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Piezoelectric Energy Harvesting Sensors and Their Applications
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Piezoelectric Energy Harvesting Sensors and Their Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 28124

Special Issue Editors

Prof. Dr. Linbing Wang

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Guest Editor
The Via Department of Civil and Environmental Engineering, 301N Patton Hall, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
Interests: smart and sustainable technologies; innovative infrastructure assessment and performance predictions; high-performance materials, material design; multiple-scale characterization, modeling, and simulation; pavement testing and mechanistic pavement design
Special Issues, Collections and Topics in MDPI journals
Dr. Ya Wei

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Guest Editor
Department of Civil Engineering, Tsinghua University, Beijing, China, 100084
Special Issues, Collections and Topics in MDPI journals
Dr. Hailu Yang

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Guest Editor
University of Science and Technology Beijing, Beijing, China
Dr. Zhoujing Ye

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Guest Editor
University of Science and Technology Beijing, Beijing, China

Special Issue Information

Dear Colleagues,

Piezoelectric sensors and actuators have been widely used due to their significant advantages. Recent trends are moving towards energy harvesting, self-powered sensors, miniaturization, 3D printing for fabrication, IoT sensor networks, and artificial-intelligence-based data analytics. In civil engineering applications, severe service environments and rough installation processes require packaging ruggedness, waterproofness, and superior stability of the sensors. Such challenging requirements have resulted in tough demands for improved design and fabrications. In addition, the monitoring of large civil infrastructure often requires sensor networks that consume significant quantities of energy. Self-powering and the use of locally harvested energy represent desirable features, especially in remote areas. In addition, long-term monitoring requires wireless data transmission and analytics of large-volume data. In this context, data analytics becomes a bottleneck. AI-based approaches such as machine learning and deep learning are promising to address this. This Special Issue will reflect recent developments in these trends.

Specifically, this Special Issue will cover the following areas: 1) innovative sensor design and fabrication; 2) optimal design and deployment of wireless sensor networks; 3) AI-based data analytics; and 4) integrated applications in safety and security assessments of civil infrastructures.

Dr. Linbing Wang
Dr. Ya Wei
Dr. Hailu Yang
Dr. Zhoujing Ye
Guest Editors

Manuscript Submission Information

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Keywords

  • Energy harvesting
  • Self-powered sensors
  • IoT sensor networks
  • Vibration and acoustic sensing
  • Miniaturization and 3D printing
  • Structural health monitoring
  • Data analytics
  • Safety and security of infrastructure

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Published Papers (7 papers)

