whose values are calculated by using Equation(1), (2) and (3) respectively.

max 100%RFS

R

y

（1）

max1100%

2HFS

H

y

（2）

2

1

3 1( ) 100%

1

n

iiFS

yy n

（3） Where maxR is the biggest repeatability error, FSy is the maximum measurement range,

maxH is biggest hysteresis error, iy is the residual error at each testing point, n is the

number of test points.

The R

, H

, and calculated from Table 3 are 0.17%, 0.053%, 0.24% respectively. The

R ,

H , and calculated from Table 4 are 0.15%, 0.059%, 0.28% respectively. The

results show that the performance of the sensor node to measure strain is good and can be recovered to original state by self-repairing process.

Table 3 Strain test calibration data during formal working state

Applied loads( g) 0 50 100 200 250 300 400 500

Strain caused(με) 0 17 35 69 86 103 137 171

Positive range 1(mv) 0 20 36 70 90 105 144 180

NegativeRange1(mv) 3 18 33 70 92 107 145 174

Positive range 2(mv) 3 18 36 72 89 106 144 173

NegativeRange 2(mv) 2 18 33 72 88 105 141 177

Positive range 3 (mv) 1 20 34 73 90 108 145 179

NegativeRange 3(mv) 3 20 37 73 91 108 146 181

Table 4 Strain test calibration data after self-repairing

Applied loads( g) 0 50 100 200 250 300 400 500

Strain caused(με) 0 17 35 69 86 103 137 171

Positive range 1(mv) 1 20 34 71 92 106 148 178

NegativeRange1(mv) 3 19 36 71 90 107 144 179

Positive range 2(mv) 2 18 37 72 87 104 142 175

NegativeRange 2(mv) 0 18 36 70 88 107 146 177

Positive range 3 (mv) 1 20 34 73 90 108 145 179

NegativeRange 3(mv) 0 20 38 74 91 109 148 180

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3.4 Node power consumption research

WSN sensor nodes usually work by a battery to supply power. The node developed should have low power consumption. Node power consumption of the developed WSN strain node with self-repairing ability is tested and compared to ordinary WSN strain node developed as reported in [13]. A fix voltage of 3.3v is used to supply both nodes. A high-performance digital multimeter 34401A from HP company is used to test the current the node consumes at different modes. The data are shown in Table 5. Under the working mode, the node samples the strain with a sampling rate of 10 HZ. The results show that the power of the developed self-repairing node is bigger than ordinary node. This is because the FPGAs chip adopted. According to the data sheet of AN231E04, it consumes about 42mA current at normal working state. However, if the node is supplied power by two 1700mAh AA batteries and the node samples strain for 10s at an interval of 1 hour per day, it still can work for about 9 months. For SHM purpose, the node can be used.

Table 5 Energy consumption testing results

Voltage applied

(V）

Current tested at

working mode

（mA）

Current tested at

idle mode（mA）

Current tested at

sleep mode（μA）

Node with

self-repairing

ability

3.3 62.7 58.9 <220

Ordinary WSN

strain node 3.3 22.1 18.4 <200

4. Conclusion

To improve the stability of the WSN and reduce the maintenance cost of WSN when they are used in SHM area, a new WSN strain node with self-repairing ability implemented by reconfigurable hardware is proposed in this paper. Demonstration experiments are performed and show that the node developed can self-diagnose the failures and recover to normal working state automatically. The size of the developed node is similar to ordinary WSN node and its energy consumption is acceptable.

Further researches can be done to improve the proposed methods. When FPAAs and FPGAs are adopted together, digital circuits of the node can be self-repaired using the same theory and the microprocessor may not need anymore.

Acknowledgements

This work is supported by the Natural Science Foundation of China (Grant No. 50830201), Seventh Framework Programme(Grant No. FP7-PEOPLE-2010-IRSES－269202), Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT0968), the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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