Method for calibration and classification of torque measuring devices

This research article refers to the application of the evaluated measurement uncertainty for deciding the statement of conformity. It is a proposal to rethink about the classification of measuring devices, taking into account the calculations of uncertainty as a decision rule. It is also a base for complete compatibility and harmonization between ISO 17025:2017 and the other standards. To verify this proposal a case study on compression testing machine classification is used. This proposal aims to review classification criteria for these machines. Since the uncertainty value is equivalent to all parameters that may affect the performance of these machines, it is logical and accurate to use it as the basis for the classification. This approach may be employed for the upcoming version of ISO 7500 standard to use the uncertainty value as a base for machine classification.

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Method for calibration and classification of torque measuring devices

10.3403/01350565u ◽

2015 ◽

Keyword(s):

Measuring Devices

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Domesticating Light: Standards and Artisanal Knowledge in Early Astrophysics

HoST - Journal of History of Science and Technology ◽

10.2478/host-2020-0002 ◽

2020 ◽

Vol 14 (1) ◽

pp. 6-31

Author(s):

Javier Ordóñez

Keyword(s):

Nineteenth Century ◽

Luminous Intensity ◽

The Sun ◽

Deep Space ◽

Second Moment ◽

Measuring Devices ◽

Traditional Classification ◽

Solar Astronomy

AbstractAstrophysics was born in the nineteenth century as a “New Astronomy” (in the words of Samuel Langley, 1884), a knowledge built primarily by amateurs who explored deep space by studying the Sun, stars and nebulae. They were credible enough to interest physicists who did research on the properties of radiation and hence came to constitute a solid and recognised discipline. The aim of this research is to study the contribution of artisanal knowledge in the construction of this new discipline at two distinct moments. The first, when artisans worked to find a standard to normalise the manufacture of the glass with which the lenses of refracting telescopes were manufactured. The most recognised of these artisans was Fraunhöfer. The second moment occurred when the experience of artisan knowledge enabled the manufacture of instruments that improved the traditional classification of the magnitude of the stars. The search for standards led to an alliance between artisans and scientists during the same period in which spectroscopy was carried out. In this case, a unit of luminous intensity was sought that could serve as a standard to classify the stars by their luminosity. Industries, university laboratories and astronomers interested in solar astronomy (such as Karl F. Zöllner), collaborated with the artisan manufacturers of measuring devices, and gave rise to a paradigmatic case of science and industry transfer.

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Classification of Superimposed Partial Discharge Patterns

Energies ◽

10.3390/en14082144 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2144

Author(s):

Benjamin Adam ◽

Stefan Tenbohlen

Keyword(s):

Short Term Memory ◽

Partial Discharge ◽

Electrical Measuring ◽

Classification Problem ◽

Classification Systems ◽

Single Class ◽

Measuring Devices ◽

Long Short Term Memory ◽

Discharge Patterns

Phase resolved partial discharge patterns (PRPD) are routinely used to assess the condition of power transformers. In the past, classification systems have been developed in order to automate the fault identification task. Most of those systems work with the assumption that only one source is active. In reality, however, multiple PD sources can be active at the same time. Hence, PRPD patterns can overlap and cannot be separated easily, e.g., by visual inspection. Multiple PD sources in a single PRPD represent a multi-label classification problem. We present a system based on long short-term memory (LSTM) neural networks to resolve this task. The system is generally able to classify multiple overlapping PRPD by while only being trained by single class PD sources. The system achieves a single class accuracy of 99% and a mean multi-label accuracy of 43% for an imbalanced dataset. This method can be used with overlapping PRPD patterns to identify the main PD source and, depending on the data, also classify the second source. The method works with conventional electrical measuring devices. Within a detailed discussion of the presented approach, both its benefits but also its problems regarding different repetition rates of different PD sources are being evaluated.

