Development of a low cost test rig for standalone WECS subject to electrical faults

In this paper, a contribution to the development of low-cost wind turbine (WT) test rig for stator fault diagnosis of wind turbine generator is proposed. The test rig is developed using a 2.5 kW, 1750 RPM DC motor coupled to a 1.5 kW, 1500 RPM self-excited induction generator interfaced with a WT mathematical model in LabVIEW. The performance of the test rig is benchmarked with already proven wind turbine test rigs. In order to detect the stator faults using non-stationary signals in self-excited induction generator, an online fault diagnostic technique of DWT-based multi-resolution analysis is proposed. It has been experimentally proven that for varying wind conditions wavelet decomposition allows good differentiation between faulty and healthy conditions leading to an effective diagnostic procedure for wind turbine condition monitoring.     

Classification of vertebral column disorders and lumbar discs disease using attribute weighting algorithm with mean shift clustering

In this article, a new data pre-processing method has been suggested to detect and classify vertebral column disorders and lumbar disc diseases with a high accuracy level. The suggested pre-processing method is called the Mean Shift Clustering-Based Attribute Weighting (MSCBAW) and is based primarily on mean shift clustering algorithm finding the number of the sets automatically. In this study, we have used two different datasets including lumbar disc diseases (with two classes-our database) and vertebral column disorders datasets (with two or three classes) taken from UCI (University of California at Irvine) machine learning database to test the proposed approach. The MSCBAW method is working as follows: first of all, the centres of the sets automatically for each characteristics in dataset by using the mean shift clustering algorithm are computed. And then, the mean values of each property in dataset are calculated. The weighted datasets by multiplying these mean values by each property value in the dataset that have been obtained by dividing the above mentioned mean values by the centres of the sets belonging to the relevant property are achieved. After the data weighting stage, three different classification algorithms that included the k-NN (k-Nearest Neighbour), RBF–NN (Radial Basis Function–Neural Network) and SVM (Support Vector Machine) classifying algorithms have been used to classify the datasets. In the classification of vertebral column disorders dataset with two classes (normal or abnormal), while the obtained classification accuracies and kappa values were 78.70% ± 0.455 (the classification accuracy ± standard deviation), 81.93% ± 0.899, and 80.32% ± 0.56 using SVM, k-NN (for k = 1), and RBF–NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF–NN classifiers were obtained 99.03% ± 0.977, 99.67% ± 0.992, and 99.35% ± 0.9852, respectively. In the classification of second dataset named vertebral column disorders dataset with three classes (Normal, Disk Hernia, and Spondylolisthesis), while the obtained classification accuracies and kappa values were 74.51% ± 0.581, 78.70% ± 0.659, and 83.22% ± 0.728 using SVM, k-NN (for k = 1), and RBF–NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF–NN classifiers were obtained 99.35% ± 0.989, 96.77% ± 0.948, and 99.67% ± 0.994, respectively. As for the lumbar disc dataset, while the obtained classification accuracies and kappa values were 94.54% ± 0.974, 94.54% ± 0.877, and 93.45% ± 0.856 using SVM, k-NN (for k = 1), and RBF–NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF–NN classifiers were obtained 100% ± 1.00, 99.63% ± 0.991, and 99.63% ± 0.991, respectively. The best hybrid models in the classification of vertebral column disorders dataset with two classes, vertebral column disorders dataset with three classes, and lumbar disc dataset were the combination of MSCBAW and k-NN classifier, the combination of MSCBAW and RBF–NN classifier, and the combination of MSCBAW and SVM classifier, respectively.     

An orifice meter for bidirectional air flow measurements: Influence of gas thermo-hygrometric content on static response and bidirectionality

