Piezoelectric Transducer System Research Articles

Brake fault diagnosis using a voting ensemble of machine learning classifiers

Brake fault diagnosis is a crucial aspect of enhancing driving safety, as various faults such as air in brake fluid, brake oil spill, reservoir leak, mechanical fade and distinct types of brake pad wear can compromise vehicle safety. This study presents a method for timely fault detection by analyzing vibration signals. Vibration signals for normal and faulty conditions were captured using a hydraulic brake test setup equipped with a piezoelectric transducer and data acquisition system. Feature extraction was performed using an auto-regressive moving average (ARMA) model. The performance of five different classifiers namely, random forest (RF), Naive Bayes (NB), instance-based k-nearest neighbours (IBk), logistic model trees (LMT) and J48 decision tree was evaluated. The LMT classifier achieved the highest accuracy at 95.00 % followed by IBk, RF, J48 and NB with accuracies of 92.00 %, 90.00 %, 90.00 % and 87.00 %. To further improve the diagnosis accuracy, a voting-based ensemble approach was employed by combining two, three, four and five classifiers with the application of five different voting strategies. The results obtained showcase that a combination of three classifiers LMT, IBk and NB utilizing the majority voting rule yielded an enhanced classification accuracy of 98.00 % highlighting the effectiveness of this ensemble method in brake fault diagnosis. A similar classification accuracy of 98.00 % was achieved by four (LMT-IBk-RF-J48) and five (IBk-RF-MLP-J48-NB) classifier ensembles; however, considering the computational complexity three classifier ensembles were selected. Whilst two classifier ensembles (IBk-J48) achieved a maximum classification accuracy of 96.00 %.

Effect of a learning-based approach using the echo-signals obtained by a single-unit transducer on detection of breast tumors.

1566 Background: Breast cancer mortalities in the less developed countries are predicted to increase over the next decades mainly due to the low utilization rates of breast cancer screening programs. Additionally, studies have shown that the sensitivity and accuracy of breast self-examination in women are suboptimal. Therefore, it is important to develop a novel and affordable technology that can accurately detect small breast tumors in women. We have developed a deep learning-based echo- signal processing system that can detect breast tumors with high sensitivity and accuracy by merely obtaining ego-motion estimated echo-signals generated by a single-unit piezoelectric transducer system. Methods: We have prospectively collected echo-signals using a single-unit piezoelectric transducer system in 200 patients with breast tumors who underwent curative surgery at the Seoul National University Hospital and SMG-SNU Boramae Medical Center between Oct 2022 and Feb 2024. The data from the first 131 cases was used for the training and testing of the deep learning model with the ratio of 8:2, and the subsequent data from 69 cases was used for the external validation. We used ImageNet pre-trained ResNet50 model to train the deep learning model and adopted various data augmentation methods for model robustness. Five nested cross-validation was used to ensure the generalization performance. Results: The median age of the enrolled patients was 55 (IOR, 47-63) and 65 patients (32.5%) had non-palpable breast tumors. Nearly half of the patients (n=93, 46.5%) had tumors smaller than 2.0 cm in the largest dimension. Our deep learning algorithm showed outstanding performance with the area under the curve (AUC) of 0.914±0.022 and 0.964±0.011 for the image- and region-level, respectively, in the training and testing set (n=131). In the external validation set (n=69), the model demonstrated an AUC of 0.846±0.012 and 0.932±0.009 for the image- and region-level, respectively. As shown (Table), the accuracy, sensitivity, and specificity were substantially improved for the region-level analysis when compared with those of the image-level analysis. Conclusions: Using a prospectively collected data from 200 patients, we developed a highly accurate and robust deep learning model to detect breast tumors in women by processing echo-signals obtained by a single-unit piezoelectric transducer system. Our data indicate that deep learning approach based on low-volume echo signal can lead to a novel and affordable breast tumor screening strategy that can improve early breast cancer detection.[Table: see text]

Trajectory optimization method based on ellipse model for dynamic motion control of piezoelectric transducer in an optical resonance cavity

External-cavity diode laser (ECDL) can continuously change the output wavelength by scanning its optical resonance cavity (ORC), which is a piezoelectric transducer (PZT) actuated mechanical system. However, because of the inherent hysteresis characteristic of PZT and limited system bandwidth, the dynamic response of the ORC system exhibits both the nonlinearity and vibration while high-frequency triangular wave scanning, thereby resulting in the degradation of the output wavelength performance of the ECDL. As a result, a trajectory optimization method is presented to improve the scanning characteristics of the ORC system. Firstly, the hysteresis behavior of PZT is described by the elliptic model to generate a compensation voltage for the PZT. Secondly, the cusp region of the compensation voltage is optimized by a frequency domain objective function to greatly reduce the high-order harmonic components of the compensation voltage. Finally, an optimal voltage trajectory for driving the ORC system is obtained. The experimental results demonstrate the validity of our proposed trajectory optimization method for simultaneously suppressing the nonlinearity and vibration of the ORC system within the scanning frequency of 100 Hz.

