Electrostatic Sensor Research Articles

Aluminum dust concentration detection based on LSTM-Kalman filter

Industrial dust emissions present serious hazards, including respiratory issues and explosion risks. Traditional dust concentration detection methods are often compromised by environmental factors. This study introduces a novel FFT-KF-LSTM-NET model to predict high-concentration aluminum powder levels, utilizing 128 sets of electrostatic induction measurement data. By applying the Fast Fourier Transform (FFT) to eliminate low-frequency interference, integrating Long Short-Term Memory (LSTM) networks for advanced time series analysis, and using the Kalman Filter (KF) for rapid model convergence, this approach significantly enhances prediction accuracy. The model achieves an 82% improvement in Mean Squared Error (MSE), reducing it to 0.1336, outperforming traditional methods. Furthermore, by modeling the voltage signal generated by charged dust particles in the electrostatic induction sensor's sensing area and using the first derivative of the voltage signal as a learning feature, the model's prediction speed is increased from 1.5 s-2 s–0.5 s, with improved anti-interference capabilities. These advancements position the FFT-KF-LSTM-NET model as a crucial tool for real-time, reliable dust concentration detection in industrial environments.

A self-adaptive fractional-order PID controller for the particle velocity regulation in a pneumatic conveying system

This paper presents the methodical development of a state-error-driven self-adaptive fractional-order proportional–integral–derivative (AFOPID) control algorithm to efficiently regulate the velocity of pneumatically conveyed particles at the desired set point and to prevent the blockage of particles in a pipe due to an imbalanced combination of their velocity and corresponding mass flow rate. The proposed fractional control law is constituted by adaptively modulating fractional orders of the integral and differential operators in the control based on the state-error variations in the velocity of solid particles. The particle’s velocity is measured and updated via electrostatic sensors in conjunction with cross-correlation signal processing algorithms. All the other fixed hyper-parameters associated with the proportional–integral–derivative (PID) control scheme are meta-heuristically optimized by using a genetic algorithm. The proposed AFOPID is benchmarked against conventional integer-order PID and the fractional-order proportional–integral–derivative (FOPID) controllers. Experiments are performed on a laboratory-scale test rig to comparatively analyze the aforesaid control schemes, where each controller is examined for three velocity set points and three disturbance levels. The experimental results validate the superior time optimality and robustness of the proposed AFOPID controller against bounded disturbances and abrupt velocity set-point variations by manifesting relatively faster settling time, low overshoots (and undershoots), and smaller steady-state fluctuations.

Data-driven system identification and model predictive control of pneumatic conveying using nonlinear dynamics analysis for optimised energy consumption

Pneumatic conveying systems provide secure transportation of particulate material and a dust-free environment. These systems face high energy consumption, material degradation, and pipeline blockages. This research presents an innovative solution by integrating nonlinear dynamics analysis of electrostatic sensor data, including chaos and recurrence quantification analysis, sparse identification of nonlinear dynamics with control (SINDYc) and model predictive control (MPC). The Lyapunov exponent, approximate entropy and recurrence rate of electrostatic sensor data reveal the chaotic nature of gas-solid flows. MPC framework was tailored for real-time optimisation of a pneumatic conveying system. SINDYc system models were developed using data collected from an open-loop control pneumatic conveying process to conduct MPC simulations and select an appropriate model for real-time control. This research illustrates the potential of integrating nonlinear dynamics analysis, SINDYc integrated and MPC for enhanced system performance, showcased a significant reduction in energy consumption without compromising the system's efficiency or reliability.

