Showing papers on "Inductive sensor published in 2023"
TL;DR: In this paper , the authors investigated the characteristics of impedance signals induced by debris at different positions in planar coil under different frequencies, debris sizes and velocities, and they demonstrated that the double peaks generated in the induced signals can be eliminated without sacrificing the sensitivity by placing the sensor channel at the coil inner wall, and the sensitivity can be improved by observing resistance signals, increasing the frequencies and reducing debris velocity.
Abstract: The uneven radial magnetic field distribution in the planar coil will lead to debris detection errors, especially for inductive debris sensors with high throughput and sensitivity, so the effect of debris position on the detection sensitivity should be addressed. In this study, based on spatial magnetic field analysis of planar coil, the characteristics of impedance signals induced by debris at different positions in planar coil were investigated under different frequencies, debris sizes and velocities. The experimental results demonstrated that the double peaks generated in the induced signals can be eliminated without sacrificing the sensitivity by placing the sensor channel at the coil inner wall, and the sensitivity can be improved by observing resistance signals, increasing the frequencies and reducing debris velocities.
2 citations
TL;DR: In this paper , a mathematical model of an ideal filamentary coil was employed to determine equivalent parameters of the sensor and sensitivity coefficients of tested physical quantities, which were determined on the basis of the measured value of the real sensor's impedance.
Abstract: This paper presents a practical way of using the method of evaluating the metrological properties of eddy current sensors. The idea of the proposed approach consists of employing a mathematical model of an ideal filamentary coil to determine equivalent parameters of the sensor and sensitivity coefficients of tested physical quantities. These parameters were determined on the basis of the measured value of the real sensor's impedance. The measurements were carried out with an air-core sensor and an I-core sensor while they were positioned at different distances from the surface of tested copper and bronze plates. An analysis of the influence of the coil's position in relation to the I core on the equivalent parameters was also carried out, and the interpretation of the results obtained for various sensor configurations was presented in a graphical form. When equivalent parameters and sensitivity coefficients of examined physical quantities are known, it is possible to compare even very different sensors with the employment of one measure. The proposed approach makes it possible to make a significant simplification of the mechanisms of calibration of conductometers and defectoscopes, computer simulation of eddy current tests, creating the scale of a measuring device, and designing sensors.
1 citations
TL;DR: In this paper , an inductive debris sensor for the online monitoring of debris particles in lubricants has received more attention from researchers to achieve a high-precision, high-efficiency sensor for accurate prediction on the degree of wear, the equivalent circuit model of the sensor coil has been established, and its equations discover the relationship between the induced voltage and excitation frequency.
Abstract: Lubricants have the ability to reduce frictions, prevent wear, convey metal debris particles and increase the efficiency of heat transfer; therefore, they have been widely used in mechanical systems. To assess the safety and reliability of the machine under operational conditions, the development of inductive debris sensors for the online monitoring of debris particles in lubricants has received more attention from researchers. To achieve a high-precision, high-efficiency sensor for accurate prediction on the degree of wear, the equivalent circuit model of the sensor coil has been established, and its equations discovering the relationship between the induced voltage and excitation frequency have been derived. Furthermore, the influence of excitation frequencies and metal debris on the magnetic flux density has been analyzed throughout the simulations to determine the sensor magnetic field. In order to identify a frequency range suitable for detecting both ferrous and non-ferrous materials with a high level of sensitivity, the analytical analysis and experiments have been conducted to investigate the frequency characteristics of the developed inductive debris sensor prototype and its improved inspection capability. Moreover, the developed inductive debris sensor with the noticeable frequency characteristics has been assessed and its theoretical model has been also validated throughout experimental tests. Results have shown that the detection sensitivity of non-ferrous debris by the developed sensor increases with the excitation frequency in the range of 50 kHz to 250 kHz, while more complex results for the detection of ferrous debris have been observed. The detection sensitivity decreases as the excitation frequency increases from 50 kHz to 300 kHz, and then increases with the excitation frequency from 300 kHz to 370 kHz. This leads to the effective selection of the excitation frequency in the process of inspection. In summary, the investigation into the frequency characteristics of the proposed novel inductive debris sensor has enabled its broad applications and also provided a theoretical basis and valuable insights into the development of inductive debris sensors with improved detection sensitivity.