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Research

17 pages, 19298 KiB  
Article
Research and Development of a Wireless Self-Powered Sensing Device Based on Bridge Vibration Energy Collection
by Xinlong Tong, Yun Hou, Yuanshuai Dong, Yanhong Zhang, Hailu Yang and Zhenyu Qian
Sensors 2021, 21(24), 8319; https://doi.org/10.3390/s21248319 - 13 Dec 2021
Cited by 1 | Viewed by 3006
Abstract
Traditional bridge monitoring has found it difficult to meet the current diversified needs, and frequent replacement of sensor batteries is neither economical nor environmentally friendly. This paper presents a wireless acceleration sensor with low power consumption and high sensitivity through integrated circuit design, [...] Read more.
Traditional bridge monitoring has found it difficult to meet the current diversified needs, and frequent replacement of sensor batteries is neither economical nor environmentally friendly. This paper presents a wireless acceleration sensor with low power consumption and high sensitivity through integrated circuit design, data acquisition and wireless communication design, package design, etc. The accuracy of the sensor in data collection was verified through calibration and performance comparison tests. The ability of triangular piezoelectric cantilever beam (PCB) was tested through design and physical manufacture. Finally, the self-powered performance of the sensor was tested by connecting the sensor and the triangular PCB through a circuit, which verifies the feasibility of using the PCB to collect bridge vibration energy and convert it into electrical energy to supply power for sensor, and also explore the green energy collection and application. Full article
(This article belongs to the Special Issue Piezoelectric Energy Harvesting Sensors and Their Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>The composition of wireless acceleration sensor.</p> Full article ">Figure 2
<p>The circuit diagram of ADXL354.</p> Full article ">Figure 3
<p>The circuit diagram of AD7689.</p> Full article ">Figure 4
<p>The circuit diagram of STM32L151C8X6.</p> Full article ">Figure 5
<p>The circuit diagram of LDO-SOT23-5.</p> Full article ">Figure 6
<p>The circuit diagram of battery voltage acquisition circuit.</p> Full article ">Figure 7
<p>The circuit diagram of serial port and power interface.</p> Full article ">Figure 8
<p>Circuit board of acceleration sensor.</p> Full article ">Figure 9
<p>Schematic diagram of the packaging box of wireless acceleration sensor.</p> Full article ">Figure 10
<p>Internal view of sensor’s case before glue filling.</p> Full article ">Figure 11
<p>Internal view of sensor’s case after glue filling.</p> Full article ">Figure 12
<p>The welded of power pin with switch pin.</p> Full article ">Figure 13
<p>Aviation plug after glue filling.</p> Full article ">Figure 14
<p>The wireless acceleration sensor.</p> Full article ">Figure 15
<p>Schematic diagram of the sensor being accelerated by gravity.</p> Full article ">Figure 16
<p>Sensor No. 1.</p> Full article ">Figure 17
<p>Sensor No. 2.</p> Full article ">Figure 18
<p>Simulation data of rectangular, trapezoidal, and triangular piezoelectrics [<a href="#B24-sensors-21-08319" class="html-bibr">24</a>].</p> Full article ">Figure 19
<p>The photo of triangular PCB.</p> Full article ">Figure 20
<p>The test of triangular PCB.</p> Full article ">Figure 21
<p>The relation between voltage and frequency of triangular PCB.</p> Full article ">Figure 22
<p>The physical test results of triangular and rectangular PCBs.</p> Full article ">Figure 23
<p>Circuit connection on breadboard.</p> Full article ">Figure 24
<p>Power generation capacity test of triangular PCB.</p> Full article ">Figure 25
<p>The relationship between power generated by triangular PCB with time.</p> Full article ">Figure 26
<p>Pin circuit welding of aviation plug.</p> Full article ">Figure 27
<p>The self-powered performance test of sensor.</p> Full article ">Figure 28
<p>Acceleration sensor data.</p> Full article ">
14 pages, 3632 KiB  
Article
Development and Piezoelectric Properties of a Stack Units-Based Piezoelectric Device for Roadway Application
by Chenchen Li, Fan Yang, Pengfei Liu, Chaoliang Fu, Quan Liu, Hongduo Zhao and Peng Lin
Sensors 2021, 21(22), 7708; https://doi.org/10.3390/s21227708 - 19 Nov 2021
Cited by 10 | Viewed by 3234
Abstract
To improve the energy harvesting efficiency of the piezoelectric device, a stack units-based structure was developed and verified. Factors such as stress distribution, load resistance, loads, and loading times influencing the piezoelectric properties were investigated using theoretical analysis and experimental tests. The results [...] Read more.