This paper presents the design and calibration of an ISO non-compliant orifice plate flowmeter whose intended use is for respiratory function measurements in the bidirectional air flow range ±9 L/min.The novelty of the proposed sensor consists of a plate beveled in both upstream and downstream sides: a symmetrical geometry is adopted in order to perform bidirectional measurements of flow rate. A mathematical model is introduced to quantify the influence of temperature on the sensor output. Four different positions of the pressure static taps are evaluated in order to maximize bidirectionality. An index is also introduced in order to quantitatively estimate the anti-symmetry of the sensor's response curve.Trials are carried out to evaluate the influence on sensor output of air temperatures (22 °C, 30 °C and 37 °C) at different values of relative humidity (5%, 55% and 85%). Experimental data show a quite good agreement with the theoretical model (R²>0.98 in each condition).The influence of air temperature on the sensor output is minimized by introducing a correction factor based on the theoretical model leading to measurement repeatability better than 2% in overall range of calibration. The mean sensitivity in the calibration range is about 2 kPa L⁻¹·min allowing to obtain a sensor discrimination threshold lower than 0.2 L/min in both directions. The time constant of the whole measurement system, equal to 2.40±0.03 ms, leads to a bandwidth up to 80 Hz making the sensor suitable for respiratory function measurements.     

Management of the wind speed data using adaptive neuro-fuzzy methodology

In this paper, the accuracy of the Weibull model of wind speed is evaluated using an adaptive neuro-fuzzy inference system (ANFIS) based on wind data. The wind data comprises of wind speed measurements in the city of Nis in Serbia at different heights of 10 m, 30 m and 40 m for duration of one year. The ANFIS results are compared with the experimental results and Weibull model using root-mean-square error (RMSE), coefficient of determination, and Pearson coefficient. The effectiveness of the proposed unified strategy is verified based on the simulation results.     

Artificial neural network modeling of thin layer drying behavior of municipal sewage sludge

The back-propagation (BP) and generalized regression neural network models (GRNN) were investigated to predict the thin layer drying behavior in municipal sewage sludge during hot air forced convection. The accuracy of the BP model to predict the moisture content of the sewage sludge thin layer during hot air forced convective drying was far higher than that of the GRNN model. The GRNN models could automatically determine the best smoothing parameters, which were 0.6 and 0.3 for predicting the moisture content and average temperature, respectively. The model type for predicting the average temperature of the sewage sludge thin layer was selected for different sample groups by comparing their MSE values or R² values. The GRNN model was suitable for predicting the average temperature corresponding to the sample groups at hot air velocity of 0.6 m/s, and drying temperatures of 100 °C, 160 °C; hot air velocity of 1.4 m/s, and drying temperatures of 130 °C, 140 °C; hot air velocity of 2.0 m/s, and drying temperatures of 150 °C, 160 °C. The average temperature for the other sample groups was best predicted by the BP model.     

Gasoline level sensor based on displacement sensor using fiber coupler

A simple and fire safe gasoline level sensor has been designed based on displacement sensor using fiber coupler. The sensing principle is to detect displacement of reflector, which is attached to membrane (reflector displacement device), due to the change of gasoline hydrostatic pressure. The displacement of reflector can be detected using fiber coupler from the change of optical power light reflected by the reflector. Three kinds of reflector displacement device used in this experiment are one-layer, two-layer, and three-layer membrane. The experimental results are 0–180 cm of dynamic range, 100–140 cm of linear range, 3.2 mV/cm of sensitivity, and 0.6 cm of resolution for reflector displacement device with one-layer membrane for emptying the tank process. The hysteresis data for emptying and filling the tank process yields the mean of difference 20% for one-layer membrane.     

An all-fiber Fabry-Pérot interferometer for pressure sensing in different gaseous environments

A gas pressure sensor based on an all-fiber Fabry-Pérot interferometer (FFPI) is reported. The sensing head consists of a small section of silica rod spliced with a large offset between two single-mode fibers. The silica rod is used only as mechanical support so that an air cavity can be formed between both SMF. It is shown that the FFPI sensor is sensitive to gas pressure variation and when submitted to different gaseous environments, namely carbon dioxide, nitrogen and oxygen – sensitivities of 6.2, 4.1 and 3.6 nm/MPa, respectively, were attained. The refractive index change on nitrogen environment by means of gas pressure variation was also determined and a sensitivity of 1526 nm/RIU was obtained. The response of the sensing device to temperature variations in air was also studied and a sensitivity of −14 pm/°C was attained.     

