A Faraday’s law paradox

The voltage drop or potential difference across a resistor is easily measured with a voltmeter. However, if the loop formed by the resistor, the connecting leads, and the voltmeter surrounds a changing magnetic field then the measured voltage drop will be different. This result can lead to a paradox when using Faraday’s law.

Analysis of Tilt Effect on Notch Depth Profiling Using Thin-Skin Regime of Driver-Pickup Eddy-Current Sensor

Electromagnetic eddy current sensors are commonly used to identify and quantify the surface notches of metals. However, the unintentional tilt of eddy current sensors affects results of size profiling, particularly for the depth profiling. In this paper, based on the eddy current thin-skin regime, a revised algorithm has been proposed for the analytical voltage or impedance of a tilted driver–pickup eddy current sensor scanning across a long ideal notch. Considering the resolution of the measurement, the bespoke driver–pickup, also termed as transmitter–receiver (T-R) sensor is designed with a small mean radius of 1 mm. In addition, the T-R sensor is connected to the electromagnetic instrument and controlled by a scanning stage with high spatial travel resolution, with a limit of 0.2 μm and selected as 0.25 mm. Experiments were conducted for imaging of an aluminium sheet with seven machined long notches of different depths using T-R sensor under different tilt angles. By fitting the measured voltage (both real and imaginary part) with proposed analytical algorithms, the depth profiling of notches is less affected by the tilt angle of sensors. From the results, the depth of notches can be retrieved within a deviation of 10% for tilt angles up to 60 degrees.

Analysis of Tilt Effect on Notch Depth Profiling Using Thin-Skin Regime of Driver-Pickup Eddy-Current Sensor

Electromagnetic eddy current sensors are commonly used to identify and quantify the surface notches of metals. However, the unintentional tilt of eddy current sensors affects results of size profiling, particularly for the depth profiling. In this paper, based on the eddy current thin-skin regime, a revised algorithm has been proposed for the analytical voltage or impedance of a tilted driver–pickup eddy current sensor scanning across a long ideal notch. Considering the resolution of the measurement, the bespoke driver–pickup, also termed as transmitter-receiver (T-R) sensor is designed with a small mean radius of 1 mm. Besides, the T-R sensor is connected to the electromagnetic instrument and controlled by a scanning stage with high spatial travel resolution , with a limit of 0.2 μm and selected as 0.25 mm. Experiments have been out on the voltage imaging of an aluminium sheet with 7 machined long notches of different depths using T-R sensor under different tilt angles. By fitting the measured voltage (both real and imaginary part) with proposed analytical algorithms, the depth profiling of notches is less affected by the tilt angle of sensors. From the results, the depth of notches can be retrieved within a deviation of 10 % for tilt angles up to 60 degrees.

DEVELOPMENT OF PADDY-FIELD WATER LEVEL GAGE CORRESPONDING TO A SENSOR-NETWORK

This study proposes a measurement system that comprises an e-Tape water level sensor, Arduino and XBee. The system was considered a success because of the linear relation between measured voltage signals and water depths obtained by it. This linearity was essential because Arduino does not have non-linear calculation ability. As a result, the numerical order of RMSE in measuring water depth using this system was obtained as 3.52 mm. For measuring water consumption for 1 day at the standard scale of paddy fields in Japan, water consumption can be estimated using the system below non-flowing water surfaces. However, when there is water flow, it will be difficult to estimate water consumption because discharge errors may be cumulative.

Numerical analysis and experimental verification of the induced waveform characteristics for aeroengine gas path debris electrostatic sensor

This article introduces the principle of aeroengine gas path electrostatic monitoring and establishes a mathematical model of aeroengine gas path debris electrostatic sensor. In this study, we simulate particle’s movement based on the established model and perform numerical analysis of the induced charge pulse waveform. The simulation results show the quantitative relationship among particle’s charge amount, velocity, and pulse waveform’s features, and obtain the qualitative relationship between particle’s spatial position and pulse waveform’s features. A test rig is designed to verify the correctness of the mathematical model. A measurement mode based on dual-channel sensors has been proposed, and corresponding signal processing methods are used to calculate the velocity of the particle and reconstruct the charge pulse waveform from the measured voltage signal. The conclusions of this study not only avoid the shortcomings of traditional signal processing methods that directly use the measured voltage signal but also have important significance for improving the electrostatic monitoring capability.