1 citations
TL;DR: In this article , an overview on inductive sensors for measuring wear debris in lubrication is introduced and the significance of analyzing the wear debris generated during the operation of the machine is explained and the working principle of the inductive wear sensors is illustrated.
Abstract: Almost all of the wear debris generated during the operation of the machine is suspended in the circulating lubricating oil. The analysis of the wear debris in the lubricating oil can effectively monitor the wear state of the machine and provide early warning of failures. An overview on inductive sensors for measuring wear debris in lubrication is introduced. To begin with, the significance of analyzing the wear debris in lubricating oil is explained and the working principle of the inductive wear sensors is illustrated. Furthermore, the development of inductive wear sensors and the key limitations are summarized. Finally, some rest factors affecting the sensor and the processing method of the induction signal aliasing are discussed, and the future development trend is prospected. It is pointed out that developing high sensitivity wear debris inductive sensors, increasing sensor throughput, and solving the problem of aliasing of detection signals are the following issues that should be further studied in the future.
TL;DR: In this paper , a passive wireless strain sensing system with magnetic resonance coupling and bulk acoustic wave (BAW) strain sensor is proposed to evaluate the structural safety of megastructures.
Abstract: In this article, we propose a passive wireless strain sensing system with magnetic resonance coupling and bulk acoustic wave (BAW) strain sensor to evaluate the structural safety of megastructures. The BAW sensor provides a high quality factor, and the magnetic resonance coupling enhances the transmitter’s gain at the system’s operational frequency. The experimental and analytical methods are investigated to characterize the effect of different parameters on this system. The results show that the sensing system can detect the resonant frequency of the receiver when the coupling coefficient is less than 0.01. Experimental demonstrations show that a prototype sensing system had a strain measurement range of $500\,\mu \varepsilon $ and a resolution of 4 Hz/ $\mu \varepsilon $ when the separation distance between coils was 10 cm. Compared with conventional passive wireless sensing systems, the change of coupling coefficient has little effect on the resonant frequency of the receiver. We envision that this system has the potential to realize noncontact strain measurement, while the measurement device is in an unsteady movement mode. In the future, this sensing system can be integrated into an unmanned aerial vehicle (UAV) to measure the strain in megastructures.
TL;DR: In this article , an inductive sensor based on differential detection and its signal conditioning circuit is presented, which can detect metal wear debris in the oil, and the sensor adopts the structure of two induction coils embedded in one excitation coil.
Abstract: Wear debris in the oil contains a wealth of information about the friction pairs of the mechanical equipment. By analyzing the size and type of wear debris through oil detection technology, condition monitoring and fault diagnosis of mechanical systems can be realized. This paper presents an inductive sensor based on differential detection and its signal conditioning circuit, which can detect metal wear debris in the oil. The sensor adopts the structure of two induction coils embedded in one excitation coil. The differential signal is obtained by reverse connecting two induction coils with the same parameters, which can suppress the common mode interference and eliminate the influence of ambient noise so that the sensor has extremely low noise. Through the designed signal conditioning circuit, the detection signal is phase-sensitive detected, and the information of wear debris is extracted by amplification and filtering. In this paper, the sensing principle of the sensor is derived, the spacing between the two induction coils is optimized using finite element simulation, and the optimal excitation frequency, detection limit, and detection error of the sensor are investigated through experiments. The experiment results show that the sensor can detect 20 μm iron particles and 130 μm copper particles in a 2 mm flow channel, and the detection error of the sensor is less than 22%. The sensor has the advantages of simple structure and high sensitivity and can be applied to detect metal wear debris in hydraulic oil.