Design and application of fiber Bragg grating (FBG) geophone for higher sensitivity and wider frequency range

The fiber Bragg grating geophone sensor with higher sensitivity and wider frequency range was reported. The methods to increase the sensitivity of the FBG cantilever sensor were presented. The acceleration sensitivity of the optimized FBG geophone is 220 pm/g, and the resonant frequency can reach to 295 Hz. The experiments show that the FBG geophone system has the minimum detectable acceleration of 1 mm/s². Some factual application examples of using this fiber Bragg grating geophone monitoring system for micro seismic monitoring in coal mine were presented.     

Development and evaluation of a two-axial shearing force sensor consisting of an optical sensor chip and elastic gum frame

We developed a promising shearing force sensor that is small in size and can measure shearing force along two axes independently. This sensor consists of an elastic gum frame and an optical sensor chip (6 mm × 6 mm × 8 mm). From the experimental results, the resolutions of the sensor along the x- and y-axes are found to be 0.070 N and 0.063 N. We also experimentally demonstrated that the sensor can separately measure shearing force along two axes. Finally, we demonstrated that the scale factor which correspond to resolution and linear portion which correspond to measuring range of the signals can be changed easily by using three types of elastic gum frame. This sensor can be embedded in the finger of a robot hand and use it to not only measure shearing force but also detect the slip phenomenon.     

Performance analysis of a DRL-less AFE for battery-powered wearable EEG

The driven-right-leg (DRL) circuit has been commonly used in the wall-powered EEG systems to reduce common-mode interference in the bio-potential amplifier. However, DRL circuit imposes limitations on the number of channels preventing modular development, and its effectiveness is diminished for a newer generation of battery-powered EEG systems. We present a performance investigation of DRL-less EEG circuit by designing a single-channel EEG with a novel Analog Front End (AFE) that contains a differential amplifier followed by a high-Q active notch filter. The prototyped wearable EEG system has been validated to record neural signals with and without the DRL circuit. The time domain and frequency domain signals show that the designed AFE is not impacted significantly (maximum 4 dB difference) by the DRL elimination and maintains similar signal quality. The customized EEG with and without DRL offers CMRR of 72.98 dB and 71.74 dB, respectively, at 60 Hz (power-line interference range in the USA), whereas CMRR of 72.64 dB and 71.01 dB, respectively, at 20 Hz (representative EEG signal range). DRL elimination allows us to envision a sensor-level modular EEG system for neural monitoring in non-clinical environments.     

Performance evaluation of a cheap,open source,digital environmental monitor based on the Raspberry Pi

We report on the design, construction and evaluation of a low-cost digital environmental monitoring system based on a popular micro-computer board and mass market digital sensors. The system is based around the use of open source software and readily available digital sensors, providing key parameters required for environmentally-controlled calibration laboratories: air temperature, pressure and humidity. Each system logs data at set intervals with front-panel display, web page graphical display and email alerting when exceeding set tolerances. The sensors have been calibrated at the National Physical Laboratory using standards traceable to the SI. Long term stability of the system is estimated and in addition to monitoring of laboratory environments for regulatory purposes, the systems can also be used to provide on-demand values for local refractive index with an expanded (k = 2) uncertainty of 1.1 × 10⁻⁷ as required for many optical-based measuring systems.     

Wind speed and direction measurement based on arc ultrasonic sensor array signal processing algorithm

This article investigates a kind of method to measure the wind speed and the wind direction, which is based on arc ultrasonic sensor array and combined with array signal processing algorithm. In the proposed method, a new arc ultrasonic array structure is introduced and the array manifold is derived firstly. On this basis, the measurement of the wind speed and the wind direction is analyzed and discussed by means of the basic idea of the classic MUSIC (Multiple Signal Classification) algorithm, which achieves the measurements of the 360° wind direction with resolution of 1° and 0–60 m/s wind speed with resolution of 0.1 m/s. The implementation of the proposed method is elaborated through the theoretical derivation and corresponding discussion. Besides, the simulation experiments are presented to show the feasibility of the proposed method. The theoretical analysis and simulation results indicate that the proposed method has superiority on anti-noise performance and improves the wind measurement accuracy.     

