Electrostatic Sensor Research Articles

Concurrent Measurement of The Size Distribution and Velocity of Pneumatically Conveyed Biomass Solids Using Acoustic Emission and Electrostatic Sensors

Concurrent Measurement of The Size Distribution and Velocity of Pneumatically Conveyed Biomass Solids Using Acoustic Emission and Electrostatic Sensors

Optimization of Electrostatic Sensors for Rotational Speed Measurement of a Metallic Rotor

Optimization of Electrostatic Sensors for Rotational Speed Measurement of a Metallic Rotor

Multiphysics Coupling Modeling and Simulation of Electrostatic Sensors for Gas Path Monitoring

Multiphysics Coupling Modeling and Simulation of Electrostatic Sensors for Gas Path Monitoring

Increasing sensitivity of the electrostatic field mill sensor by determining its optimal configuration

The object of research is the process of measuring the strength of the electrostatic field for a low dynamic range (from 0 to 1 kV/m). This study is aimed at increasing the sensitivity of the sensor of the electrostatic field mill (EF) by determining its optimal geometric configuration, which will reduce the error of measuring the electrostatic field strength. To establish the actual value of the induced current, a computer model was built and simulation modeling of the EF sensor was carried out. On the basis of the constructed computer model, studies of the EF sensor were carried out to determine the numerical value of the induced current. As a result, it was established that the occurrence of edge effects leads to the appearance of methodological error, which occurs due to the fact that the average induced current is smaller compared to the calculated value. As a result of computer modeling of the EF sensor to determine the value of the optimal number of sectors, it was established that for the proposed design of the EF sensor, the optimal number of sectors is six. It was established that the optimal value of the distance between the sensitive plates and the shielded rotor should be in the range of 2.5–3 mm to ensure the maximum sensitivity of the EF sensor and its safe use. The determined optimal parameters of the EF geometric configuration will allow to form the necessary requirements for the construction of improved electrostatic field strength meters in a low dynamic range (from 0 to 1 kV/m). A promising direction of application of such devices in production will be the development of an additional system for monitoring the strength of the electrostatic field, which will allow to prevent the occurrence of a dangerous situation.

An optimization method of electrostatic sensor array based on Kriging surrogate model and improved non-dominated sorting genetic algorithm with elite strategy algorithm

Array-type electrostatic monitoring is gradually becoming an effective tool for aero-engine fault diagnosis. In this paper, we innovatively apply the surrogate optimization method to the optimization of the sensor array structure in order to meet the need of improving the particle information recognition capability of the electrostatic sensor array (ESA). A structure optimization method of ESA based on the Kriging surrogate model and improved NSGA-II algorithm is proposed. In this paper, a finite element simulation model of ESA is established, and the array optimization problem is abstracted as the solution of a mixed-integer optimization problem. This paper reduces the large-scale numerical simulations in the full-variable space with the help of the Kriging surrogate model. In addition, an improved NSGA-II algorithm for mixed-integer optimization is proposed. The simulation experiment verified that the average absolute error of the sensor before and after optimization for the identification of particle position and charge quantity was reduced by 69.88% and 49.68%, respectively. The array structure optimization method proposed in this paper facilitates the acceleration of the design process of electrostatic sensors and provides a scientific design method for their specific design for airborne application.

A D-norm cross-correlation method for particle velocity measurement through electrostatic sensing

The cross-correlation (CC) method is conventionally used for measuring the velocity of gas–solid flows using electrostatic sensors, wherein velocity measurement is transformed into a time-delay estimation problem. However, owing to the intricacies of signals in gas–solid flows, the cross-correlation function may exhibit multiple peaks, resulting in peak misjudgment and time-delay estimation bias. To address these issues, this paper proposes a D-norm cross-correlation (DCC) velocity measurement method and applies it to flow velocity measurements in pneumatic conveying. Such a technique leverages the sensitivity of D-norm to pulses, highlighting the pulse characteristics of measurement signals. This approach increases the accuracy of time-delay estimation for upstream and downstream signals, mitigating the issues caused by multi-peaks and fake peaks in the cross-correlation function. Compared to conventional CC, the DCC can eliminate the problem of multiple and fake peaks in cross-correlation functions more effectively, thereby increasing the reliability of velocity measurements.