Structural simulation and performance evaluation of self-priming electrostatic diesel vehicle emission particle sensor

Here is reported a self-priming electrostatic particulate matter (PM) sensor for real-time monitoring of diesel exhaust under different operating conditions. Initially, a multi-physics coupling model for the sensor was established. By varying the exhaust flow and temperature, the emissions of diesel vehicles were simulated to evaluate the sensor. The results showed that within the typical velocities (5 to 60 m/s) and temperature ranges (100 to 500 °C) of diesel exhaust, the sensor outlet pressure was consistently below the inlet pressure, with a maximum differential pressure of 259 Pa, indicating the capability for self-priming sampling of exhaust under different operating conditions. Additionally, the calibration platform was constructed using a miniature inverted soot generator which produced a series of samples to calibrate the relationship between current and mass concentration with a correlation coefficient of 0.99. From 0 to 100 mg/m3, the PM sensor exhibited a relative error between −6.00% and 6.00% with good stability and consistency for samples of different sizes and concentrations, with correlation coefficients exceeding 0.98. The results between the self-developed sensor and a commercial instrument revealed that the former provided high-precision real-time measurement of diesel exhaust.

Simulation of electrostatic particulate matter sensor regeneration based on the particulate deposition patterns

Purpose This study aims to establish a multi-physics-coupled model for an electrostatic particulate matter (PM) sensor. The focus lies on investigating the deposition patterns of particles within the sensor and the variation in the regeneration temperature field. Design/methodology/approach Computational simulations were initially conducted to analyse the distribution of particles under different temperature and airflow conditions. The study investigates how particles deposit within the sensor and explores methods to expedite the combustion of deposited particles for subsequent measurements. Findings The results indicate that a significant portion of the particles, approximately 61.8% of the total deposited particles, accumulates on the inside of the protective cover. To facilitate rapid combustion of these deposited particles, a ceramic heater was embedded within the metal shielding layer and tightly integrated with the high-voltage electrode. Silicon nitride ceramic, selected for its high strength, elevated temperature stability and excellent thermal conductivity, enables a relatively fast heating rate, ensuring a uniform temperature field distribution. Applying 27 W power to the silicon nitride heater rapidly raises the gas flow region's temperature within the sensor head to achieve a high-temperature regeneration state. Computational results demonstrate that within 200 s of heater operation, the sensor's internal temperature can exceed 600 °C, effectively ensuring thorough combustion of the deposited particles. Originality/value This study presents a novel approach to address the challenges associated with particle deposition in electrostatic PM sensors. By integrating a ceramic heater with specific material properties, the study proposes an effective method to expedite particle combustion for enhanced sensor performance.

Finite element analysis for the measurement error of electrostatic accelerometer due to the electrode misalignment

Abstract The electrostatic accelerometer is the key payload of many space missions, such as satellite gravity measurements, space gravitational experiments, with a typical precision requirement from nano-g to femto-g. The measurement error model analysis is an important issue for the electrostatic accelerometer with numerous error sources. The traditional method by theoretical analysis is complicated to evaluate the complete measurement error models quantitatively especially when the machining and assembly errors of the sensor head are considered. Limited by the earth gravity and seismic noise which is much higher than the intrinsic noise, the complete error model of electrostatic accelerometers can also hardly be tested on ground. In this paper, the method by using finite element analysis of the sensor head is studied. The simulation model for an electrostatic accelerometer sensor head is built, and the multi-conductor capacitance matrix data is simulated. The accuracy of the capacitance simulation is evaluated in three ways including the convergence check of the capacitance with the mesh size, the symmetry verification, and the sensitivity comparison with the theoretical model. Finally, the accelerometer measurement model is analyzed based on the simulation data, using the case of rotating installation misalignment of the upper electrode as an example. The measurement model and error items of one horizontal axis and one rotating axis are quantitatively evaluated. The method proposed in this paper provide a new effective approach to the measurement model analysis of the electrostatic accelerometers in complex working scenarios both in orbit and on ground, which could be helpful to enhance the performance and the efficiency of application.

Investigations into the flow dynamics of mixed biomass particles in a fluidized bed through Hilbert-Huang transformation and data-driven modelling

Flow dynamics of binary particles are investigated to realize the monitoring and optimization of fluidized beds. It is a challenge to accurately classify the mass fraction of mixed biomass, considering the limitations of existing techniques. The data collected from an electrostatic sensor array is analyzed. Cross correlation, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT) are applied to process the signals. Under a higher mass fraction of the wood sawdust, the segregation behavior occurs, and the high energy region of HHT spectrum increases. Furthermore, two data-driven models are trained based on a hybrid wavelet scattering transform and bidirectional long short-term memory (ST-BiLSTM) network and a EMD and BiLSTM (EMD-BiLSTM) network to identify the mass fractions of the mixed biomass, with accuracies of 92% and 99%. The electrostatic sensing combined with the EMD-BiLSTM model is effective to classify the mass fraction of the mixed biomass.