Improving APT Systems' Performance in Air via Impedance Matching and 3D-Printed Clamp.

This paper presents a study on improving the performance of the acoustic piezoelectric transducer system in air, as the low acoustic impedance of air leads to suboptimal system performance. Impedance matching techniques can enhance the acoustic power transfer (APT) system's performance in air. This study integrates an impedance matching circuit into the Mason circuit and investigates the impact of fixed constraints on the piezoelectric transducer's sound pressure and output voltage. Additionally, this paper proposes a novel equilateral triangular peripheral clamp that is entirely 3D-printable and cost-effective. This study analyses the peripheral clamp's impedance and distance characteristics and confirms its effectiveness through consistent experimental and simulation results. The findings of this study can aid researchers and practitioners in various fields that employ APT systems to improve their performance in air.

Vibration harvesting integrated into vehicle suspension and bodywork

This work proposes a new piezoelectric transducer system with four freedoms of movement modelled and evaluated by mechatronic techniques. The proposed modelling techniques (finite element and bond graph) were performed in a 20-Sim framework attached to the ANSYS software. The established harvester system has the ability to increase the driver's comfort when travelling on several types of road surfaces. The piezoelectric harvester is designed to investigate and provide the health requirement and ride comfort of the vehicle's drives on random road surfaces. The simulation results affirm that the improved piezoelectric transducer arrangement is more productive for various aspects. The power recovery is significantly enhanced as well as the driving comfort on the three road categories. Finally, the harvestable power amount is highlighted and is graphically discussed for several specific applications.

Various Types of Defects Detection in Flat and Curved Laminated Composite Plates Using Nonintrusive Lamb Wave System

Abstract Composite materials are widely used in aerospace industries due to their light weight, strength, and various other desired properties. However, they are susceptible to various defects occurring during the manufacturing process or in service. Typical defects include porosity, wrinkles, and delamination. Nondestructive means of detection of the defects at any stage are of great importance to ensure quality and safety of composite structures. A nonintrusive removable Lamb wave system and accompanied methodology that is not material dependent are presented in this paper to detect various types of typical defects in laminated composite plates, flat or curved. Through multidimensional data acquisition and processing, abnormality in waves caused by defects is captured and presented in inspection images. The methodologies are demonstrated in two cases: delamination in a curved plate and wrinkles in a flat plate. Overall, the results show that Lamb waves using the piezoelectric transducer and laser vibrometer system can be used for various types of defects inspection in flat or curved composite plates.

Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools. This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach. A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe, where the condition of tool is monitored using vibration characteristics. The vibration signals for conditions such as heathy, damaged, thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system. The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques. The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm. The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis. The decision tree model produced the classification accuracy as 94.78% with five selected features. The developed fuzzy model produced the classification accuracy as 94.02% with five membership functions. Hence, the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.

Noninvasive Mechanochemical Imaging in Unconstrained Caenorhabditis elegans.

Physical forces are transduced into chemical reactions, thereby ultimately making a large impact on the whole-animal level phenotypes such as homeostasis, development and behavior. To understand mechano-chemical transduction, mechanical input should be quantitatively delivered with controllable vibration properties–frequency, amplitude and duration, and its chemical output should be noninvasively quantified in an unconstrained animal. However, such an experimental system has not been established so far. Here, we develop a noninvasive and unconstrained mechanochemical imaging microscopy. This microscopy enables us to evoke nano-scale nonlocalized vibrations with controllable vibration properties using a piezoelectric acoustic transducer system and quantify calcium response of a freely moving C. elegans at a single cell resolution. Using this microscopy, we clearly detected the calcium response of a single interneuron during C. elegans escape response to nano-scale vibration. Thus, this microscopy will facilitate understanding of in vivo mechanochemical physiology in the future.

A shoe-equipped piezoelectric transducer system based on PVDF film

ABSTRACTMany piezoelectric transducers mounted in shoes have been conducted to harvest energy from walking. However, the energy harvested is influenced by some factors, such as the structure of piezoelectric transducer, piezoelectric material and so forth. Taking into account these factors, a wide-band energy harvester-shoe equipped piezoelectric transducer with cantilever beam structure based on polyvinylidene fluoride (PVDF) piezoelectric film is designed and examined in this paper. The piezoelectric transducer makes little difference in the sensation with the device mounted in the shoes when walking. The harvester can provide a maximum output power of 0.48 mW at the load resistance of 300 kΩ. This study demonstrates that it is feasible to scavenge energy from human motion by piezoelectric harvester to power wearable sensors, such as pedometer, respiration and pulse monitoring system and so on.

