Simulation Modeling of Neural-Based Method of Multi-Sensor Output Signal Recognition

2006 ◽  
Author(s):  
I. Turchenko ◽  
V. Kochan ◽  
A. Sachenko ◽  
R. Kochan ◽  
A. Stepanenko ◽  
...  
Keyword(s):  
Output Signal ◽  
Simulation Modeling ◽  
Signal Recognition ◽  
Sensor Output

SENSOR PVDF DENGAN POLARISASI PERMUKAAN SEBAGAI PENDETEKSI GAYA POTONG PADA MESIN GURDI

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Aditya Nugraha ◽  
Masri Bin Ardin
Keyword(s):  
Cutting Force ◽  
Output Signal ◽  
Axial Loading ◽  
Force Sensor ◽  
Maximum Deviation ◽  
Drilling Machine ◽  
Sensor Output ◽  
Sensor Surface ◽  
Surface Polarization ◽  
Tangential Directions

PVDF sensor is a sensor that is often used to measure force, strain, vibration and heat. In this study, PVDF sensors with surface polarization are used to detect cutting forces on the machine. The PVDF sensor that has been polarized on the surface is placed in the chuck part of the engine. Measuring instrumen for testing and calibrating PVDF sensors is oscilloscope with increased loading and reduced axial and tangential directions. After the calibration process, the PVDF sensor was used to measure cutting force on drilling machine, and then the results were compared with the PCB piezotronics force sensor. The PVDF sensor output signal is measured and studied for its voltage using an oscilloscope, where the output signal is compared to the weight given to the PVDF sensor. From the results of these tests indicate that the maximum deviation in axial loading is 0.32V while the tangential loading is 0.31VKeywords. PVDF sensor, Surface polarization, Drilling machine, Cutting force

A Method for Measuring Pressure under Rotating Conditions

2013 ◽  
Vol 663 ◽  
pp. 522-527
Author(s):  
Deng Chao Li ◽  
Xiang Luo ◽  
Xu Cai
Keyword(s):  
Experimental Data ◽  
Centrifugal Force ◽  
Surface Pressure ◽  
Pressure Sensor ◽  
Rotational Speed ◽  
Output Signal ◽  
Functional Relationship ◽  
Sensor Output ◽  
Strain Type ◽  
Type Pressure

A method which can accurately measure surface pressure under rotating conditions is presented. Based on the calibration for strain type pressure sensor, a curve which shows the functional relationship between sensor output signal and actual pressure under different rotational speed is obtained; and the diaphragm deformation caused by centrifugal force can be neglected by the curve. Thus, the actual pressure can be acquired accurately. The factors which may cause errors on the experiment are analyzed. Moreover, the correctional method for the experimental data is attained.

Research on Signal Processing Circuits of Ring Oscillator Accelerometer

2012 ◽  
Vol 263-266 ◽  
pp. 287-291
Author(s):  
Fu Yi Cao ◽  
Zhi Li ◽  
Xiang Feng Wang
Keyword(s):  
Signal Processing ◽  
Basic Principle ◽  
Output Signal ◽  
Low Frequency ◽  
Ring Oscillator ◽  
Sensor Output ◽  
Frequency Signal

In this paper, the basic principle of the ring oscillator accelerometer has been analysed. According to the characteristics of low-frequency signal of ring oscillator accelerometer output, the methods to measure the low frequency of the sensor output signal by measure period is proposed and simulated the comparator circuit. Finally, we design and simulate all measure circuits and the results and theory in line with the good.

scholarly journals Fabrication and Characterization of Bending- Independent Capacitive CMOS Pressure Sensor Stacks

2018 ◽  
Vol 4 (1) ◽  
pp. 595-598
Author(s):  
Roland Fischer ◽  
Heinrich Ditler ◽  
Michael Görtz ◽  
Wilfried Mokwa
Keyword(s):  
Mechanical Stress ◽  
Pressure Sensor ◽  
Output Signal ◽  
Strong Dependence ◽  
Pressure Sensors ◽  
Target Thickness ◽  
Sensor Output ◽  
Additional Information ◽  
Four Point Bending ◽  
Foil Substrate

AbstractArtificial limbs, equipped with miniaturized tactile sensors, can handle objects with more dexterousness. Next to detecting forces, the sensor devices are also able to measure temperature. With this additional information, the touched objects can be better characterized. As such sensors, active CMOS-based capacitive pressure sensors are used in this work. The Sensors are thinned to 20-30 μm target thickness to make them bendable. One challenge of such thin sensors is the strong dependence of the output signal upon bending. To compensate this dependency, two sensors were mounted back to back. This allows a numerical adjustment of the two characteristic sensor output signals to mechanical stress curves. After electrically contacting of the stacks with a 15 μm thin polyimide foil substrate, the bending dependence of the stacks was characterized with a four-point bending procedure. By this characterization the dependency of the pressure sensor output signal on the height of mechanical stress was determined. Both sensor output signals show an inverted behavior under the same mechanical stress which confirmed prior simulation results with the same setup. Based on this information, a numerical method for compensating the bending dependence was successfully proven.