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Energy Controlling - Analysis and Evaluation of Energy Measuring Equipment for the Purpose of Energy Transparency in Production Plants

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.655.35 ◽

2014 ◽

Vol 655 ◽

pp. 35-40 ◽

Cited By ~ 2

Author(s):

Tobias Rackow ◽

Tallal Javied ◽

Teresa Geith ◽

Peter Schuderer ◽

Jörg Franke

Keyword(s):

Measuring Equipment ◽

Manufacturing Industry ◽

Electricity Consumption ◽

Analysis And Evaluation ◽

Measuring Devices

This paper addresses the analysis and evaluation of energy measuring devices for the operation in the manufacturing industry. The focus lies on the comparison and scoring of energy meters regarding their performance spectrum against the backdrop of an energy controlling. Based on the fundamentals of electro technical metrology, the main measurement parameters were identified which are necessary for the purpose in the manufacturing industry. Further, capability characteristics for the differentiation of electricity meters were defined. With this, a classification of meters into a basic, a standard, an advanced and a premium class was undertaken. It is shown, that the advanced class is sufficient for the permanent monitoring of electricity consumption in the lights of energy controlling.

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Credal Fusion of Classifications for Noisy and Uncertain Data

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i2.pp1071-1087 ◽

2017 ◽

Vol 7 (2) ◽

pp. 1071 ◽

Cited By ~ 1

Author(s):

Fatma Karem ◽

Mounir Dhibi ◽

Arnaud Martin ◽

Med Salim Bouhlel

Keyword(s):

Missing Values ◽

Uncertain Data ◽

Main Idea ◽

Belief Function ◽

Training Data ◽

Classification Model ◽

Measuring Devices ◽

Belief Function Theory

This paper reports on an investigation in classification technique employed to classify noised and uncertain data. However, classification is not an easy task. It is a significant challenge to discover knowledge from uncertain data. In fact, we can find many problems. More time we don't have a good or a big learning database for supervised classification. Also, when training data contains noise or missing values, classification accuracy will be affected dramatically. So to extract groups from data is not easy to do. They are overlapped and not very separated from each other. Another problem which can be cited here is the uncertainty due to measuring devices. Consequentially classification model is not so robust and strong to classify new objects. In this work, we present a novel classification algorithm to cover these problems. We materialize our main idea by using belief function theory to do combination between classification and clustering. This theory treats very well imprecision and uncertainty linked to classification. Experimental results show that our approach has ability to significantly improve the quality of classification of generic database.

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Study on the Influence of Measuring AE Sensor Type on the Effectiveness of OLTC Defect Classification

Sensors ◽

10.3390/s20113095 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3095 ◽

Cited By ~ 1

Author(s):

Daria Wotzka ◽

Andrzej Cichoń

Keyword(s):

Optimal Algorithm ◽

International Standards ◽

Supervised Machine Learning ◽

Discrete Wavelet ◽

Destructive Testing ◽

Fault Type ◽

Measuring Devices ◽

Load Tap Changer ◽

Ae Signals

The principal objective of this study is to improve the diagnostics of power transformers, which are the key element of supplying electricity to consumers. On Load Tap Changer (OLTC), which is the object of research, the results of which are presented in this article, is one of the most important elements of these devices. The applied diagnostic method is the acoustic emission (AE) method, which has the main advantage over others, that it is considered as a non-destructive testing method. At present, there are many measuring devices and sensors used in the AE method, there are also some international standards, according to which, measurements should be performed. In the presented work, AE signals were measured in laboratory conditions with various OLTC defects being simulated. Five types of sensors were used for the measurement. The recorded signals were analyzed in the time and frequency domain and using discrete wavelet transformation. Based on the results obtained, sets of indicators were determined, which were used as features for an autonomous classification of the type of defect. Several types of learning algorithms from the group of supervised machine learning were considered in the research. The performance of individual classifiers was determined by several quality evaluation measures. As a result of the analyses, the type and characteristics of the most optimal algorithm to be used in the process of classification of the OLTC fault type were indicated, depending on the type of sensor with which AE signals were recorded.

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