To improve the energy harvesting efficiency of the piezoelectric device, a stack units-based structure was developed and verified. Factors such as stress distribution, load resistance, loads, and loading times influencing the piezoelectric properties were investigated using theoretical analysis and experimental tests. The results show that the unit number has a negative relationship with the generated energy and the stress distribution has no influence on the power generation of the piezoelectric unit array. However, with a small stress difference, units in a parallel connection can obtain high energy conversion efficiency. Additionally, loaded with the matched impedance of 275.0 kΩ at 10.0 kN and 10.0 Hz, the proposed device reached a maximum output power of 84.3 mW, which is enough to supply the low-power sensors. Moreover, the indoor load test illustrates that the electrical performance of the piezoelectric device was positively correlated with the simulated loads when loaded with matched resistance. Furthermore, the electrical property remained stable after the fatigue test of 100,000 cyclic loads. Subsequently, the field study confirmed that the developed piezoelectric device had novel piezoelectric properties with an open-circuit voltage of 190 V under an actual tire load, and the traffic parameters can be extracted from the voltage waveform. Full article
(This article belongs to the Special Issue Piezoelectric Energy Harvesting Sensors and Their Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>The structure of the stacked piezoelectric unit.</p> Full article ">Figure 2
<p>The main components of the stack units-based piezoelectric device.</p> Full article ">Figure 3
<p>The mechanical testing and simulation system: (<b>a</b>) the loading test system; (<b>b</b>) the electrical properties monitoring system.</p> Full article ">Figure 3 Cont.
<p>The mechanical testing and simulation system: (<b>a</b>) the loading test system; (<b>b</b>) the electrical properties monitoring system.</p> Full article ">Figure 4
<p>Electrical properties of the piezoelectric device with load impedance: (<b>a</b>) the output voltage, (<b>b</b>) the output power.</p> Full article ">Figure 5
<p>Electrical properties of the piezoelectric device with different load conditions: (<b>a</b>) effect of load magnitude at 10 Hz, (<b>b</b>) effect of load frequency at 10 kN.</p> Full article ">Figure 6
<p>Electrical properties of the piezoelectric device with different loading times.</p> Full article ">Figure 7
<p>On-site tests of the piezoelectric device: (<b>a</b>) cutting the pavement surface, (<b>b</b>) installing the PEH, and (<b>c</b>) applying vehicle load.</p> Full article ">Figure 8
<p>The open-circuit voltage under actual vehicle load.</p> Full article ">
14 pages, 3546 KiB  
Communication
Influence of Temperature on the Natural Vibration Characteristics of Simply Supported Reinforced Concrete Beam
by Yanxia Cai, Kai Zhang, Zhoujing Ye, Chang Liu, Kaiji Lu and Linbing Wang
Sensors 2021, 21(12), 4242; https://doi.org/10.3390/s21124242 - 21 Jun 2021
Cited by 24 | Viewed by 4340
Abstract
Natural vibration characteristics serve as one of the crucial references for bridge monitoring. However, temperature-induced changes in the natural vibration characteristics of bridge structures may exceed the impact of structural damage, thus causing some interference in damage identification. This study analyzed the influence [...] Read more.
Natural vibration characteristics serve as one of the crucial references for bridge monitoring. However, temperature-induced changes in the natural vibration characteristics of bridge structures may exceed the impact of structural damage, thus causing some interference in damage identification. This study analyzed the influence of temperature on the natural vibration characteristics of simply supported beams, which is the most widely used bridge structure. The theoretical formula for the variation of the natural frequency of simply supported beams with temperature was proposed. The elastic modulus of simply supported beams in the range of ?40 °C to 60 °C was acquired by means of the falling ball test and the theoretical formula and was compared with the elastic modulus obtained by the three-point bending test at room temperature (20 °C). In addition, the Midas/Civil finite-element simulation was carried out for the natural frequency of simply supported beams at different temperatures. The results showed that temperature was the main factor causing the variation of the natural frequency of simply supported beams. The linear negative correlation between the natural frequency of simply supported beams and their temperature were observed. The natural frequency of simply supported beams decreased by 0.148% for every 1 °C increase. This research contributed to the further understanding of the natural vibration characteristics of simply supported beams under the influence of temperature so as to provide references for natural frequency monitoring and damage identification of beam bridges. Full article
