IMEKO Event Proceedings Search

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László Móricz, Zsolt János Viharos
Vibration Based Cutting Tool Path Differentiation by Feature Selection

During machining along a complex tool path, it is difficult to connect the change in the amplitude and in the frequency of the recorded vibration signals to the actual state of tool wear. In this article, a method is presented in which the tool wear processes that occur during the machining of a complex geometry (cooling pocket formed on the ceramic coating of the turbine blade) can be evaluated based on the vibration data series recorded during the machining differentiating of some elements of the complex path movement performed by the tool.

Adrian Bilski
Wind Generation Forecast With the Use of AI-Based Regression Methods

The topic of the paper is the presentation of the methodology and the results of forecasting energy generation by the wind turbine with the utilization of three regression methods: factorization machines, decision trees and random forests. The data coming from the wind farm in Turkey was first preprocessed to facilitate prediction task. The prediction of the eneregy production.

Monica Egusquiza; Alex Presas; David Valentin; Carme Valero; Beibei Xu; Diyi Chen; Eduard Egusquiza
Diagnostics of a hydraulic turbine failure

This paper presents the failure analysis and diagnostic of a hydraulic turbine. Shortly after a maintenance revision the thrust bearing of the turbine was destroyed when the turbine was put into operation. The damaged bearing was examined and the possible causes discussed. To identify the problem that led to the failure of the thrust bearing, a comprehensive on-site measurement campaign was done. Vibrations, pressures, temperatures and operating parameters were acquired at different operating conditions of the turbine. The analysis of the data allowed to determine the source of the problem and to implement a solution.

Mirko Marracci, G. Caposciutti, A. Buffi, G. Bandini and B. Tellini
Failure limit analysis for Li-ion batteries using Ragone plot: a preliminary study

In this paper, a new possible definition of failure zone for Li-ion batteries is proposed. Based on the general concept that a battery can be considered failed when its performance no longer meets the requirements of the application for which it is designed, a new application-dependent failure zone definition is proposed using the Ragone plot of the cell. The results of an experimental campaign to validate the proposal are presented and discussed in the paper.

Iacopo Marri, Emil Petkovski,Loredana Cristaldi, Marco Faifer
Lithium-ion batteries soh estimation, based on support-vector regression and a feature-based approach

Lithium-Ion batteries, have become enormously used in many systems and applications, and are the most widespread energy storage system. Optimizing the usage of batterie is therefore very important to increase the safety of systems like electric vehicles or portable devices, to reduce economic loss in industrial environments, and to increase their availability. An accurate State of Health (SoH) estimation is important since it allows us to know battery conditions and make an appropriate use of it, and it improves the accuracy of other diagnostic measures, like State of Charge (SoC). In this paper, an approach for SoH estimation is proposed, based on Support Vector Regression machine learning algorithm and a smart feature extraction process, finding a good trade-off between applicability, light computation effort, and accuracy of results. Features selection and parameters tuning are discussed, and performances are measured on a dataset from the Prognostics Center of Excellence at NASA, considering 3 batteries of the dataset.

I. MpiaBolombanza, G.EkuluNdiakama, M.AvociUgwiri, B.Kilundu
Coupling Singular Spectral and Envelope Analysis for Localised Bearing Defect Detection

In many industrial situations, bearing failure can lead to serious consequence on the overall process. A bearing’s fault progressive character raises the question of finding the right moment to perform replacement at the cost of stopping the machine. The study done in this paper deals with mathe- matical modeling of the bearing’s rolling element with a local defect on its fixed outer ring, based on a mass-spring-damper archetype system. A simulation of the vibratory behavior is performed, and its impact on ball-defect coincidences during shaft rotation under different working conditions is analyzed. The paper suggests applying advanced pre-processing tech- niques such as Singular Spectrum Analysis (SSA) and Envelope Analysis (EA) before extracting statistical indicators. Some well- known time-domain indicators such as the Root Mean Square (RMS), kurtosis, and Energy around Ball Pass Frequency Outer- ring (EBPFO) are used on the raw and processed signals to highlight the defect evolution. The results carried out show that, applying the EA with SSA on the raw time-series signal at the pre-processing level, before statistical analysis can significantly improve the detection, thus an excellent diagnosis to incipient defects in bearings.

Yu Jun, Olena Hordiichuk-Bublivska, Yan Lingyu, Marian Kyryk, Mykola Beshley, Hu Jiwei
Big Data Аnalysis in Smart Grid Systems

The problem of Big Data processing in large industrial systems requires the use of machine learning methods. A smart grid system is an example of improving the efficiency of traditional data processing systems, which allows much more efficient and flexible distribution of electricity to end-users. However, for a smart grid to work properly, it needs to constantly monitor data from sensors and meters. The Singular Value Decomposition (SVD) algorithm is used to improve the efficiency of Big Data processing and reduce its dimensionality. The paper proposes the use of advanced SVD, which can work in distributed industrial systems and ensure the reliability and speed of data processing.

Karol Kuczynski, Piotr Bilski, Adrian Bilski, Jerzy Szymanski
Identification of Defects in the Magnetoelectric Ring Sensor using Spectral Analysis

The paper discusses the method for detecting defects that may arise in the construction of ring-shaped magnetoelectric sensors. The approach is based on the amplitude and phase spectral analysis generated on the sensor’s output. The defect detection is performed using the Discrete Fourier Transform (DFT). The experiments covered two faults, related to the physical dislocation of the amorphous metal ribbon around the magnetic ring. Results show that it is possible to automatically locate particular defects in the electromagnetic sensor based on the input-output characteristic analysis.

Krzysztof Dowalla, Piotr Bilski, Ryszard Kowalik
Series arc fault detection and line selection based on Non-Intrusive Load Monitoring Method

The series arc faults are a common cause of household fires. Low fault current amplitude is the reason for the difficulties in implementing effective arc detection systems. The paper presents a novel arc detection and line selection method. It can be easily used in the low-voltage Alternate Current (AC) network to enhance the functionality of the Non-Intrusive Load Monitoring (NILM) system with arc detection for the whole household. Unlike existing methods, the proposed approach exploits not only current signal but also voltage signal time domain analysis. In the case of arc fault detection, line selection is based on the mean values of changes in the consecutive current signal periods during the arc and comparing them with current waveforms for each appliance in non-arc conditions. Tests have been conducted with up to 6 devices operating simultaneously in the same circuit. Single period arc detection accuracy was 98.47%, with recall at 97.5% and F-score of 0.983. The arc detection accuracy in terms of the IEC 62606:2013 standard was 99.33%, F-score of 99.33. Line selection accuracy was 91.57%.

Tamás Gyulai, Péter Wolf, Ferenc Kása, Zsolt János Viharos
Operational Structure for an Industry 4.0 oriented Learning Factory

Learning Factory (LF) – as a concept - is fully in line with the Industry 4.0 and also with the novel Industry 5.0 major trends, creating an integrated, realistic learning environment, combining didactics, layout and processes with testing and experimentation opportunities. There are already well functioning LFs but their main emphases are very different, fitted to the given, regional industrial potentials. To manage a LF, a precise and structured activity set is needed which formulation is the main contribution of the given manuscript. The paper reviews the main global trends and various existing LFs’ activities. The comprehensive set of LFs’ expectations is defined through the identification of the key target groups and their objectives together with the three main related pillars of “A”: Regional and market connections, "B": implementation and operation of the physical and virtual Learning Factory and "C": development and delivery of customised Learning Factory services. The paper suggests having an activity set consisting of ten, precisely formulated Work Packages. This novel framework also supported the concept formulation of a new LF in Hungary, Europe.

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