Microbubble generation by piezotransducer for biological studies.

Bubbles induced by blast waves or shocks are speculated to be the major cause of damages in biological cells in mild traumatic brain injuries. Microbubble collapse was found to induce noticeable cell detachment from the cell substrate, changes in focal adhesion and biomechanics. To better understand the bubble mechanism, we would like to construct a system, which allows us to clearly differentiate the impact of bubbles from that of shocks. Such a generator needs to be low profile in order to place under a microscope. A piezoelectric transducer system was designed to meet the need. The system uses either a flat or a spherical focusing piezoelectric transducer to produce microbubbles in a cuvette loaded with cell-culture medium. The transducer is placed on the side of the cuvette with its axis lining horizontally. A cover slip is placed on the top of the cuvette. The impact of the waves to the cells is minimized as the cover slip is parallel to the direction of the wave. Only bubbles from the medium reach the cover slip and interact with cells. The effect of bubbles therefore can be separated that of pressure waves. The bubbles collected on a cover slip range in size from 100 μm to 10 μm in radius, but the dominant size is 20-30 μm.

Noninvasive detection of sleep/wake changes and cataplexy-like behaviors in orexin/ataxin-3 transgenic narcoleptic mice across the disease onset

Sleep and behavioral monitoring of young mice is necessary for understating the progress of symptoms in congenital and acquired diseases associated with sleep and movement disorders. In the current study, we have developed a non-invasive sleep monitoring system that identifies wake and sleep patterns of newborn mice using a simple piezoelectric transducer (PZT). Using this system, we have succeeded in detecting age-dependent occurrences and changes in sleep fragmentation of orexin/ataxin-3 narcoleptic mice (a narcoleptic mouse model with postnatal hypocretin/orexin cell death) across the disease onset. We also detected REM sleep/cataplexy patterns (i.e., immobility with clear heartbeat [IMHB] signals due to the flaccid posture) by the PZT system, and found that sudden onset of REM sleep-like episodes specifically occur in narcoleptic, but not in wild type mice, suggesting that these episodes are likely cataplexy. In contrast, gradual onset of IMHB likely reflects occurrence of REM sleep.In summary, we have shown that the PZT system is useful as a non-invasive sleep and behavior monitoring system to analyze the developmental aspects of sleep and movement disorders in mice models.

The Impacts of Weighting Functions on the Robust Performance of a Multi-Axial

This paper applies robust control strategies to a piezoelectric transducer (PZT) stage, and investigates the influences of weighting functions on the system performance. The designed controllers are implemented for experimental verification, and are shown to significantly improve aspects of the system performance, such as settling time, overshoot, and root-mean-square error. The developed loop-shaping design procedures can be directly applied to general PZT systems.

Comparative study of decision tree classifier and best first tree classifier for fault diagnosis of automobile hydraulic brake system using statistical features

Hydraulic brakes are the most important components in automobile. It requires advanced supervision and fault diagnosis to improve the safety of passenger, reliability and economy. Condition monitoring is one of the major division through which the reliability of such components could be monitored. The condition of the brake components can be monitored by using the vibration characteristics which will reveal the condition of the brake systems. In this paper machine learning algorithm using vibration monitoring is proposed as a possible solution to this problem. From the hydraulic brake test set up, the vibration signals were acquired by using a piezoelectric transducer and data acquisition system. C4.5 decision tree algorithm was used to extract statistical features from vibration signals. Feature selection was also carried out. Since no much of methodologies are available to find the effective number of features for a given problem, a detailed study is needed to find the best possible number of features. Hence the effect of number of features was studied by using decision tree. The selected features were classified using C4.5 decision tree algorithm and Best first decision tree algorithm with pre pruning and post pruning techniques. The results are discussed and conclusions of the study are presented.

A novel method of dynamic correction in the time domain

The dynamic error of measured signals is sometimes unacceptably large. If the dynamic properties of the measurement system are known, the true physical signal may to some extent be re-constructed. With a parametrized characterization of the system and sampled signals, time-domain digital filters may be utilized for correction. In the present work a general method for synthesizing such correction filters is developed. It maps the dynamic parameters of the measurement system directly on to the filter coefficients and utilizes time reversed filtering. This avoids commonly used numerical optimization in the filter synthesis. The method of correction is simple with absolute repeatability and stability, and results in a low residual error. Explicit criteria to control both the horizontal (time) and vertical (amplitude) discretization errors are presented in terms of the utilization of bandwidth and noise gain, respectively. To evaluate how close to optimal the correction is, these errors are also formulated in relation to the signal-to-noise ratio of the original measurement system. For purposes of illustration, typical mechanical and piezo-electric transducer systems for measuring force, pressure or acceleration are simulated and dynamically corrected with such dedicated digital filters.