(This article belongs to the Special Issue Piezoelectric Energy Harvesting Sensors and Their Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Diagram of the simply supported beam.</p> Full article ">Figure 2
<p>The relationship between <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi mathvariant="normal">f</mi> <mi mathvariant="normal">n</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">T</mi> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Test scheme.</p> Full article ">Figure 4
<p>Relationship between the first-grade natural frequency of the No. 1 beam and the No. 2 beam with temperature change.</p> Full article ">Figure 5
<p>Relationship between theoretical results of the testing beam and experimental results of the first-grade natural frequency with temperature change.</p> Full article ">Figure 6
<p>Three-point bending test.</p> Full article ">Figure 7
<p>The testing beam model.</p> Full article ">Figure 8
<p>Relationship between the n-grades natural frequency (n = 1, 2, 3, 4) and temperature of the testing beam’s theoretical and simulation results.</p> Full article ">Figure 8 Cont.
<p>Relationship between the n-grades natural frequency (n = 1, 2, 3, 4) and temperature of the testing beam’s theoretical and simulation results.</p> Full article ">
19 pages, 7381 KiB  
Article
Development of Piezoelectric Energy Harvester System through Optimizing Multiple Structural Parameters
by Hailu Yang, Ya Wei, Weidong Zhang, Yibo Ai, Zhoujing Ye and Linbing Wang
Sensors 2021, 21(8), 2876; https://doi.org/10.3390/s21082876 - 20 Apr 2021
Cited by 22 | Viewed by 5994
Abstract
Road power generation technology is of significance for constructing smart roads. With a high electromechanical conversion rate and high bearing capacity, the stack piezoelectric transducer is one of the most used structures in road energy harvesting to convert mechanical energy into electrical energy. [...] Read more.
Road power generation technology is of significance for constructing smart roads. With a high electromechanical conversion rate and high bearing capacity, the stack piezoelectric transducer is one of the most used structures in road energy harvesting to convert mechanical energy into electrical energy. To further improve the energy generation efficiency of this type of piezoelectric energy harvester (PEH), this study theoretically and experimentally investigated the influences of connection mode, number of stack layers, ratio of height to cross-sectional area and number of units on the power generation performance. Two types of PEHs were designed and verified using a laboratory accelerated pavement testing system. The findings of this study can guide the structural optimization of PEHs to meet different purposes of sensing or energy harvesting. Full article
(This article belongs to the Special Issue Piezoelectric Energy Harvesting Sensors and Their Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>PEH road. (<b>a</b>) Schematic of PEH road; (<b>b</b>) schematic of internal force of the PEH.</p> Full article ">Figure 2
<p>The PEH designed for road energy harvesting.</p> Full article ">Figure 3
<p>Connection mode of multilayer stacked piezoelectric ceramics. (<b>a</b>) Series connection mode; (<b>b</b>) parallel connection mode.</p> Full article ">Figure 4
<p>The test system.</p> Full article ">Figure 5
<p>Structure of the PEH. (<b>a</b>) Diagram of the PEH. (<b>b</b>) Arrangement of piezoelectric units in the PEH.</p> Full article ">Figure 6
<p>Diagram of the laboratory pavement loading test.</p> Full article ">Figure 7
<p>Piezoelectric energy collection circuit. (<b>a</b>) Schematic of the circuit. (<b>b</b>) The PCB of the circuit.</p> Full article ">Figure 8
<p>The specimens of Test A.</p> Full article ">Figure 9
<p>Comparison of electrical properties of different connection modes: (<b>a</b>) open peak–peak voltages of A-1 and A-2; (<b>b</b>) charge variations of A-1 and A-2; (<b>c</b>) generated electrical energy of A-1 and A-2.</p> Full article ">Figure 9 Cont.
