Self-Diagnosis and Self-Reconfiguration of Piezoelectric Actuator and Sensor Network for Large Structural Health Monitoring

Liang, Dong; Wu, Lili; Fan, Zhaolin; Xu, Yang

doi:10.1155/2015/207303

Cited by 13 publications

(10 citation statements)

References 11 publications

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“…This video camera data is synchronized with the sensor data [9,10]. The self-diagnosis system helps to find out the failed piezoelectric sensors and reconfigure the network with existing sensors in large SHM systems [11,12]. Piezoelectric wafer active sensors (PWAS) are used in a variety of damage detection methods [13,14].…”

Section: Previous Workmentioning

confidence: 99%

“…In the pitch-catch technique, two PZT sensors are used: one as transmitter that located before the damage and the other as a receiver that located after the damage. The first PZT sends a signal, and the received signal by the second PZT is then used to determine the location and/or the extent of the damage [11]. On the other hand, the pulse-echo technique relies on the reflected wave from the damage, and one PTZ transducer is used for both transmitting and receiving the signal.…”

Section: Shm Damage Detection Techniquesmentioning

confidence: 99%

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Internet of Things (IoT) Platform for Structure Health Monitoring

Abdelgawad

Yelamarthi

2017

Wireless Communications and Mobile Computing

108

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Increase in the demand for reliable structural health information led to the development of Structural Health Monitoring (SHM). Prediction of upcoming accidents and estimation of useful life span of a structure are facilitated through SHM. While data sensing is the core of any SHM, tracking the data anytime anywhere is a prevailing challenge. With the advancement in information technology, the concept of Internet of Things (IoT) has made it possible to integrate SHM with Internet to track data anytime anywhere. In this paper, a SHM platform embedded with IoT is proposed to detect the size and location of damage in structures. The proposed platform consists of a Wi-Fi module, a Raspberry Pi, an Analog to Digital Converter (ADC), a Digital to Analog Converter (DAC), a buffer, and piezoelectric (PZT) sensors. The piezoelectric sensors are mounted as a pair in the structure. Data collected from the piezoelectric sensors will be used to detect the size and location of damage using a proposed mathematical model. Implemented on a Raspberry Pi, the proposed mathematical model will estimate the size and location of structural damage, if any, and upload the data to Internet. This data will be stored and can be checked remotely from any mobile device. The system has been validated using a real test bed in the lab.

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Section: Previous Workmentioning

confidence: 99%

Section: Shm Damage Detection Techniquesmentioning

confidence: 99%

Internet of Things (IoT) Platform for Structure Health Monitoring

Abdelgawad

Yelamarthi

2017

Wireless Communications and Mobile Computing

108

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“…The prototype sensor node employs the piezoelectric sensor as a sensor with the electromotive force in this case. The piezoelectric sensor has been used in several sensor network applications [5][6][7]. These applications are the monitor of moving humans and objects and doors opening and closing by airflow [5], the health monitoring of an aircraft wing [6], and the large structural health monitoring [7].…”

Section: Organizationmentioning

confidence: 99%

A sensor node architecture with zero standby power on wireless sensor network

Yamawaki

Yamanaka

Serikawa

2015

Artif Life Robotics

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The WSN has various problems to be resolved in order to spread its usage widely. One of the problems is the power consumption of the sensor node driven by the battery. Changing the batteries on many nodes incurs significant cost. To tackle such problem, the sensor nodes using photovoltaics have been proposed [2]. However, the photovoltaics cannot generate enough power in nighttime. It also does not work well when enough sunlight cannot reach like the indoor, cloudy day, tunnel, and so on. There are also several researches using other energy harvesting such as vibrations, thermoelectric, wind flow, acoustic noise, magnetic field energy, and so on. However, the amount of energy gained by such harvesting is not enough to make the sensor node work normally and reliably [2]. By the way, there are researches to make the battery life longer by making the sensor node sleep with low power consumption in a cyclic manner [3]. However, the sensor node in the sleep mode constantly consumes the battery power even if it is low. In addition, the cyclic wake-up performs waste sensing when any event does not occur. It leads to waste of the battery power.Thus, we propose a novel architecture of the sensor node with zero power consumption in the sleep mode. Since the sleeping sensor node also wakes only when the event that the sensor should sense occurs, the waste wake-up consuming the battery power can be eliminated completely. The proposed sensor node utilizes a transistor switch cutting the power line, which is driven by the electromotive force from a sensor [4]. The novelties of our paper are to propose the new architecture of the sensor node using this mechanism on a sensor network, to confirm whether a wireless sensor node with this mechanism can be practically realized, and to show the quantitative effect to reduce the power consumption.The rest of the paper is organized as follows. Section 2 shows an overview of the architecture of the proposed Abstract The wireless sensor network (WSN) is a promising technology to improve the social life cooperating with the robotic technologies like nursing robots, disaster rescue robots, industrial robots, and so on. For such WSN, we propose an architecture of the sensor node with zero standby power consumption. This is accomplished by combining a power transistor cutting the ground line, an electromotive from a sensor for just turning on the power transistor and a conventional battery. The preliminary experiments demonstrate that our proposal can actually activate the sensor node, which stays at the sleep mode with zero power consumption, with a short-time electromotive force generated by the piezoelectric sensor. It is also confirmed that the sensor node activated can establish the wireless communication correctly.

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“…The first method is mainly based on the ultrasonic guided wave (such as Lamb wave) as the effects of the defects are found to be significant, modifying the magnitude and the shape of the propagated waves. Liang et al 9 proposed a method of self-diagnosis and self-reconstruction of piezoelectric actuator and sensor network based on Lamb wave to realize real-time and reliable assessment of large-scale structure damage. Park et al 10,11 studied the influence of bonding defects between PZT and structure on high-frequency SHM technology by using the Lamb wave propagation and impedance method, then verified the feasibility of the proposed sensor diagnosis program through experiments.…”

Section: Introductionmentioning

confidence: 99%

Electromechanical Impedance Based Self-Diagnosis of Piezoelectric Smart Structure Using Principal Component Analysis and LibSVM

Xie

Zhang

Tang

et al. 2021

Preprint

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The long-term use of a piezoelectric smart structure make it difficult to judge whether the structure or piezoelectric lead zirconate titanate (PZT) is damaged when the signal changes. If the sensor fault occurs, the cases and degrees of the fault are unknown based on the electromechanical impedance method. Therefore, after the principal component analysis (PCA) of six characteristic indexes, a two-component solution which could explain 99.2% of the variance in the original indexes was obtained to judge whether the damage comes from the PZT. Then LibSVM was used to make an effective identification of four sensor faults (pseudo soldering, debonding, wear, and breakage) and their three damage degrees. The result shows that the identification accuracy of damaged PZT reached 97.5%. The absolute scores of PCA comprehensive evaluation for structural damages are less than 0.5 while for sensor faults are greater than 0.6. By comparing the scores of the samples under unknown conditions with the set threshold, whether the sensor faults occur is effectively judged; the intact and 12 possible damage states of PZT can be all classified correctly with the model trained by LibSVM. It is feasible to use LibSVM to classify the cases and degrees of sensor faults.

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Self-Diagnosis and Self-Reconfiguration of Piezoelectric Actuator and Sensor Network for Large Structural Health Monitoring

Cited by 13 publications

References 11 publications

Internet of Things (IoT) Platform for Structure Health Monitoring

Internet of Things (IoT) Platform for Structure Health Monitoring

A sensor node architecture with zero standby power on wireless sensor network

Electromechanical Impedance Based Self-Diagnosis of Piezoelectric Smart Structure Using Principal Component Analysis and LibSVM

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