The pre-exponential voltage-exponent as a sensitive test parameter for field emission theories

For field electron emission (FE), an empirical equation for measured current I m as a function of measured voltage V m has the form I m = CV m k exp[– B / V m ], where B is a constant and C and k are constants or vary weakly with V m . Values for k can be extracted (i) from simulations based on some specific FE theory, and in principle (ii) from current–voltage measurements of sufficiently high quality. This paper shows that a comparison of theoretically derived and experimentally derived k- values could provide a sensitive and useful tool for comparing FE theory and experiment, and for choosing between alternative theories. Existing methods of extracting k -values from experimental or simulated current–voltage data are discussed, including a modernized ‘least residual’ method, and existing knowledge concerning k -values is summarized. Exploratory simulations are reported. Where an analytical result for k is independently known, this value is reliably extracted. More generally, extracted k -values are sensitive to details of the emission theory used, but also depend on assumed emitter shape; these two influences will need to be disentangled by future research, and a range of emitter shapes will need examination. Other procedural conclusions are reported. Some scientific issues that this new tool may eventually be able to help investigate are indicated.

Arduino-Based Battery Voltage Monitoring and SMS Gateway

This study aims to monitor the value of the voltage on the battery using a voltage sensor and using the State of Charge method to estimate the charged power of the VRLA battery remotely by utilizing SMS Gateway-based technology so that checking is no longer necessary. The results obtained are displayed on the smartphone in the form of an SMS. To determine the SOC in a 12V VRLA battery, it is calculated based on the number of each cell. VRLA 12V has 6 cells, each cell consisting of 2V to 2.4Volt. The capacity of a VRLA battery in 1 cell is declared 100% full at a voltage of 2.3 volts. So that data is obtained to determine the full percentage of the VRLA 12V 6 cell battery with a capacity of 7.2Ah, namely 13.8V. Experiments were carried out using solar panels, VRLA batteries, voltage sensors, Arduino UNO, and GSM SIM900A modules. This study succeeded in reading the measured voltage value with the sensor, and obtained an error value of 0.20 and a standard deviation of 0.02, and for the monitoring process to run smoothly without problems.

PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

Background: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents. Methods: Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition. Results: After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test. Conclusion: The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

Study on Restriking Transient Voltage Characteristics and Waveform Patterns of Planar Copper-Carbon Electrodes using Forms

In this study, the authors measured voltage and current waveforms in real time during a serial arc discharge. The analysis results of the arc discharge radiation patterns exhibited intermittent discharge, arc growth, creation of a heat generating area, occurrence of plume, and formation of a red heat area, which proceeded in that order. When the serial arc discharge was introduced, the current and voltage waveforms exhibited periodicity as sine waves. It was also observed that a restriking transient voltage occurred when the waveform changed from positive (+) to negative (-) and vice versa. When the discharge proceeded, the amount of heat generated for 1 s and 600 s was approximately 0.317 mJ, and 190 mJ, respectively. The duration of the short circuit was approximately 1.66 ms, and in the case of the voltage waveform, it was evident that the electric potential increased to 49.9 V in the same cycle. Furthermore, when the discharge proceeded, the effective value (RMS value) of the current was approximately 1.72 A with a maximum current of approximately 2.53 A, whereas the effective value of the voltage was approximately 42.8 V with a maximum of approximately 208 V.

Calibration of the loop probe for the near-field measurement

Accurate near-field measurements for either deterministic or stochastic electromagnetic fields characterization require a relevant process that removes the influence of the probes, transmission lines, and measurement circuits. The main part of the experimental work presented here is related to a calibration procedure of a test setup consisting of a microstrip test structure and a scanning loop probe. The calibration characteristic, obtained by comparing measured and simulated results, is then used to convert the measured voltage into the magnetic field across and along the microstrip line at the specific height above it. By performing the measurements and simulations of the same test structure with the loop probe in the presence of an additional scanning probe, the influence of the additional probe to the measured output is thoroughly investigated and relevant corrections are given. These corrections can be important when two-point correlation measurement is required, especially in scanning points when two probes are mutually close.