Continuous accurate pH measurements of human GI tract using a digital pH-ISFET sensor inside a wireless capsule

This paper describes the design and implementation of a reliable, low-power pH-ISFET sensor inside a wireless diagnostic capsule for monitoring the pH of the human gastrointestinal (GI) tract. The sensitive element of the pH sensing device was an ion-sensitive FET (ISFET) with a threshold voltage that varies by concentration of hydrogen ions. An Ag/AgCl reference electrode that was surrounded by gel (mixed KCl) was applied in this pH-sensing device. An application-specific integrated circuit (ASIC) that is designed by a very large scale integration (VLSI) architecture was used as the interface between the pH sensing device and a wireless transceiver. Experimental results demonstrated that the pH sensitivity and operating range of the pH sensing device was 44.94 mV/pH and 1–11 pH; the device is linear. With the 18-bit Analog/Digital converter (ADC) module in the ASIC, the resolution could achieve 11,780 bits/pH. Moreover, the power dissipation of the pH-sensing device was only 0.048 mW while working in intermittent mode (duty cycle = 20%). Results from human experiments showed that this pH sensing device can work reliably for 136 h, and the pH data of sampled from the human GI tract can be received by a portable data recorder outside the human body in real time.     

Application of two-output port fiber coupler as gasoline level sensor

Detection of gasoline level can be done in a safe and simple way using two output port multimode fiber coupler with a structure of 2 × 2 as a sensor. Two output ports (sensing port) are connected with two reflector displacement device (RDD) and functioned as two probes. These probes are placed on the wall of gasoline tank in a storied and work interchangeably or together depending on setting of these probes. Detection mechanism of the system is based on changes in intensity of reflected light from the reflector RDD that shifts due to changes in level of gasoline (hydrostatic pressure principle). Changes in intensity of light coming into the sensing port are then forwarded to the optical detector. Experiments performed by varying the location of the second probe as 45 cm, 50 cm, and 55 cm above the first probe to detect the level of gasoline in the process of filling and emptying the tank. Experimental results show the process of filling and emptying the tank have small differences of 6% with the dynamic range, the linear region, and resolution are 100 cm, 70 cm, and 0.4 cm respectively. Sensor sensitivity in filling and emptying process of the tank are 2.7 mV/cm and 2.8 mV/cm respectively. These results were the best performance of the sensor, which occurs when the level of the second probe was 55 cm above the first probe.     

High immunity wafer-level measurement of MHz current

Wafer-level characterization of functional prototypes of solid state current sensors is usually the first step in the development stage, therefore a dedicated wafer-level measurement systems are required. Especially for MHz characterization, where electromagnetic interference issues became strongly relevant. Thus, for frequencies above 1 MHz, a sophisticated measurement techniques are needed, because parasitic couplings can significantly affect sensor response and other signal processing stages, causing errors or malfunction of the whole system. We propose technique for high immunity MHz current measurement by solid state sensor in wafer-level configuration. The system is especially well-suited for MHz characterization, where electromagnetic interference issues become highly relevant. We showed that balanced transmission provides possibility to reduce interference in the sensor biasing circuit of about 60 dB compared to single-ended method. The effectiveness of the proposed technique was tested in monitoring MHz current in the external line by single magnetoresistive sensors on wafer level.     

Estimation of biosurfactant yield produced by Klebseilla sp. FKOD36 bacteria using artificial neural network approach

Environmental and medium parameters estimation is an essential step in Bioprocess engineering. In the present study, artificial neural network (ANN) was employed in estimation of biosurfactants yield from bacterial strain Klebseilla sp. FKOD36, surface tension reduction as well emulsification index. The data obtained from experimental design were used in modelling and optimization of ANN method. Temperature, pH value, incubation period, carbon, nitrogen and hydrocarbon sources were used as input of ANN model independently in the prediction of biosurfactants yield, surface tension reduction and emulsification index. Using the optimized values of critical input elements of ANN, the experimental values of biosurfactant yield, emulsification index and surface tension showed close agreement with the model estimate. The most efficient ANN model assessment was 0.030 g/l for actual value 0.038 g/l of biosurfactant yield, 31.67% for actual value 31.68% of emulsification index, and 21.6 dyne/cm for actual value 21.5 dyne/cm of surface tension respectively.     