TL;DR: In this article , the authors presented a bulk acoustic wave (BAW) passive wireless strain sensor, which consists of two coils and a BAW sensor, and the force sensitive element is a quartz wafer with a high quality factor, which is embedded into the sensor housing.
Abstract: Near-field passive wireless sensors can realize non-contact strain measurement, so these sensors have extensive applications in structural health monitoring. However, these sensors suffer from low stability and short wireless sensing distance. This paper presents a bulk acoustic wave (BAW) passive wireless strain sensor, which consists of two coils and a BAW sensor. The force-sensitive element is a quartz wafer with a high quality factor, which is embedded into the sensor housing, so the sensor can convert the strain of the measured surface into the shift of resonant frequency. A double-mass-spring-damper model is developed to analyze the interaction between the quartz and the sensor housing. A lumped parameter model is established to investigate the influence of the contact force on the sensor signal. Experiments show that a prototype BAW passive wireless sensor has a sensitivity of 4 Hz/με when the wireless sensing distance is 10 cm. The resonant frequency of the sensor is almost independent of the coupling coefficient, which indicates that the sensor can reduce the measurement error caused by misalignment or relative movement between coils. Thanks to the high stability and modest sensing distance, this sensor may be compatible with a UAV-based monitoring platform for the strain monitoring of large buildings.
TL;DR: In this paper , a four-coil structure-based inductive debris sensor is theoretically introduced and the optimal coil length, the turns of the excitation coil, and the turn ratio are obtained through simulation to improve the sensitivity of the inductive sensor.
Abstract: The online monitoring of debris in oil can timely and accurately reflect the wear status of mechanical equipment. The inductive debris sensor based on the electromagnetic induction can effectively detect various parameters of debris. The four-coil structure-based of the sensor is theoretically introduced. The optimal coil length, the turns of the excitation coil, and the turn ratio are obtained through simulation to improve the sensitivity of the inductive debris sensor. The effects of the excitation frequency and the movement velocity, size, position, and material of the debris on the sensor output are investigated. The sensing signal recovered by coherent demodulation confirms the sensor’s effectiveness in identifying metal debris. The experiment realized the identification of iron debris with a diameter of $93.5 \mu \text{m}$ and copper debris with a diameter of $447.3 \mu \text{m}$ under the 3 mm pipe radius, providing a reference for the design of four-coil inductive debris sensor.
TL;DR: In this article , a variable inductive sensing system using an SMA spring for high sensitivity measurements, performance of which greatly relies on its coil geometry and permeability, was presented, enabling the sensing operation at an isothermal condition for testing force, displacement, and as a proximity sensor.
TL;DR: In this article , an inductive force sensor composed of a ceramic-based magnetic core and a polymer-based helical spring was fabricated using various additive manufacturing technologies to ensure reasonable force-sensing performance.
Abstract: Additive manufacturing (AM) is an innovative technology that overcomes the technical limitations of traditional ceramic manufacturing methods. AM offers unprecedented opportunities for the development of ceramic-based electronics with complex 3D structures, which can be imparted with electronic functionalities. In this study, inductive force sensors composed of a ceramic-based magnetic core and polymer-based helical spring were fabricated using various AM technologies. To ensure reasonable force-sensing performance, the response to the external force was controlled by controlling the spring constant of the helical spring and measured according to the change in inductance of the magnetic sensing circuit implemented in the sensor. The fully additive manufactured (AMed) inductive force sensors exhibited outstanding sensing performance: they achieved a sensitivity of 0.139 μH/N with good repeatability, high linearity (99.01%), negligible hysteresis (less than 0.01 μH), and robust cyclic endurance. The sensor performance was further validated by building and testing a robotic gripper that employed an AMed inductive force sensor. This study offers an innovative way to manufacture a variety of sensors with complex shapes that can fit into dummy spaces allowed by the design of electronic devices, which will contribute to the effective integration of various electronic components in future electronic systems, such as robots.