Research on multi-velocity measurement method of solid particles based on interdigital electrostatic sensor

Abstract In gas-solid two-phase flow, the velocities of solid particles will show different distributions as the flow pattern changes, and it is a challenging task to achieve the measurement of different velocity parameters in the flow field. To obtain the velocity information of solid particles in the flow field, a novel method to achieve multi-velocity measurement of solid particles is proposed by using the spatial filtering characteristics of interdigital electrostatic sensor with variational mode decomposition (VMD). Firstly, a three-dimensional model of the interdigital electrostatic sensor is established using COMSOL simulation software. Based on its spatial dynamic sensitivity, a theoretical analysis of its spatial filtering characteristics is conducted to obtain a mathematical relationship for velocity measurement. Subsequently, the VMD decomposition method is applied to an experiment involving charged wires, with the experimental results verifying the effectiveness of VMD in achieving multi-scale decomposition of electrostatic signals. Finally, the proposed measurement method is validated on a gravity feeding device and a pneumatic conveying system. The experimental results demonstrate that the method exhibits good feasibility.

Behavior analysis of comb-drive actuators operating in near-zero-overlap configuration

Comb structures are widely used in micro-electromechanical systems as basic components of electrostatic actuators, capacitive sensors, resonators and energy harvesters. Typically, they are operated with a large overlap of the comb fingers, in the linear region. Here we study the behavior of comb-drive actuators during the displacement along the axis of the fingers in the unconventional regime where the fingers are slightly overlapping (around the width d of the fingers), which has recently gained attention in the MEMS community. In the intermediate region where the overlap approaches zero, fringing electric fields induce a non-linear variation of the capacitance, yielding a significant electrostatic force between the combs even without finger overlap with a maximum gradient occurring in this region. The comb capacitance variation with overlap was calculated using analytical and FEM modeling and confirmed with experimental results. We explored the implications of this condition in both static and dynamic modes, highlighting the effect of spring softening arising from the second derivative of the capacitance. These findings introduce new operational possibilities for actuators and sensors and substantial tunability in frequency for resonators.

The Electrostatic Induction Characteristics of SiC/SiC Particles in Aero-Engine Exhaust Gases: A Simulated Experiment and Analysis

This study investigates the electrostatic induction characteristics of silicon carbide-fiber-reinforced silicon carbide (SiC/SiC) particles within aero-engine exhaust gases using a dedicated J20 turbojet engine experimental platform. Our comprehensive experiments explored the electrostatic properties of SiC/SiC particles under varying engine operational states—specifically focusing on different thermal conditions, particle mass concentrations, particle sizes, and exhaust gas velocities compared to those of common engine exhaust constituents like carbon (C) and iron (Fe) particles. The results demonstrate that SiC/SiC particles consistently maintain a stable positive charge across varied temperatures, significantly diverging from the behaviors of carbon (C) and iron (Fe) particles. Additionally, our findings reveal that higher mass concentrations of SiC/SiC particles, smaller particle sizes within a certain range, and greater exhaust gas velocities of the aero-engine all lead to increased particle charge and more pronounced electrostatic induction characteristics. This study highlights the potential of electrostatic sensors for the early detection and diagnosis of failures in aero-engines, offering crucial insights into the development of more resilient real-time aero-engine health monitoring systems.