<p>Comparison of electrical properties of different connection modes: (<b>a</b>) open peak–peak voltages of A-1 and A-2; (<b>b</b>) charge variations of A-1 and A-2; (<b>c</b>) generated electrical energy of A-1 and A-2.</p> Full article ">Figure 10
<p>The specimens of Test B.</p> Full article ">Figure 11
<p>Comparison of electrical properties of stack piezoelectric units with different layers: (<b>a</b>) the open peak–peak voltage of B-1, B-2 and B-3; (<b>b</b>) charge variation of B-1, B-2 and B-3; (<b>c</b>) generated electric energy of B-1, B-2 and B-3.</p> Full article ">Figure 12
<p>The specimens of test C.</p> Full article ">Figure 13
<p>Comparison of electrical properties of stack piezoelectric unites with the same volume and different height to cross section ratio: (<b>a</b>) the open peak–peak voltage of C-1, C-2 and C-3; (<b>b</b>) charge variation of C-1, C-2 and C-3; (<b>c</b>) generated electrical energy of C-1, C-2 and C-3.</p> Full article ">Figure 14
<p>The number of piezoelectric units from 8 to 15.</p> Full article ">Figure 15
<p>The electric energy signal of the PEH with different numbers of units.</p> Full article ">Figure 16
<p>The laboratory pavement loading test of the PEH road. (<b>a</b>) The accelerated pavement testing device. (<b>b</b>) The laboratory test road. (<b>c</b>) Monitor system of the voltage. (<b>d</b>) Voltage waveform of the energy storage capacitor.</p> Full article ">Figure 17
<p>The voltage of the supercapacitor.</p> Full article ">
16 pages, 5243 KiB  
Article
Real-Time and Efficient Traffic Information Acquisition via Pavement Vibration IoT Monitoring System
by Zhoujing Ye, Guannan Yan, Ya Wei, Bin Zhou, Ning Li, Shihui Shen and Linbing Wang
Sensors 2021, 21(8), 2679; https://doi.org/10.3390/s21082679 - 10 Apr 2021
Cited by 21 | Viewed by 3964
Abstract
Traditional road-embedded monitoring systems for traffic monitoring have the disadvantages of a short life, high energy consumption and data redundancy, resulting in insufficient durability and high cost. In order to improve the durability and efficiency of the road-embedded monitoring system, a pavement vibration [...] Read more.
Traditional road-embedded monitoring systems for traffic monitoring have the disadvantages of a short life, high energy consumption and data redundancy, resulting in insufficient durability and high cost. In order to improve the durability and efficiency of the road-embedded monitoring system, a pavement vibration monitoring system is developed based on the Internet of things (IoT). The system includes multi-acceleration sensing nodes, a gateway, and a cloud platform. The key design principles and technologies of each part of the system are proposed, which provides valuable experience for the application of IoT monitoring technology in road infrastructures. Characterized by low power consumption, distributed computing, and high extensibility properties, the pavement vibration IoT monitoring system can realize the monitoring, transmission, and analysis of pavement vibration signal, and acquires the real-time traffic information. This road-embedded system improves the intellectual capacity of road infrastructure and is conducive to the construction of a new generation of smart roads. Full article
(This article belongs to the Special Issue Piezoelectric Energy Harvesting Sensors and Their Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Pavement vibration IoT monitoring system.</p> Full article ">Figure 2
<p>Composition of acceleration sensing node.</p> Full article ">Figure 3
<p>The Printed Circuit Board (PCB) of the acceleration sensing node.</p> Full article ">Figure 4
<p>Circuit diagram of the accelerometer.</p> Full article ">Figure 5
<p>Circuit diagram of the XTR115.</p> Full article ">Figure 6
<p>The acquisition board.</p> Full article ">Figure 7
<p>Circuit diagram of acquisition board.</p> Full article ">Figure 8
<p>Gateway composition.</p> Full article ">Figure 9
<p>The overlapping upload mechanism.</p> Full article ">Figure 10
<p>Data processing algorithms in remote server.</p> Full article ">Figure 11
<p>Pavement vibration data of a two-axle vehicle passing through the monitoring area.</p> Full article ">Figure 12
<p>Website interface.</p> Full article ">Figure 13
<p>Variation in traffic count and temperature over hours.</p> Full article ">
18 pages, 3738 KiB  
Article
Pavement 3D Data Denoising Algorithm Based on Cell Meshing Ellipsoid Detection
by Chuang Yan, Ya Wei, Yong Xiao and Linbing Wang