Numerical modeling of double-layered piezoelectric transducer systems using a high-order discontinuous Galerkin method

A high-order discontinuous Galerkin finite element method, on triangular elements, is used to model piezoelectric systems. Non-destructive testing problems are studied. In particular, flex circuit transducer designs which can be used to obtain information about the surroundings. The focus is to look at simple transducer designs, where the system reduces to a two-dimensional plane strain problem or a one-dimensional longitudinal strain problem. Numerical modulations are compared to analytical/exact solutions obtained from a longitudinal strain model, in both time and frequency domains.

Plasmadynamic hypervelocity dust injector for the National Spherical Torus Experiment

The design and construction of a plasmadynamic device to accelerate dust to hypervelocities is presented. High speed dust will be used to measure magnetic field lines in the National Spherical Torus Experiment. The plasma gun produces a high density (ne≈1018cm−3) and low temperature (a few eV) deuterium plasma, ejected by J×B forces which provide drag on the dust particles in its path. The dust will be entrained by the plasma to velocities of 1–30km∕s, depending on the dust mass. Carbon dust particles will be used, with diameters from 1to50μm. The key components of the plasmadynamic accelerator are a coaxial plasma gun operated at 10kV (with an estimated discharge current of 200kA), a dust dispenser activated by a piezoelectric transducer, and power and remote-control systems.

SIMULATION OF PULSER RECEIVER SYSTEM FOR ULTRASONIC MEASUREMENTS

The success of modern electronics is built on the possibility to accurately predict system behavior by using simulation tools. This paradigm can be extended to components such as piezoelectric transducers attached to the electronics. The ability to simulate both piezoelectric transducer and electronics together renders possible effective optimizations at system level, i.e. minimizing size, cost and power consumption. In this paper a computer simulation of a combined electronics and piezoelectric transducer system is explored. The analogy between acoustic wave propagation and wave propagation in an electric transmission line is given. The simulation approach is applied to a pulser-receiver setup for the determination of speed of sound and attenuation in liquids. Experiments and simulations are made for fixed temperature and in the frequency range 1–10 MHz using ethanol, methanol, carbon tetrachloride, acetone, benzene and distilled water as test samples. Comparison shows a good agreement between simulation and experiments. Furthermore, the use of an ultrasonic simulation package allows for the development of the associated electronics to amplify and process the received ultrasonic signals.

System for simultaneously monitoring heart and breathing rate in mice using a piezoelectric transducer

We propose a novel system for simultaneously monitoring the heart rate (HR) and breathing rate (BR) of anesthetized mice using a piezoelectric transducer (PZT) placed under the body. It is known that a PZT, which transforms mechanical vibrations into electrical signals, can detect heart sounds and breathing movements. However, no PZT system has been reported for simultaneous and continuous monitoring of HR and BR, possibly due to difficulties in eliminating noises and errors caused by the amplitude fluctuation of heart sounds and breathing movements. These difficulties were overcome by custom-designed analogue circuitry and a microprocessor program, resulting in detection of HR and BR with a high reliability compared to the values obtained from ECG and a conventional airflow sensor. We believe that this practical and easy-to-use system can be applied to a wide variety of basic and clinical research fields.

Effect of tool drop on the performance of a high-frequency piezoelectric wire-bonding transducer system

This work describes a method to determine a suitable tool drop for clamping the bonding tool in a high-frequency (130 kHz) piezoelectric horn transducer used for wire bonding. Effect of different tool drops on four critical parameters, namely, the resonant frequency, the electrical impedance, the phase angle, and the tool tip displacement amplitude, were studied with the transducer system being driven at normal bonding power. Strong interdependence was observed among these parameters. The results were interpreted from the system stability and other physical point of views. Appropriate ranges of tool drop were found. With the tool drop in these ranges, it can produce desirable characteristics for the wire bonding.

The Effect of Discontinuities on the Long-range Propagation of Lamb Waves in Pipes

Laboratory tests and supporting finite element predictions of the interaction of Lamb waves with typical features found in chemical plant pipework are reported. The L(0, 2) mode is an attractive mode to use for long-range (> 15 m) propagation in 2–12 inch (51–305 mm) nominal bore pipes and the behaviour of this mode has been studied using a bonded piezoelectric transducer system. The amplitude of the reflection from butt welds and welded supports was found to be between 5 and 20 per cent of the incident amplitude, while flanges gave almost 100 per cent reflection. It is therefore not possible to test past a flange. It has been shown that wet or dry insulation has little effect on the propagation of the L(0, 2) mode, so inspection under insulation is feasible. Initial work on the reflections from defects is also reported. It has been shown that when the axial extent of the defect is significantly less than a wavelength of the L(0, 2) mode (about 80 mm at 70 kHz), the reflectivity is largely a function of the depth and circumferential extent of the defect. The reflections from notches, troughs and corrosion patches have been detected successfully.