A wear debris recognition method based on dual-channel electrostatic signal and prominence of cross-correlation function

Electrostatic monitoring is a hot technique used for online wear debris detection. This study presented a recognition method by using a dual-channel electrostatic senor. A signal enhancement model based on an improved Variational Mode Decomposition (VMD) by double screening intrinsic mode function was proposed. Wear debris recognition algorithm and its processes were researched by using the dual-channel signals and the prominence of cross-correlation value. Experiments were conducted on a gear reducer bench, and the results of wear debris recognition between the electrostatic sensor and the inductive sensor were compared. The results show that the suggested algorithm can efficiently recognize signal pulses caused by wear debris, offering an effective analytical approach for early identification of wear status.

An analytical model of the detecting structure of electrostatic inductive electric field sensor

To analyze and design the detecting structure of electrostatic inductive electric field sensors, it is crucial to determine the capacitance. This study proposes a model for the theoretical analysis of the capacitance of the detecting structure of the electrostatic inductive electric field sensor. The aim is to achieve high calculation accuracy and deepen the fundamental understanding of high performance. Using Schwarz–Christoffel mapping, a reasonably simplified and approximative solution program is developed. The precision is comparable to that of a finite element solver. The calculation results exhibit good agreement with the simulation results from finite element analysis when various parameter ratios are used. Notably, the calculation results of the analytical model are verified by the measured data of a realistic electrostatic sensor. Using the analytical model, a parametric study of the detecting structure is conducted, leading to the determination of the final optimized, technically achievable device structures.

Vibrating capacitive electrostatic potential sensor based on a double cantilever tuning fork.

In this work, a vibrating capacitive sensor utilizing dual cantilever tuning fork resonance for electrostatic potential modulation was designed and fabricated. Initially, an electrostatic induction model was established, and the principle of non-contact electrostatic potential measurement was analyzed to identify the key factors influencing the sensor's performance. Subsequently, the vibration mode of the tuning fork was simulated to determine the mechanical dimensions of the tuning fork and the driving frequency of the piezoelectric crystal. Additionally, the concept of enhancing the uniformity of the electric field near the sensor by employing a shield plate was proposed. Simultaneously, the optimal mechanical parameters of the shielding plate were determined through multi-physical field simulation to improve the sensor's linearity. Finally, a compact calibration device was designed to measure the key performance parameters of the non-contact electrostatic potential sensor. The results demonstrate that the sensor has a measurement range of -10 to 10kV, a measurement accuracy better than ±3%, and a linearity of 0.46%. This work offers an alternative solution for non-contact potential measurement.

A Systematic Approach for Inertial Sensor Calibration of Gravity Recovery Satellites and Its Application to Taiji-1 Mission

High-precision inertial sensors or accelerometers can provide references for free-falling motion in gravitational fields in space. They serve as the key payloads for gravity recovery missions such as CHAMP, the GRACE-type missions, and the planned Next-Generation Gravity Missions. In this work, a systematic method for electrostatic inertial sensor calibration of gravity recovery satellites is suggested, which is applied to and verified with the Taiji-1 mission. With this method, the complete operating parameters including the scale factors, the center of mass offset vector, and the intrinsic biased acceleration can be precisely calibrated with only two sets of short-term in-orbit experiments. This could reduce the gaps in data that are caused by necessary in-orbit calibrations during the lifetime of related missions. Taiji-1 is the first technology-demonstration satellite of the “Taiji Program in Space”, which, in its final extended phase in 2022, could be viewed as operating in the mode of a high–low satellite-to-satellite tracking gravity mission. Based on the principles of calibration, swing maneuvers with time spans of approximately 200 s and rolling maneuvers for 19 days were conducted by Taiji-1 in 2022. Given the data of the actuation voltages of the inertial sensor, satellite attitude variations, precision orbit determinations, the inertial sensor’s operating parameters are precisely re-calibrated with Kalman filters and are relayed to the Taiji-1 science team. The relative errors of the calibrations are <1% for the linear scale factors, <3% for center of mass, and <0.1% for biased accelerations. Data from one of the sensitive axes are re-processed with the updated operating parameters, and the resulting performance is found to be slightly improved over the former results. This approach could be of high reference value for the accelerometer or inertial sensor calibrations of the GFO, the Chinese GRACE-type mission, and the Next-Generation Gravity Missions. This could also create some insight into the in-orbit calibrations of the ultra-precision inertial sensors for future GW space antennas because of the technological inheritance between these two generations of inertial sensors.

A Highly Sensitive and High-Resolution Resonant MEMS Electrostatic Field Microsensor Based on Electrostatic Stiffness Perturbation.