TL;DR: In this article , a triple-coil inductive debris sensor combined with a digital lock-in amplifier (DLIA) module was proposed to detect and distinguish multiple contaminants in hydraulic oil so as to achieve high sensitivity and high detectability.
Abstract: Wear debris monitoring in lubrication oil has emerged as a promising solution for health monitoring and fault diagnosis of mechanical equipment. However, the debris feature signal is weak and overwhelmed in the noise. Furthermore, the traditional inductive detection sensors suffer from poor detectability to distinguish non-ferromagnetic metal particles. To address these problems, in this paper, a triple-coil inductive debris sensor combined with a digital lock-in amplifier (DLIA) module was proposed to detect and distinguish multiple contaminants in hydraulic oil so as to achieve high sensitivity and high detectability. First, we introduced the sensor design and fabrication. Second, the triple-coil inductive debris sensor model, the DLIA principle, and the performance analysis of suppressing noise for lock-in amplifier were provided, respectively. Finally, both simulation and experimental platforms were built to substantiate the effectiveness of the proposed approach. Experimental results showed that the detection limit of the designed sensor for iron, copper, and aluminium particles could be inferred as 85.4 μm, 102.4 μm, and 111.6 μm, respectively.
02 Mar 2023
TL;DR: In this article , the authors focus on analysis of inductive and optical displacement sensors and their use in industrial applications, and investigate their performances and compare them in this regard with inductive sensors.
Abstract: The paper focuses on analysis of inductive and optical displacement sensors and their use in industrial applications. Differential inductive displacement sensors, usually in the form of linear variable differential transformer LVDT, are widely used in hydraulic applications because they have excellent accuracy, robust construction and are able to measure large displacements (in hundreds of millimeters range). However, they are large, heavy and offer slow measurements. For fast measurements, in millisecond range, optical sensors like time-of-flight ranging sensor could be a better choice, and present paper investigates their performances and compares them in this regard with inductive sensors.
01 Jan 2023
TL;DR: In this paper , an industrial proximity sensor is interfaced to the NI DAQ which is used as a measurement device and interfacing device between the PC without any additional microcontroller or programmable logic controller.
Abstract: This paper presents a method to measure DC motor speed using an industrial-rated proximity sensor and NI DAQ card. The industrial proximity sensor captures the positive edge of a wave when a metallic portion on the motor’s wheel segment has occurred. The industrial proximity sensor is interfaced to the NI DAQ which is used as a measurement device and interfacing device between the PC without any additional microcontroller or programmable logic controller. The data is processed by the MATLAB-based NI MAX toolbox. MATLAB programming is used to calculate the speed of a DC motor. The speed is recorded using the conventional setup and compared with the proposed DAQ-based setup.
TL;DR: In this article , a novel eddy current linear sensor for displacement measurement with high accuracy and long range is proposed, which consists of a stationary component with metal reflection conductors and a movable component with spiral coils.
Abstract: To enlarge the measurement range of traditional eddy current displacement sensors, a novel eddy current linear sensor for displacement measurement with high accuracy and long range is proposed in this paper. The sensor consists of a stationary component with metal reflection conductors and a movable component with spiral coils. The metal reflection conductors below the movable component will generate an eddy current and radiate an eddy current magnetic field after the high-frequency alternating excitation signal is applied to field spiral coils. The inductive electromotive forces (EMFs) will be generated in inductive spiral coils. The measurement of displacement relies on the inductive EMFs variation of inductive spiral coils caused by the change of coupling areas between the spiral coils and the metal reflection conductors. The absolute positioning is realized by the combination of a coarse measurement channel and a fine measurement channel. The structure and measurement principle are described in detail. A sensor prototype is fabricated by PCB technology, and the experiments are carried out. Based on the experimental results, some error sources are analyzed and the sensor is optimized. The experimental results show that the sensor can achieve absolute positioning, and the worst-case error is 6.75 μm within the measurement range of 500 mm.
