Metrology and Measurement Systems

Light sources and luminaires made in the LED technology are nowadays widely used in industry and at home. The use of these devices affects the operation of the power grid and energy efficiency. To estimate this impact, it is important to know the electrical parameters of light sources and luminaires, especially with the possibility of dimming. The article presents the results of measurements of electrical parameters as well as luminous flux of dimmable LED luminaires as a function of dimming and RMS supply voltage. On the basis of the performed measurements, a model of LED luminaire was developed for prediction of electrical parameters at set dimming values and RMS values of the supply voltage. The developed model of LED luminaire has 2 inputs and 26 outputs. This model is made based on 26 single models of electrical parameters, whose input signals are supply and control voltages. The linear regression method was used to develop the models. An example of the application of the developed model for the prediction of electrical parameters simulating the operation of an LED luminaire in an environment most similar to real working conditions is also presented.

We describe construction and investigation results of optical trace gas sensor working in the 3.334–3.337 μm spectral range. Laser spectroscopy was performed with a multipass cell. A setup was elaborated for detection of ethane at the 3.3368035 μm absorption line. Analysis of the gas spectra and the experiment have shown that, beside C2H6, the sensor is suitable for simultaneous detection of methane, formaldehyde and water vapor. Due to nonlinearity of the laser power characteristic we decided to detect the fourth harmonic of signal. Additional laser wavelength modulation was applied for optical interference suppression. In result, the precision of ethane detection of approximately 80 ppt has been achieved for the averaging time of 20 seconds. Long-term stability as well as the measurement linearity have also been positively tested. The system is suitable for detecting potential biomarkers directly in human breath.

Different temperature sensors show different measurement values when excited by the same dynamic temperature source. Therefore, a method is needed to determine the difference between dynamic temperature measurements. This paper proposes a novelty approach to treating dynamic temperature measurements over a period of time as a temperature time series, and derives the formula for the distance between the measurement values using uniformsampling within the time series analysis. The similarity is defined in terms of distance to measure the difference. The distance measures were studied on the analog measurement datasets. The results show that the discrete Fréchet distance has stronger robustness and higher sensitivity. The two methods have also been applied to an experimental dataset. The experimental results also confirm that the discrete Fréchet distance performs better.

Capacitive leakage and adjacent interference are the main influence sources of the measuring error in the traditional series step-up method. To solve the two problems, a new algorithm was proposed in this study based on a three-ports network. Considering the two influences, it has been proved that response of this three-ports network still has characteristics of linear superposition with this new algorithm. In this threeport network, the auxiliary series voltage transformers use a two-stage structure that can further decrease measurement uncertainty. The measurement uncertainty of this proposed method at 500/√3 kV is 6.8 ppm for ratio error and 7 μrad for phase displacement ( k = 2). This new method has also been verified by comparing its results with measurement results of the PTB in Germany over the same 110/√3 kV standard voltage transformer. According to test results, the error between the two methods was less than 2.7 ppm for ratio error and 2.9 μrad for phase displacement.

In this work, the electromotive force (EMF) near a permanent magnet heating cylinder was determined using a practical test bench. The aim is to elaborate three-dimensional analytical calculation capable of predicting accurately the same electromagnetic quantities by calculating the induced EMF in the presence of an inductive sensor. The analytical approach is obtained from developing mathematical integrals using the Coulombian approach to permanent magnets. In this approach, rotations are considered by Euler’s transformations matrices permitting the calculation of all permanent magnets flux densities contributions at the same points in the surrounding free space. These points, part of a uniform rectangular grid of the active EMF sensor surface, are used to compute the EMF by Faraday’s law. The validation results between experimental and simulated ones confirm the robustness and the efficiency of the proposed analytical approach.

Vibration analysis for conditional preventive maintenance is an essential tool for the industry. The vibration signals sensored, collected and analyzed can provide information about the state of an induction motor. Appropriate processing of these vibratory signals leads to define a normal or abnormal state of the whole rotating machinery, or in particular, one of its components. The main objective of this paper is to propose a method for automatic monitoring of bearing components condition of an induction motor. The proposed method is based on two approaches with one based on signal processing using the Hilbert spectral envelope and the other approach uses machine learning based on random forests. The Hilbert spectral envelope allows the extraction of frequency characteristics that are considered as new features entering the classifier. The frequencies chosen as features are determined from a proportional variation of their amplitudes with the variation of the load torque and the fault diameter. Furthermore, a random forest-based classifier can validate the effectiveness of extracted frequency characteristics as novel features to deal with bearing fault detection while automatically locating the faulty component with a classification rate of 99.94%. The results obtained with the proposed method have been validated experimentally using a test rig.

The article presents the method of identifying surface damage by measuring changes in resistance in graphitebased sensing skin. The research focused on analysis of conductivity anomalies caused by surface damage. Sensitivity maps obtained with Finite Element Method (FEM) in conjunction with the analytical damage model were used to build the coating evaluation algorithm. The experiment confirmed the ability of this method to identify a single elliptical-shape damage. Eight electrodes were enough to locate the damage that covered about 0.1‰ of the examined area. The proposed algorithm can prove useful in simple applications for surface condition monitoring. It can be implemented wherever it is possible to apply a thin layer of conductor to a non-conductive surface.

Optically stimulated luminescence (OSL) and thermoluminescence (TL) methods are commonly used in dosimetry of ionizing radiation and dating of archaeological and geological objects. A typical disadvantage of OSL detectors is signal loss over a longer time scale. In this article, we present a method of studying this phenomenon as well as monitoring the state of the detector by means of optical sampling. The method was used to determine the OSL signal loss (fading) characteristics of selected potassium feldspars.