Identification and characterisation of steel corrosion using passive high frequency RFID sensors

High frequency RFID sensors are attractive in diverse applications where sensor performance is required at a low cost and dimension restriction. An approach adapting commercial passive 13.56 MHz RFID tags has been developed for sensing corrosion stage. This investigation includes balance of sensing and positioning of RFID sensors for corrosion detection by analysing real and imaginary parts of the complex impedance. With passive HF RFID sensors, real part and imaginary part of complex impedance have been extracted from the reader coil with VNA (vector network analyser) and delivering a unique capability for corrosion sensing with different atmospheric exposure time steel samples (1 month, 6 months, 10 months and 12 months). With different positioning (5–25 mm), features extraction based on the complex impedance with PCA (principal component analysis) has been designed for position-independent corrosion evaluation.     

Performance tests of a new non-invasive sensor unit and ultrasound electronics

Industrial applications involving pulsed ultrasound instrumentation require complete non-invasive setups due to high temperatures, pressures and possible abrasive fluids. Recently, new pulser-receiver electronics and a new sensor unit were developed by Flow-Viz. The complete sensor unit setup enables non-invasive Doppler measurements through high grade stainless steel. In this work a non-invasive sensor unit developed for one inch pipes (22.5 mm ID) and two inch pipes (48.4 mm ID) were evaluated. Performance tests were conducted using a Doppler string phantom setup and the Doppler velocity results were compared to the moving string target velocities. Eight different positions along the pipe internal diameter (22.5 mm) were investigated and at each position six speeds (0.1–0.6 m/s) were tested. Error differences ranged from 0.18 to 7.8% for the tested velocity range. The average accuracy of Doppler measurements for the 22.5 mm sensor unit decreased slightly from 1.3 to 2.3% across the ultrasound beam axis. Eleven positions were tested along the diameter of the 48.4 mm pipe (eight positions covered the pipe radius) and five speeds were tested (0.2–0.6 m/s). The average accuracy of Doppler measurements for the 48.4 mm sensor unit was between 2.4 and 5.9%, with the lowest accuracy at the point furthest away from the sensor unit. Error differences varied between 0.07 and 11.85% for the tested velocity range, where mostly overestimated velocities were recorded. This systematic error explains the higher average error difference percentage when comparing the 48.4 mm (2.4–5.9%) and 22.5 mm (1.3–2.3%) sensor unit performance. The overall performance of the combined Flow-Viz system (electronics, software, sensor) was excellent as similar or higher errors were typically reported in the medical field. This study has for the first time validated non-invasive Doppler measurements through high grade stainless steel pipes by using an advanced string phantom setup.     

Development and evaluation of a sensor glove for hand function assessment and preliminary attempts at assessing hand coordination

Quantitative measurement of hand kinematics can help us to better understand pathophysiological aspects of finger neural control and quantify hand impairments. A sensor glove was developed based on resistive bend sensors and resistive force sensors to monitor finger joint angles and forces exerted to objects by fingers. The validity and reliability of the glove were evaluated. A novel method was proposed to visualize and quantify the abnormality of the inter-joint coordination. The validity test indicated that the accuracy error of measuring joint angles was approximately ±6° and that of measuring forces was approximately ±8 g. The reliability test yielded an intraclass correlation coefficient of 0.9876 ± 0.0058 for the force sensors and 0.9561 ± 0.0431 for the bend sensors. The stability, accuracy and reliability of measuring joint angles were comparable with previous studies. The involvement of force sensors and the capacity of reflecting inter-joint coordination make the glove more comprehensive for hand function assessment.     

Development and evaluation of a compact 6-axis force/moment sensor with a serial structure for the humanoid robot foot

In order to walk safely, forces and moments exerted on humanoid robot foot should be measured and used for controlling the robot. This paper describes the development and evaluation of a six-axis force/moment sensor used under humanoid robot foot. The developed sensor is capable of measuring 400 N horizontal force, 1000 N vertical force, 20 N·m moment about the horizontal axis and 10 N·m moment about the vertical axis using rectangular cross-sectional beams. The structure of the sensor is newly modeled, and the sensing elements are simulated by using finite element method (FEM). Then the sensor is fabricated by attaching strain gages onto the beams. Finally, a characteristic test of the developed sensor is carried out, and the output from FEM analysis agrees with those from the characteristic test.