TL;DR: In this paper , the authors proposed a new criterion for evaluating the performance of an inductive oil debris sensor: spatial resolution, referred to the allowable distance between two particles when the sensor can accurately distinguish the particle signals.
Abstract: Inductive debris sensor is becoming increasingly important in online oil debris detection. This work proposes a new criterion for evaluating the performance of an inductive oil debris sensor: spatial resolution. When multiple metal particles in lubricating oil simultaneously pass through the sensor, it is vital that clear signals responding to each particle are produced. The spatial resolution is referred to the allowable distance between two particles when the sensor can accurately distinguish the particle signals. Theoretical analysis shows that spatial resolution has a relationship with the output signal of a single particle. A COMSOL model is established to simulate a coil output when two adjacent particles pass, and the output signals change with the distance between two particles, the particle size and the coil structures. Results show that the relevant factors affecting the spatial resolution, and a small coil radius can help improve the sensor’s accuracy of debris identification.
24 Apr 2023
TL;DR: In this paper , the authors investigated the possibility of combining actuation and sensing via inductive detection of model microrobots under field magnitudes ranging from 0.5 mT to 10s of mT rotating at 1 Hz to 100 Hz.
Abstract: The engineering space for magnetically manipulated biomedical microrobots is rapidly expanding. This includes synthetic, bioinspired, and biohybrid designs, some of which may eventually assume clinical roles aiding drug delivery or performing other therapeutic functions. Actuating these microrobots with rotating magnetic fields (RMFs) and the magnetic torques they exert offers the advantages of efficient mechanical energy transfer and scalable instrumentation. Nevertheless, closed-loop control still requires a complementary noninvasive imaging modality to reveal position and trajectory, such as ultrasound or x-rays, increasing complexity and posing a barrier to use. Here, we investigate the possibility of combining actuation and sensing via inductive detection of model microrobots under field magnitudes ranging from 0.5 mT to 10s of mT rotating at 1 Hz to 100 Hz. A prototype apparatus accomplishes this using adjustment mechanisms for both phase and amplitude to finely balance sense and compensation coils, suppressing the background signal of the driving RMF by 90 dB. Rather than relying on frequency decomposition to analyze signals, we show that, for rotational actuation, phase decomposition is more appropriate. We demonstrate inductive detection of a micromagnet placed in distinct viscous environments using RMFs with fixed and time-varying frequencies. Finally, we show how magnetostatic gating fields can spatially isolate inductive signals from a micromagnet actuated by an RMF, with the resolution set by the relative magnitude of the gating field and the RMF. The concepts developed here lay a foundation for future closed-loop control schemes for magnetic microrobots based on simultaneous inductive sensing and actuation.
TL;DR: In this article , the authors used proximity sensors such as inductive, and capacitive sensors, and with the help of motors the machine can automatically segregate the papers, plastics, and metals into their correct bins.
Abstract: Improper disposal of waste is a rampant problem that contributes to pollution. The objective of the study is to create an automatic waste segregator bin to be used by the households utilizing proximity sensors. Papers, plastics, and metals are the common wastes at home. Using proximity sensors such as inductive, and capacitive sensors, and with the help of motors the machine can automatically segregate the papers, plastics, and metals into their correct bins. The researchers created the whole setup of the motors and sensors using wood, pipe, screws, and nails to be able to create a functional segregator machine. Proving its effectiveness, testing procedures were conducted in detecting and segregating papers, plastics, and metals, then each was tested based on its maximum weight capacity, the detection time, and lastly, the success rate of being able to detect different types of wastes were tested. The results showed that the segregator has a success rate to detect 100% of metals accurately, 100% of papers, and 100% of plastics. The segregator bin has a maximum weight of 0.6 kg and an average detection time of 1.73 seconds. Through the different testing procedures, the researchers found that it is feasible to create an automated segregator bin using proximity sensors which is also a smart recycling bin as it was able to accurately detect papers, plastics, and metals. Although the researchers were able to attain the desired results, future researchers are still recommended to modify, innovate, and improve the automated waste segregator bin.