Short-wavelength infrared light sensing using an electrostatic MEMS resonator integrated with a plasmonic optical absorber

Near-infrared light detectors are employed in various fields such as food inspection for spectroscopic measurements. Despite the fact that photodiodes made of compound semiconductors are used in conjunction with optical filters or monochromators for highly sensitive and wavelength-selective detection of near-infrared light, improvements in device size and cost are necessary for widespread application. Therefore, we propose the use of silicon microelectromechanical systems (MEMS) sensors. This study proposes a single-crystalline Si electrostatic MEMS resonator sensor that is integrated with a gold nanostructure near-infrared light absorber. The device utilizes the resonance frequency shift that occurs due to the thermal stress caused by the heat generated by the near-infrared light on the resonator. A gold nanostructure absorber is mounted on the resonator to enhance photothermal conversion efficiency, enabling the detection of near-infrared light with wavelength selectivity. We fabricated an Au nanostructure-mounted electrostatic transducer and evaluated its performance by measuring its resonance under near-infrared light irradiation. In a light intensity detection experiment, it was found that the relative resonant frequency shift increased linearly with the input in the range below 100 μW, which is consistent with the trend against the input heat. The minimum detection resolution of the light intensity was approximately 5 nW, and the minimum detectable intensity was 3.8 nW. The linear relationship between the optical absorption coefficient and the relative resonance frequency shift was clarified. An array of the devices with different absorption wavelength bands can be used to realize an ultra-compact spectroscopic analysis device without the need for traditional optical filters or monochromators.

RFX-mod2 diagnostic capability enhancements for the exploration of multi-magnetic-configurations

The RFX-mod2 device, the upgraded version of the previous RFX-mod with a modified magnetic boundary, is presently under realization and will start to be operated in 2025. Significant upgrades of the diagnostic capabilities have been proposed and are under development. These include a largely increased number of in-vessel magnetic and electrostatic sensors, a new fast reciprocating manipulator for the exploration of the edge plasma in a wide range of experimental conditions, the improved Thomson scattering and soft x-ray diagnostics system for a detailed determination of the behavior of the electron temperature profile, new dedicated systems for the space and time resolved analysis of x-ray spectra and neutron rate, a reflectometric diagnostic for real-time determination of plasma position, two diagnostics devoted to the imaging of light impurities and influxes behavior along with arrays of halo current sensors. These diagnostic upgrades will be accompanied by a significant effort to improve the control of the electron density and of the impurity influxes by means of proper treatment of plasma facing components with in-vessel fixed electrodes distributed over the first wall. The described advancements will allow a deeper understanding of physics phenomena in the wide variety of magnetic configurations, including the tokamak, the reversed-field pinch and the Ultra-low q, which can be produced in RFX-mod2 thanks to its flexibility and unique MHD control capabilities.

Unraveling the nature of sensing in electrostatic MEMS gas sensors

This paper investigates the fundamental sensing mechanism of electrostatic MEMS gas sensors. It compares among the responsivities of a set of MEMS isopropanol sensors before and after functionalization, and in the presence and absence of electrostatic fields when operated in static and dynamic detection modes. In the static mode, we found that the sensors do not exhibit a measurable change in displacement due to added mass. On the other hand, bare sensors showed a clear change in displacement in response to isopropanol vapor. In the dynamic mode, functionalized sensors showed a measurable frequency shift due to the added mass of isopropanol vapor. In the presence of strong electrostatic fields, the measured frequency shift was found to be threefold larger than that in their absence in response to the same concentration of isopropanol vapor. The enhanced responsivity of dynamic detection allows the sensors to measure the vapor mass captured by the functional material, which is not the case for static detection. The detection of isopropanol by bare sensors in static mode shows that change in the medium permittivity is the primary sensing mechanism. The enhanced responsivity of dynamic mode sensors when operated in strong electrostatic fields shows that their sensing mechanism is a combination of a weaker added mass effect and a stronger permittivity effect. These findings show that electrostatic MEMS gas sensors are independent of the direction of the gravitational field and are, thus, robust to changes in alignment. It is erroneous to refer to them as ‘gravimetric’ sensors.