This paper proposes a highly sensitive and high-resolution resonant MEMS electrostatic field sensor based on electrostatic stiffness perturbation, which uses resonant frequency as an output signal to eliminate the feedthrough interference from the driving voltage. The sensor is composed of a resonator, driving electrode, detection electrode, transition electrode, and electrostatic field sensing plate. The working principle is that when there is an electrostatic field, an induction charge will appear at the surface of the electrostatic field sensing plate and induce electrostatic stiffness on the resonator, which will cause a resonant frequency shift. The resonant frequency is used as the output signal of the microsensor. The characteristics of the electrostatic field sensor are analyzed with a theoretical model and verified by finite element simulation. A device prototype is fabricated based on the Silicon on Insulator (SOI) process and tested under vacuum conditions. The results indicate that the sensitivity of the sensor is 0.1384Hz/(kV/m) and the resolution is better than 10 V/m.

Application of nonlinear dynamics analysis to gas-solid flow system in horizontal pneumatic conveying of plastic pellets

Chaotic invariant and recurrence quantification analysis measures have characterised fully developed gas-solid flow in horizontal pneumatic conveying of plastic pellets. These measures describe the complexity in phase spaces (attractors) and recurrence plots, reconstructed from pressure and bottom arc-shaped electrostatic signals to characterise the behaviour of flow patterns. Different flow patterns were identified using high-speed video imaging of a transparent pipeline and classified at several operating conditions in a flow pattern map and state diagram. Recurrence plots were analysed for the identified flow patterns, which showed different qualitative structures. The chaotic invariant and recurrence quantification analysis measures were correlated with the state diagram, indicating that the fluctuations of pressure senor and electrostatic sensor signals can classify the flow patterns at different operating conditions. Combining the analysis measures for electrostatic signals can indicate whether the flow condition is above, near or below the minimum energy consumption operating conditions.

Electrostatic Acoustic Sensor with an Impedance-Matched Diaphragm Characterized for Body Sound Monitoring.

Acoustic sensors are able to capture more incident energy if their acoustic impedance closely matches the acoustic impedance of the medium being probed, such as skin or wood. Controlling the acoustic impedance of polymers can be achieved by selecting materials with appropriate densities and stiffnesses as well as adding ceramic nanoparticles. This study follows a statistical methodology to examine the impact of polymer type and nanoparticle addition on the fabrication of acoustic sensors with desired acoustic impedances in the range of 1-2.2 MRayls. The proposed method using a design of experiments approach measures sensors with diaphragms of varying impedances when excited with acoustic vibrations traveling through wood, gelatin, and plastic. The sensor diaphragm is subsequently optimized for body sound monitoring, and the sensor's improved body sound coherence and airborne noise rejection are evaluated on an acoustic phantom in simulated noise environments and compared to electronic stethoscopes with onboard noise cancellation. The impedance-matched sensor demonstrates high sensitivity to body sounds, low sensitivity to airborne sound, a frequency response comparable to two state-of-the-art electronic stethoscopes, and the ability to capture lung and heart sounds from a real subject. Due to its small size, use of flexible materials, and rejection of airborne noise, the sensor provides an improved solution for wearable body sound monitoring, as well as sensing from other mediums with acoustic impedances in the range of 1-2.2 MRayls, such as water and wood.

Ring-Rod Detection Array for Gas–Solid Flow Pattern Based on Electrostatic Induction Principle

The sensitivity matrix of a ring electrostatic sensor array for gas-solid flow pattern detection presents a significant decrease and poor uniformity at the center of the pipe. To improve the uniformity of the sensitivity matrix, a ring-rod electrostatic array sensor is proposed. Firstly, the structure of the ring-rod array sensor is proposed, with the influence of the number of ring-rod electrodes in the array on the average and uniformity error of sensitivity being analyzed. Secondly, the ring-rod array and ring array in gas-solid flow detection were both simulated, and the influencing factors on their performances were studied. Finally, an experimental platform was setup to evaluate their performance for detecting gas-solid flow patterns under different conditions. Results show that the sensitivity matrix of the ring-rod array electrostatic sensor is more uniform, with its sensitivity error being small, and it shows better potential for detecting the gas-solid flow patterns.

Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on the Noise2Noise Method.