TL;DR: In this article , a three-coil microfluidic lubricating oil detection sensor is designed by combining microfluidity principle with three-cil inductive detection method, which adopts a microchannel design to reduce the position error, improve the detection accuracy, and reduce the occurrence of detection errors caused by multiple particles passing through at the same time.
Abstract: In this article, a three-coil microfluidic lubricating oil detection sensor is designed by combining microfluidic principle with three-coil inductive detection method. The sensor adopts a microchannel design to reduce the position error, improve the detection accuracy of a single particle, and reduce the occurrence of detection errors caused by multiple particles passing through at the same time. The inductive electric potential of the induction coil was obtained by a simulation analysis and experimental verification methods. It is found that the influence of particles on the internal magnetic field of the micro coil is relatively enhanced when the micro coil is used as excitation and induction coil. This results in two peaks and valleys shown from the generated inductive electric potential, increasing of the signal’s characteristic point position, and improvement in signal recognition. Therefore, it will provide more information for the subsequent material identification and multiparticle mixing signal differentiation. Meanwhile, a signal processing circuit with the function of low-pass filtering and amplification is designed to suppress the noise value of the output signal to around 1.25 mV, which could improve the stability of the system. A series of lubricating oil abrasive particle detection tests are conducted, and the results show that the new detection sensor is able to detect iron particles below 64 $\mu \text{m}$ and copper particles below 125 $\mu \text{m}$ . The abrasive particle information collected by this sensor can provide more precise wear status information for the health assessment and fault diagnosis of mechanical systems, which is important for the health monitoring of mechanical systems.
TL;DR: In this paper , an advanced tool that uses electromagnetic resonance coupling and machine learning techniques to detect and characterize metal loss on the inner surface of a metallic pipe is described, which can fully characterize the size, location, and level (thickness) of metal loss.
Abstract: This paper describes an advanced tool that uses electromagnetic resonance coupling and machine learning techniques to detect and characterize metal loss on the inner surface of a metallic pipe. The proposed tool uses a transmitter coil placed along the axis of the pipe and four sensor coils installed around the transmitter coil. Any defect on the pipe surface leads to changes in the impedance of the transmitter and sensor coils as well as in the mutual coupling between them, thus creating a detectable variation in the outputs of one or multiple sensor coils. An artificial neural network is developed to reconstruct two-dimensional pipe cross sections and to completely characterize the defects using these variations. The proposed tool is tested and validated via simulations and data collected using an experimental prototype. Results show that the tool can fully characterize the size, location (azimuthal angle), and level (thickness) of metal loss.
TL;DR: In this paper , the simulation optimization structure design was carried out based on the traditional three-coil inductance wear particle sensor, and the numerical model of electromotive force induced by the wearable sensor was established.
Abstract: In order to monitor the diagnosis of mechanical equipment by monitoring the metal wear particles carried in large aperture lubricating oil tubes, the simulation optimization structure design was carried out based on the traditional three-coil inductance wear particle sensor. The numerical model of electromotive force induced by the wear particle sensor was established, and the coil distance and coil turns were simulated by finite element analysis software. When permalloy is covered on the surface of the excitation coil and induction coil, the background magnetic field at the air gap increases, and the induced electromotive force amplitude generated by wear particles is increased. The effect of alloy thickness on the induced voltage and magnetic field was analyzed to determine the optimum thickness, and increase the induction voltage of the alloy chamfer detection at the air gap. The optimal parameter structure was determined to improve the detection ability of the sensor. Ultimately, by comparing the extreme values of the induced voltage of various types of sensors, the simulation determined that the minimum allowable detection of the optimal sensor was 27.5 µm ferromagnetic particles.