Electrostatic vehicle exhaust particle sensor for the evaluation of the diesel particulate filter (DPF)

A real-time electrostatic particle sensor has been developed to measure the concentration of soot in diesel exhaust. Based on the Venturi effect, the self-priming sampling probe of the sensor was designed to generate a pressure difference to drive particles into the detection region of the electrostatic trap. A detection module was developed for real-time measurement of the nanoamp current generated by charged soot within the probe. The microcurrent was amplified by the signal processing system of the sensor. To guarantee the precision of particles, it was essential to test the performance. The sensor was able to measure currents from −100 nA to −30 nA with a relative error of −0.01%. In addition, experiments comparing the sensor with a commercial instrument have shown that they correlate well. Furthermore, the standard deviation of the sensor was 0.001049 during a 36 h long-term test, which is lower than a commercial instrument with better stability. The sensor was investigated to demonstrate the detection of diesel particulate filter (DPF) failure. The results showed that the consistency between the sensor and the regulatory measurement equipment was good. The data ratios of the sensors installed at the front and rear of the DPF differed by many fold, which indicated that the sensor was able to determine the status of the exhaust system.

Design and analysis of novel microelectromechanical system based microgripper for manipulating microbiological species and micro objects

Design and analysis of a pair of novel microelectromechanical system (MEMS) microgrippers having single and multiple sets of jaws respectively, incorporated with displacement amplification mechanism and electrostatic force sensor is presented in this research work. The novelty of the proposed microgrippers is their unique and efficient designs, having the versatility of gripping ranges. The size of the microgripper is 3.2 × 3.7 mm2 in both cases and has one and three sets of jaws with gripping ranges of 30–64 µm, 175–200 µm, and 350–380 µm respectively. The three jaw sets are application-specific to the multicellular small organism, eukaryotic cells, and microcells, respectively. The microgrippers are being actuated by an optimized V-shaped thermal chevron actuator whose input is amplified by using a pantograph displacement amplification mechanism with an amplification factor of 54. The force applied to the gripping object is sensed by a rotary comb electrostatic force sensor. Special attention has been paid to the structural design, and detailed stress analysis has been discussed. This research work includes static, electrostatic, parametric, and electrothermal analysis carried out using Finite Element Method (FEM) techniques and analytical modeling. The results show that the operating voltage range of the devices is from 4 to 9.25 V, having a maximum operational temperature of 380 °C with a factor of safety of 1.026. The maximum capacitance change with the full operational range of microgrippers is 0.63 µf, having a capacitive sensitivity of 19.6 nf/µm. The microgrippers can be used as an instrument to grip not only the microbiological species but also can be beneficial to carry out manipulations at the micro-level like micro-assembly. The proposed microgripper has a substantial structural stability, has a high amplification factor and accommodates manipulation of a wide range of microorganism.

Improvement and optimization of coal dust concentration detection technology: Based on the 3σ criterion and the kalman filtering composite algorithm

Due to the inherent time delay in dust concentration data transmission, dust reduction equipment is unable to respond according to the current moment's dust concentration. Hence, the focus of mine dust concentration detection technology extends beyond the current dust concentration to accurately predicting the next moment's dust concentration, allowing a response window for dust reduction equipment, leading to more optimal dust reduction effects. This necessitates the introduction of the Kalman filtering algorithm. Despite the traditional single Kalman filtering algorithm's ability to make certain dust concentration predictions, it is constrained by high-temperature, high-humidity underground environments and electromagnetic pulse signal interference from fan and coal mining machine operations, with an error rate reaching up to 40 %, seriously disrupting underground dust reduction equipment. However, the composite Kalman filtering algorithm incorporating the 3σ rule effectively eliminates bad values and removes instability from the underground environment and noise, rendering it applicable to most complex environments, providing accurate predicted concentrations for dust reduction equipment, and boosting overall universality. This study designed a fusion algorithm based on the Pauta criterion and Kalman filter to further optimize the dust concentration detection technology based on electrostatic induction. The sensor's model and measuring principle were first analyzed, and the initial calibration and precision testing of the sensor using the experimental system were completed. The dust concentration fluctuation characteristics were then modeled with Matlab, and the noise variance was estimated to process and filter the dust concentration data with the Pauta criterion, Kalman filter, and fusion algorithm respectively. The accuracy of the dust concentration sensor was tested again after applying the fusion algorithm, which proved to significantly enhance the stability and accuracy of the electrostatic induction-based dust concentration sensor. This study has critical implications for coal mine dust concentration monitoring and coal miner occupational health protection.