Onboard electrostatic suspension inertial sensors are important applications for gravity satellites and space gravitational-wave detection missions, and it is important to suppress noise in the measurement signal. Due to the complex coupling between the working space environment and the satellite platform, the process of noise generation is extremely complex, and traditional noise modeling and subtraction methods have certain limitations. With the development of deep learning, applying it to high-precision inertial sensors to improve the signal-to-noise ratio is a practically meaningful task. Since there is a single noise sample and unknown true value in the measured data in orbit, odd-even sub-samplers and periodic sub-samplers are designed to process general signals and periodic signals, and adds reconstruction layers consisting of fully connected layers to the model. Experimental analysis and comparison are conducted based on simulation data, GRACE-FO acceleration data, and Taiji-1 acceleration data. The results show that the deep learning method is superior to traditional data smoothing processing solutions.

A passive optical fibre sensor based on Fabry‐Perot interferometry for bipolar electrostatic monitoring of insulating dielectrics

AbstractThe measurement of the surface charge on the insulating dielectrics is critical to estimate the insulation strength of electrical equipment in operation. Unavoidable interactions with the electromagnetic field, however, generally result in distortion measurement of active sensor. Thus, it is of great significance to investigate a passive sensor for monitoring the deposited charge. Here, a passive optical fibre electrostatic sensor is developed on the principle of Fabry–Perot interference to quantify the surface potential of the insulating dielectrics. The sensitivity of quantifying the electrostatic charge is explored in terms of the wavelength shift in the interference spectrum, and the linear correlation is 0.99. Meanwhile, the sensor can identify a negative quadratic trend with the increase of the surface roughness of the insulating dielectrics. The merits of the proposed sensor such as passive monitoring, bipolar electrostatic detection, and the excellent ability of anti‐electromagnetic interference are highlighted in the results. Hence, this sensor can serve as an effective tool to monitor the electrostatic charge in the fields of smart electrical equipment, fuel transportation, aerospace, integrate circuit etc.

Simulation of the measurement and control system of the electrostatic suspension inertial sensor

The electrostatic levitation inertial sensor has a very wide range of applications in the fields of navigation, orientation, motion vector control, and basic physics experiments. This paper takes the sensing probe of the electrostatic suspension inertial sensor as the research object, the motion state of the mass block inside the sensing probe and the electrostatic force it is subjected to are analyzed in detail, and deduce the coefficients of signal transmission and conversion between each module, The mechanical model of the sensing probe is established in ADAMS, and the mechanical model is added to the Simulink control module as the controlled object, Then, the co-simulation analysis of the measurement and control system is carried out. The results show that when the system reaches stability, the steady-state error is7.044×10-14 m. The system has good stability and can meet the requirements of measurement accuracy.

Noise reduction by using wavelet transform in charge signal acquired from an electrostatic inductive sensor

Noise reduction by using wavelet transform in charge signal acquired from an electrostatic inductive sensor

A Study on the Influence of Electrical Discharges on the Formation of White Etching Cracks in Oil-Lubricated Rolling Contacts and Their Detection Using Electrostatic Sensing Technique

In bearing applications, the presence of stray and parasitic currents in combination with lubricants has been studied for almost a century and has been found to cause fluting and corrugation damages under high current densities. However, recent research has suggested that at low current densities (<1 mA/mm2) under specific operating conditions, electrical discharges can substantially reduce bearing life due to the formation of white etching cracks (WECs). To date, limited studies have investigated the critical operating and electrical conditions for WEC formation and demonstrated effective fault detection techniques. This study uses a novel monitoring technique known as the electrostatic sensing technique to detect, monitor and characterise electrical discharges in an oil-lubricated steel–steel rolling contact on a TE74 twin-roller machine. The findings demonstrate that WECs can be formed under the influence of electrical discharges in less than 50 h, and the electrostatic sensors are effective for the early detection of critical electrical discharges related to WEC-induced failures.

Development of a Coal Dust Concentration Sensor Based on the Electrostatic Induction Method.

In order to solve the problems of easy blockage and difficult maintenance of the current coal dust concentration sensor, a coal dust concentration sensor based on the electrostatic induction method was designed. Based on the analysis of the principle of electrostatic induction dust concentration detection, an electrostatic induction dust concentration sensor composed of a electrostatic detection electrode, a dust extraction fan, an induction signal processing circuit, an insulator, a shield, and other parts was designed. The influence of the length and width of the electrostatic detection electrode and the particle flow rate on the standard deviation of the induction signal was analyzed through experiments to optimize them. The induction signals on the electrostatic detection electrode at different dust concentrations were determined in tests, and the mathematical relationship between the standard deviation of the induction signal and the dust concentration was obtained. According to segment multiple-curve height fitting, the maximum deviation between the detected value and actual dust concentration does not exceed 10%.