Multiple Piezoelectric Sensors Research Articles

Evolution characteristics of deflagration wave system and flame acceleration effect of combustible gas in variable-caliber pipelines

To understand the deflagration phenomenon of combustible gas in gas pipelines and improve the design of pipeline structural strength, this work adopted the self-developed experimental platform with a length of 12 m and a diameter of 90 mm to simulate the transportation process of urban variable-caliber gas pipelines. Experiments were conducted in a 9.5% methane/air premixed gas environment using a high-voltage ignition system, high-resolution data acquisition system, multiple piezoelectric sensors, fiber-optic flame speed sensors, ultra-high-speed laser schlieren system, and laser texture technology. The effects of blocking ratios of 0.4, 0.6, and 0.8 on the evolution characteristics of the deflagration wave system and the acceleration effect of combustible gas flames were comprehensively investigated. The blocking ratio was varied by placing an annular block somewhere along the pipeline. The results reveal that: (1) As the blocking ratio increased from 0 to 0.8, the pressure peak of the flow field did not appear near the ignition location. Comparing different blocking ratios, 0.4 blocking ratio had the maximum pressure, reaching 302 kPa, 0.6 blocking ratio reached the pressure peak position earliest, and the maximum pressure occurred at 83 times the cross-sectional diameter; (2) The blocking ratio of 0.6 had the most obvious effect on the flame acceleration, it could accelerate nearly three times within a range of 2 m, with a peak velocity of 130 m/s and an acceleration of 829 m/s2; (3) By identifying the laser schlieren image, the flame acceleration process and the coupling relationship between the reflected wave and the flame were able to be clearly understood, and the obtained flame speed was consistent with the measurement speed of the fiber optic sensors. The research results can provide a basis for urban variable-caliber gas pipeline accident prevention and structural design, and provide new ideas for re-understanding the gas deflagration mechanism.

Active Vibration Control on a Smart Composite Structure Using Modal-Shaped Sliding Mode Control

Abstract This article proposes an active modal vibration control method based on a modal sliding mode controller applied to a smart material composite structure with integrated piezoelectric transducers as actuators and sensors. First, the electromechanical coupled system is identified using a modal reduced-order model. The sliding surface is based on the modal-filtered states and designed using a general formulation allowing the control of multiple vibration modes with multiple piezoelectric sensors and actuators. The performance and stability of the nonlinear controller are addressed and confirmed with the experimental results on a composite smart spoiler-shaped structure. The nonlinear switching control signal based on the modal-shaped sliding surface improves performances of the linear part of the control while maintaining not only stability but also robustness. The attenuation level achieved on the target modes on all piezoelectric sensors starts from −14 dB up to −22 dB, illustrating the strong potential of nonlinear switching control methods in active vibration control.

Pitch-catch UGW-based multiple damage inference: a heterogeneous graph interpretation

Ultrasonic guided waves (UGWs) have been extensively utilized in nondestructive testing and structural health monitoring (SHM) for detection and real-time monitoring of structural defects. By implementing multiple piezoelectric sensors onto a plane of the target structure to form a sensor network, damages within the sensing range can be detected or even visualized through a pitch-catch configuration. On the other hand, deep learning (DL) techniques have recently been widely used to aid UGW-based SHM when the waveform is over complicated to extract a specific mode of interest due to irregular structure or boundary reflections. However, not too much research work has been conducted to thoroughly combine sensor networks with DL. Existing research using DL approaches is mainly used to train and interpret waveforms from isolated sensor pairs. The topological structure of sensor layout and sensor-damage relative positions are hardly considered in the data-driven process. Motivated by these concerns, this study offers a first-of-its-kind perspective to interpret UGW data collected from a sensor network by mapping the physical sensor-damage layout into a graph, in which sensors and potential damages serve as graph vertices bearing heterogenous properties upon coming to UGWs and the process of UGW transmission between sensors are encapsulated as wavelike message passing between the vertices. A novel physics-informed end-to-end graph neural network model, named as WaveNet, was exquisitely and meticulously developed. By utilizing wave information and topological structure, WaveNet enables inference of multiple damages in terms of severity and location with satisfactory accuracy, even when the waveforms are chaotic, and the sensor arrangement is different at the training and testing stages. More importantly, beyond the SHM scenario, the present study is expected to enlighten new thinking on interconnecting physical wave propagation with virtual messaging passing in neural networks.

Structural Failure Detection Using Wireless Transmission Rate From Piezoelectric Energy Harvesters

This article presents a new self-powered wireless failure detection method that uses different signal transmission rates from piezoelectric energy harvesters. Reliable signal transmission from a wireless sensor network has been challenging due to the power supply issue, often covered by batteries that need regular replacement. Piezoelectric energy harvesting is an excellent option to power the sensors in a vibrational environment, but the power level is limited by input vibrational energy. In this article, we introduce a new failure detection strategy that uses multifunctional piezoelectric material for vibration sensing and energy harvesting. That is, when the vibration is stronger and the material strain is higher, piezoelectric material produces higher voltage, and power. Different power level from multiple piezoelectric sensors is used for sensing structural failure. We design a simple power management circuit that saves power proportional to the piezoelectric voltage so that the piezoelectric patch with higher strain can transmit more frequent wireless signals (higher transmission rate). To store the piezoelectric power, the circuit is composed of a full-bridge rectifier, a single pole double throw switch, a comparator with hysteresis, and a wireless transmitter (Zigbee). The failure detection performance is investigated in a case study that monitors screw joint failure in a vibrating plate. Reliability-based design optimization is conducted to determine the piezoelectric sensor network in terms of the number and sizes of multiple piezoelectric (PZT) patches. The test results show that the proposed system successfully powers the wireless transmitter using the ultra-low scale of power from the PZT sensors (microwatt) and detect the different combination of screw joint failure in the vibrating plate.