A low-cost machine learning process for gait measurement using an electrostatic sensors network

Continuous in-house measurement of gait of elderly People is relevant for health professionals. To be adopted by most, the system must be low-cost and non-intrusive. In this paper we present a solution for measuring the walking velocity based on a network of 4 electric potential sensors. In our experiments, we also add PIR sensors used in our previous work for comparative purposes. A temporary Depth camera is used for training the model on walking velocity. The first results presented are obtained without machine learning. Then a machine learning regression method is tested to reduce the uncertainty of the sensors. The results show that the electric potential sensors are suitable for the in-house measurement of walking speed of elderly people. The uncertainty is lower than the target of 0.15 m s-1 known as the upper limit for detecting a reduction in speed due to illness. As for the PIR sensors, electric potential sensors consume very little energy, they are inexpensive, they can be embedded and hidden in the home which makes them less -intrusive and furthermore have better accuracy.

Charge pattern detection through electrostatic array sensing

As reported in previous work, electrostatic charge induced by contact potential difference (CPD) arising from oxidational wear can be detected by electrostatic button sensors. However, to detect signs of localised wear and to investigate the mechanisms involved, the charge distribution on worn regions needs to be measured. Therefore, this paper investigates the evolution of surface charge during wear processes, its interplay with friction, and the correlation between charge distribution and surface chemistry. An electrostatic bar sensor and an array sensor were initially calibrated using CPD patterns generated by dissimilar metal inserts in steel plates. After appropriate signal processing, the sensor outputs exhibited excellent agreement with the electric field strength modelled using COMSOL Multiphysics. Subsequently, the bar sensor was integrated into a reciprocating tribometer for in-situ detection of oxidational wear of steel-on-steel rubbing contacts, followed by ex-situ array sensing of worn regions. Positive picocoulomb-level surface charge during the evolution of oxidational wear were successfully detected by the bar sensor and were found to correlate to changes in friction coefficient. The array sensor effectively mapped the distribution of surface charge. EDS mapping suggested patchy formation of Fe3O4 layers over the worn areas, and these patches correlated to the surface charge map. Increased electrostatic charge levels were associated with higher concentrations of oxidational wear. Therefore, this paper evaluates the potential of electrostatic array sensors to spatially resolve surface charge patterns induced by surface chemistry transformations, which enables the monitoring of localised and smaller-scaled machinery component deterioration and provides additional information for machinery diagnosis.

Multisensor Integrated Platform Based on MEMS Charge Variation Sensing Technology for Biopotential Acquisition.

We propose a new methodology for long-term biopotential recording based on an MEMS multisensor integrated platform featuring a commercial electrostatic charge-transfer sensor. This family of sensors was originally intended for presence tracking in the automotive industry, so the existing setup was engineered for the acquisition of electrocardiograms, electroencephalograms, electrooculograms, and electromyography, designing a dedicated front-end and writing proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in a domestic environment will be discussed in detail. The excellent results indicate that this technology can provide a low-power, unexplored solution to biopotential acquisition. The technological breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, it opens up additional possibilities for wearable sensors and strengthens the role of MEMS technology in medical wearables for the long-term synchronous acquisition of a wide range of signals.

Analytical modeling and optimization of electrostatic particle sensors for particle number detection: incorporating particle size influence

Developing measurement devices and methods to track increasingly stringent emission standards, including particle number, is crucial. This paper presents a novel analytical model to describe the signal response of an electrostatic particle sensor not only to particle mass concentration, but also to the particle number concentration of in-flowing particles. The uniqueness of this model lies in its ability to calculate the signal as a function of particle diameter, enabling the determination of particle number concentration from the signal. The model considers the effects of aerosol flow, electrode voltage and temperature, and can be used for the optimization of the sensor geometry parameters, length, width, and electrode gap. The model was designed to optimize the sensor’s geometry and signal retrieval as well as the optimization of the electric field between the electrodes. Comparative analysis was conducted between the proposed model and a model from the literature as well as experimental data from literature and experimental data collected in this paper.