scholarly journals Investigation of sensitive element for pressure sensor based on bipolar piezotransistor

2021 ◽  
Author(s):  
Mikhail ◽  
Denis Prigodskiy
Keyword(s):  
Pressure Sensor ◽  
Output Signal ◽  
Dynamic Range ◽  
Strong Dependence ◽  
Pressure Sensors ◽  
High Sensitivity ◽  
Wheatstone Bridge ◽  
Differential Amplifier ◽  
Sensor Output ◽  
Thin Part

The article translated from Russian to English on pp. 691-693 (please, look down). The paper summarizes results of investigation of high-sensitivity MEMS pressure sensor based on a circuit containing both active and passive stress-sensitive elements: a differential amplifier utilizing two n-p-n piezotransistors and for p-type piezoresistors. A comparative analysis of a sensor utilizing this circuit with a pressure sensor based on traditional piezoresistive Wheatstone bridge and built on the same mechanical part is provided. MEMS pressure sensor with the differential amplifier (PSDA) has sensitivity of S = 0.66 mV/kPa/V, which exceeded the sensitivity of the element with piezoresistive Wheatstone bridge (PSWB) by 2.2 times. The sensitivity increase allows for the following sensor improvements: die size reduction, increase of diaphragm mechanical strength while keeping high pressure sensitivity, and simplifying requirements to external processing of the pressure sensor output signal. There are two main challenges related to the use of PSDA-based pressure sensors: strong dependence of output signal on temperature and higher than in PSWB noise reducing the dynamic range of the device to 10 3. The article describes methods of addressing these problems. The temperature dependence of sensor output signal can be minimized with help of an offset thermal compensation circuit and by eliminating metallization at the thin part of the diaphragm. The noise can be minimized by reducing the thickness of the active base region of the transistor. Circuit analysis with software NI Multisim shows that sensitivity of PSDA-based pressure sensor can be increased 2.3 times by circuit optimization.

scholarly journals METHOD FOR DETECTING AGING RELATED FAILURES OF PROCESS SENSORS VIA NOISE SIGNAL MEASUREMENT

2019 ◽  
pp. 135-146
Author(s):  
Topi Toosi ◽  
Miki Sirola ◽  
Jarkko Laukkanen ◽  
Mark Van Heeswijk ◽  
Juha Karhunen
Keyword(s):  
Output Signal ◽  
Nuclear Power ◽  
Power Spectra ◽  
Principal Component ◽  
Noise Signal ◽  
Pressure Vessels ◽  
Differential Pressure ◽  
Sensor Output ◽  
Sensor Failures ◽  
Water Level Measurements

In this article we examine the methods for detecting and predicting aging related process sensor failures by analyzing the noise of the sensor output signal. The study uses data from non-differential and differential pressure transmitters used in the pressure and water level measurements of the reactor pressure vessels of units 1 and 2 of the Olkiluoto nuclear power plant in Finland. The article contains a review of the current methods for detection of sensor failures. Additionally, we present a new method for detecting changes in the sensor output signal. The method creates fingerprints of the power spectra of the sensors by using Principal Component Analysis (PCA). The changes in these fingerprints together with the measurements of the redundant sensors can be used to detect indications of some of the impending sensor failures. In the experimental study we are able to produce stable fingerprints for both the non-differential and differential pressure transmitters. Also, a potential failure in one of the differential pressure transmitters in Olkiluoto unit 2 is detected by inspecting the fingerprints and analyzing the spectral changes of the transmitter output signal.

scholarly journals NEURAL-BASED RECOGNITION OF MULTI-PARAMETER SENSOR SIGNAL DESCRIBED BY MATHEMATICAL MODEL

2014 ◽  
pp. 140-147
Author(s):  
Iryna Turchenko ◽  
Volodymyr Kochan ◽  
Anatoly Sachenko
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Simulation Modeling ◽  
General Structure ◽  
Signal Recognition ◽  
Neural Network Training ◽  
Training Algorithm ◽  
Sensor Signal ◽  
Network Training ◽  
Network Usage

The possibility of artificial neural network usage for recognition of a signal of a multi-parameter sensor is described in this paper. The general structure of data acquisition channel with usage of neural networks as well as mathematical model of output signal of a multi-parameter sensor is studied in this article. The model of neural network, training algorithm and achieved results of simulation modeling of a multi-parameter sensor signal recognition using MATLAB software are presented at the end of this paper.

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