Sensors 2021, 21(7), 2310; https://doi.org/10.3390/s21072310 - 25 Mar 2021
Cited by 4 | Viewed by 2242
Abstract
As a new measuring technique, laser 3D scanning technique has advantages of rapidity, safety, and accuracy. However, the measured result of laser scanning always contains some noise points due to the measuring principle and the scanning environment. These noise points will result in [...] Read more.
As a new measuring technique, laser 3D scanning technique has advantages of rapidity, safety, and accuracy. However, the measured result of laser scanning always contains some noise points due to the measuring principle and the scanning environment. These noise points will result in the precision loss during the 3D reconstruction. The commonly used denoising algorithms ignore the strong planarity feature of the pavement, and thus might mistakenly eliminate ground points. This study proposes an ellipsoid detection algorithm to emphasize the planarity feature of the pavement during the 3D scanned data denoising process. By counting neighbors within the ellipsoid neighborhood of each point, the threshold of each point can be calculated to distinguish if it is the ground point or the noise point. Meanwhile, to narrow down the detection space and to reduce the processing time, the proposed algorithm divides the cloud point into cells. The result proves that this denoising algorithm can identify and eliminate the scattered noise points and the foreign body noise points very well, providing precise data for later 3D reconstruction of the scanned pavement. Full article
(This article belongs to the Special Issue Piezoelectric Energy Harvesting Sensors and Their Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>The flow chart of pavement point cloud denoising algorithm based on cell meshing ellipsoid detection: (<b>a</b>) Preparation stage; (<b>b</b>) cell meshing the point cloud and pre-denoising; (<b>c</b>) Denoising by ellipsoid detection; (<b>d</b>) Comparison between cells; (<b>e</b>) Noise points elimination.</p> Full article ">Figure 2
<p>(<b>a</b>) The schematic diagram of cell meshing point cloud, (<b>b</b>) the surrounding cells of the point to be detected and (<b>c</b>) view 1, (<b>d</b>) view 2 and (<b>e</b>) view 3 of the (<b>b</b>), respectively.</p> Full article ">Figure 3
<p>Ground points in the Ellipsoidal neighborhood of the scattered noise point.</p> Full article ">Figure 4
<p>Schematic diagram of the ellipsoid detection of point cloud containing the foreign body, (<b>a</b>) 3D view, (<b>b</b>) side view of (<b>a</b>), (<b>c</b>) view 1 of (<b>b</b>) and (<b>d</b>) view 2 of (<b>b</b>).</p> Full article ">Figure 5
<p>(<b>a</b>) The impact of <math display="inline"><semantics> <mi>θ</mi> </semantics></math> and <span class="html-italic">a</span> on <span class="html-italic">S<sub>foreign body</sub></span> when <span class="html-italic">d</span> = 0.5 mm and (<b>b</b>) the impact of <math display="inline"><semantics> <mi>θ</mi> </semantics></math> and <span class="html-italic">d</span> on <span class="html-italic">S<sub>foreign bod</sub></span> when <span class="html-italic">a</span> = 10 mm.</p> Full article ">Figure 6
<p>(<b>a</b>) The simulated point cloud before denoising, (<b>b</b>) denoised result by spherical detection (relevant factors refer to No. 4 in <a href="#sensors-21-02310-t002" class="html-table">Table 2</a>) and (<b>c</b>) denoised result by ellipsoid detection (relevant factors refer to No. 3 in <a href="#sensors-21-02310-t002" class="html-table">Table 2</a>).</p> Full article ">Figure 7
<p>(<b>a</b>) The photo of the flat concrete pavement, (<b>b</b>) the original point cloud and (<b>c</b>) the original point cloud within in the target area.</p> Full article ">Figure 8
<p>(<b>a</b>) The photo of the flat concrete pavement with stones and kerbs, (<b>b</b>) the original point cloud and (<b>c</b>) the original point cloud within the target area.</p> Full article ">Figure 9
<p>The denoising results of the flat concrete pavement by using (<b>a</b>) No. 1, (<b>b</b>) No. 2, (<b>c</b>) No. 3, (<b>d</b>) No. 4, (<b>e</b>) No. 5 and (<b>f</b>) No. 6 parameter values in <a href="#sensors-21-02310-t004" class="html-table">Table 4</a>, respectively. (ED: ellipsoid detection; SD: spherical detection).</p> Full article ">
17 pages, 4382 KiB  
Article
Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors
by Cheng Zhang, Shihui Shen, Hai Huang and Linbing Wang
Sensors 2021, 21(5), 1721; https://doi.org/10.3390/s21051721 - 2 Mar 2021
Cited by 37 | Viewed by 4059
Abstract