Measurement of heartbeat intervals in a sitting position using multiple piezoelectric sensors with body movement reduction.

In this study, we proposed a measurement system that extracts heartbeat interval data from multiple piezoelectric sensors placed on a chair that eliminates noise generated by body movement. We asked five healthy males (21-24 years old) to sit in an arbitrary position on a chair that had eight piezoelectric sensors attached, and heartbeat signals were measured. The experiment consisted of four measurements (5 min measurements after adequate rest), while performing specific body movements at a specific time, with deliberately mixed noise of body movements. To remove body movement noise, bandpass filter processing (4 Hz-20 Hz) was applied to the signal obtained from the piezoelectric sensors, and the heartbeat component was extracted using independent component analysis (number of separations was eight) on the processed waveform. For verification, the error rate was obtained before and after the removal of body movement noise, respectively. The error rate after removal of body motion was 2.91 ± 0.75%. In our previous study with two piezoelectric sensors without body movement, the error rate was 2.47 ± 2.66%. Therefore, our proposed measurement system may improve the accuracy of heartbeat interval detection, which can be observed continuously by removing the body motion.

Measuring Cardiorespiratory Information in Sitting Position using Multiple Piezoelectric Sensors

<p>We have been studying equipment to easily acquire cardiorespiratory information at home using piezoelectric sensors arranged on the seat surface of a chair. In our previous study, we suggested that the cardiac and respiratory components could be extracted by executing template matching using a two-dimensional cross-correlation function for the signals that were obtained from the piezoelectric sensors. However, there was a difficulty with the signal extraction, depending on the seating position. Therefore, in this study, we examined the measurement of the heartbeat and breathing interval using independent component analysis and multiple piezoelectric sensors. Moreover, the heartbeat and breathing intervals that were obtained from the extracted cardiorespiratory components using our developed automatic decision method were compared with those obtained from electrocardiogram and pneumogram. As a result, it was found that we could achieve better error rates (0.93±0.44% and 5.23±3.04% for the heartbeat and respiratory intervals, respectively) than in our previous study.</p>

Stripe-PZT Sensor-Based Baseline-Free Crack Diagnosis in a Structure with a Welded Stiffener.

This paper proposes a stripe-PZT sensor-based baseline-free crack diagnosis technique in the heat affected zone (HAZ) of a structure with a welded stiffener. The proposed technique enables one to identify and localize a crack in the HAZ using only current data measured using a stripe-PZT sensor. The use of the stripe-PZT sensor makes it possible to significantly improve the applicability to real structures and minimize man-made errors associated with the installation process by embedding multiple piezoelectric sensors onto a printed circuit board. Moreover, a new frequency-wavenumber analysis-based baseline-free crack diagnosis algorithm minimizes false alarms caused by environmental variations by avoiding simple comparison with the baseline data accumulated from the pristine condition of a target structure. The proposed technique is numerically as well as experimentally validated using a plate-like structure with a welded stiffener, reveling that it successfully identifies and localizes a crack in HAZ.

In Vitro Atherosclerotic Plaque Characterization by Acoustic Impedance Monitoring, Part I: Sensor Modeling, Design, and Fabrication

This investigation aims at developing a technique to diagnose the presence of plaque in arteries by measuring the variations of the electromechanical impedance of a piezoelectric (PZT) sensor in contact with the arterial surface. The proposed technique makes use of either one or multiple PZT sensors integrated on a balloon and used prior to percutaneous transmural angioplasty interventions. The system we envision consists of two components: the active, miniaturized PZT sensors (surface-mounted on an angioplasty balloon), and the computer and software. In the proposed technique, the angioplasty balloon with the wall-mounted sensor(s) is inflated in the artery to make contact with the plaque. Initially, low pressure is used not to disrupt the plaque. The diagnostic test feeds an electrical signal that excites the sensors; by analyzing the sensors' response, information on features and properties of the plaque is obtained. Doctors will use this information to recognize unstable plaque prior to full pressurization of the angioplasty balloon. The first part of the article describes the procedure used in designing a sensor for the detection of unstable plaque by the impedance method. Both finite element methods and an analytical model — a modified version of the Krimholtz—Leedom—Matthaei model (Krimholtz et al., 1970) — are utilized in the design of the sensor. An appropriate fabrication technique for manufacturing small-scale PZT sensors is also proposed for use in smaller arteries such as the coronary arteries. Experimentation and verification of the proposed technique is presented in the second paper stemmed from this investigation.