Traffic information is critical for pavement design, management, and health monitoring. Numerous in-pavement sensors have been developed and installed to collect the traffic volume and loading amplitude. However, limited attention has been paid to the algorithm of vehicle speed estimation. This research focuses [...] Read more.
Traffic information is critical for pavement design, management, and health monitoring. Numerous in-pavement sensors have been developed and installed to collect the traffic volume and loading amplitude. However, limited attention has been paid to the algorithm of vehicle speed estimation. This research focuses on the estimation of the vehicle speed based on a cross-correlation method. A novel wireless micro-electromechanical sensor (MEMS), Smartrock is used to capture the triaxial acceleration, rotation, and stress data. The cross-correlation algorithms, i.e., normalized cross-correlation (NCC) algorithm, the smoothed coherence transform (SCOT) algorithm, and the phase transform (PHAT) algorithm, are applied to estimate the loading speed of an accelerated pavement test (APT) and the traffic speed in the field. The signal-noise-ratio (SNR) and the mean relative error (MRE) are utilized to evaluate the stability and accuracy of the algorithms. The results show that both the correlated noise and independent noise have significant influence in the field data. The SCOT algorithm is recommended for speed estimation with reasonable accuracy and stability because of a large SNR value and the lowest MRE value among the algorithms. The loading speed investigated in this study was within 50 km/h and further verification is needed for higher speed estimation. Full article
(This article belongs to the Special Issue Piezoelectric Energy Harvesting Sensors and Their Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>SmartRock sensors: (<b>a</b>) 3D printed SmartRock; (<b>b</b>) size of SmartRock; (<b>c</b>) Smartrock with stress sensors; (<b>d</b>) wireless data acquisition (DAQ).</p> Full article ">Figure 2
<p>Calibration of the stress cell of SmartRock: (<b>a</b>) Equipment of the direct shear test; (<b>b</b>) Voltage signal under pressure; (<b>c</b>) Relationship between voltage and stress.</p> Full article ">Figure 3
<p>The configuration of the APT test: (<b>a</b>) Top view; (<b>b</b>) Side view; (<b>c</b>) The embedded SmartRocks.</p> Full article ">Figure 4
<p>The configuration of the Field test: (<b>a</b>) Top view; (<b>b</b>) Side view.</p> Full article ">Figure 5
<p>An example of the collected vertical stress.</p> Full article ">Figure 6
<p>Collected Stress Signals in the Field (<b>a</b>) a series of stress signal; (<b>b</b>) case 1: two SmartRocks captured two axle loads, respectively; (<b>c</b>) case 2: one of the SmartRocks only captured an axle load; (<b>d</b>) case 3: two SmartRocks captured an axle load, respectively; (<b>e</b>) case 4: one of the SmartRock didn’t capture the signals.</p> Full article ">Figure 6 Cont.
<p>Collected Stress Signals in the Field (<b>a</b>) a series of stress signal; (<b>b</b>) case 1: two SmartRocks captured two axle loads, respectively; (<b>c</b>) case 2: one of the SmartRocks only captured an axle load; (<b>d</b>) case 3: two SmartRocks captured an axle load, respectively; (<b>e</b>) case 4: one of the SmartRock didn’t capture the signals.</p> Full article ">Figure 7
<p>An example of the cross-correlation results for APT test: (<b>a</b>) The NCC algorithm, (<b>b</b>) The PHAT algorithm, (<b>c</b>) The SCOT algorithm.</p> Full article ">Figure 7 Cont.
<p>An example of the cross-correlation results for APT test: (<b>a</b>) The NCC algorithm, (<b>b</b>) The PHAT algorithm, (<b>c</b>) The SCOT algorithm.</p> Full article ">Figure 8
<p>An example of the cross-correlation results for field test: (<b>a</b>) The NCC algorithm, (<b>b</b>) The PHAT algorithm, (<b>c</b>) The SCOT algorithm.</p> Full article ">Figure 8 Cont.
<p>An example of the cross-correlation results for field test: (<b>a</b>) The NCC algorithm, (<b>b</b>) The PHAT algorithm, (<b>c</b>) The SCOT algorithm.</p> Full article ">Figure 9
<p>An example of the cross-correlation results (<b>a</b>) case 2: one of the SmartRocks only captured an axle load; (<b>b</b>) case 3: two SmartRocks captured an axle load, respectively.</p> Full article ">Figure 10
<p>The calculated velocity: (<b>a</b>) The APT test results, (<b>b</b>) The field test results.</p> Full article ">Figure 10 Cont.
<p>The calculated velocity: (<b>a</b>) The APT test results, (<b>b</b>) The field test results.</p> Full article ">
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