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Michael Ramsey
Measurement Uncertainty from Sampling: Implication for Testing, Diagnostics and Inspection

Measurement uncertainty (MU) has become recognised as the primary metric of data quality for quantitative chemical measurements made in the laboratory (i.e. ex situ). Furthermore, the primary sampling of the material under consideration, is now generally considered to be the first step in the measurement process. It follows that the MU that arises from sampling (UfS) needs to be included in the overall estimate of MU. This principle also applies when measurements are made in situ, without removal of a physical sample, as is the case for most hand-held and locally positioned measurement devices. A worked example with Portable XRF is used to explain how MU and UfS can be estimated for in situ measurements. It is argued that this role of UfS within MU is equally applicable to quantitative measurements made for Testing, Diagnostics and Inspection in general, and will improve the reliability of compliance decision made on whatever material is under consideration.

Ankica Milinković, José Luis García-Balboa, Antonio Miguel Ruiz-Armenteros
An Overview of Methods Applied To Quality Control Of Storage Tanks Volume

Fixed storage tanks at atmospheric pressure or under pressure are built for bulk liquid storage and may be used for the measurement of volume or mass of liquid contained. When used for that measurement, they shall comply with the requirements described in international legal norms, developed by the International Organization of Legal Metrology (IOLM). There are different approaches and methods used for the conformity assessment of tanks, covering all or part of the operations for initial verification as well as subsequent verification or recalibration in service of the tanks. In this paper, conclusions will be shown based on the understanding of the definition of storage tanks for petroleum products, the needs of the gauging process of tanks, selections, and comparations of officially recognized methods, as well as relevant researches with respect to improving and advancing of traditional methods. The main categorization implies the geometrical and volumetric operations, a combination of these two, or some other more appropriate method. The aim of this paper is to select, compare and analyze existing approaches, as well as give recommendations to improve general aspects of tank control using the new and advanced methods based on the capabilities of automated and sophisticated tools.

Loredana Cristaldi, Marco Faifer, Alessandro Ferrero, Roberto Ottoboni
Weld, Defect, Segmentation, Image processing, Region Growing, Interval Fusion, Preference Aggregation.

Failure Mode and Effects Analysis (FMEA) is a procedure that is often skipped or not completed generally because of time of financial constraints, despite its proper use during the different steps of the product life cycle (design, development, manufacturing and maintenance) makes it possible to identify and possibly solve potentially critical problems. The most often skipped part of the FMEA procedure is that related to the sensing and measuring equipment, when present. Even when they are considered, the analysis is confined to the possible effects of failures that make them unavailable, while also the deviation from the expected metrological performance (larger uncertainty, as well as unexpected bias) may cause severe malfunctions in the whole system. This paper proposes a possible way to consider also the metrological performance of the sensing elements in the FMEA analysis and shows, also with a practical example, how they can be taken into account.

Sergey V Muravyov, Ekaterina Y Pogadaeva
Recognition Ability of Interval Fusion with Preference Aggregation in Weld Defects Images Analysis

This paper describes a potential applicability of the interval fusion with preference aggregation (IF&PA) approach to the weld image segmentation as the key stage in recognizing a welding joint defective region. In the proposed method, the weld image is divided into a series of equal horizontal bands. For each band, an intensity histogram is plotted, using which the lower and upper bounds are defined for the intervals, which are expected to characterize the defect (foreground) and defect-free (background) areas. The intervals are represented by inrankings forming the foreground and background preference profiles. The Kemeny ranking algorithm is applied to the two profiles in order to determine the best representative points values (in RGB code) of the foreground and background areas. The values serve then as seed parameters of the region growing algorithm applied to distinguish defect and background regions during the segmentation. This approach was tested in segmenting a number of typical weld defect images. The experimental results showed that the proposed approach allows to accurately separate

Angéla Váradiné Szarka, Sándor Tollár, Sándor Fegyverneki
Development of Lifetime Testing Methods for Increased Requirements of Coolant Pumps in Electrical Vehicles

Increasing market of electrical cars pushes part suppliers to fulfil new requirements and meet new challenges. This paper focuses to the increased lifetime requirements of parts in electrical vehicles. Component developers and suppliers are facing to difficulties of lifetime testing, as the increased periods are too long for practical tests. New accelerated endurance testing and mathematical estimation methods are required for reliable lifetime estimation. Research team of the University of Miskolc works on development of new lifetime estimation method for bush bearings of coolant pumps in electrical vehicles in cooperation with its industrial partners. Lifetime requirement for these parts is increased to 30.000 hours which makes no possible the traditional endurance testing at all. In the paper acceleration parameters and endurance test stands developed for defining practical abrasion curves are summarized. Mathematical model of lifetime estimation and two new methods for accelerated lifetime testing for industrial use are described.

Robert Glawar, Fazel Ansari, Zsolt János Viharos, Kurt Matyas, Wilfried Sihn
A cost-based model for integrating maintenance strategies in autonomous production control

Autonomous production control (APC) is able to deal with challenges, inter alia, high delivery accuracy, shorter planning horizons, increasing product and process complexity, and frequent changes. However, several state-of-the-art approaches do not consider maintenance factors contributing to operational and tactical decisions in production planning and control. The incomprehensiveness of the decision models and related decision support tools cause inefficiency in production planning and thus lead to a low acceptance in the manufacturing enterprises. To overcome this challenge, this paper presents a conceptual model for integrating different maintenance strategies in autonomous production control. The model provides relevant decision aspects and a cost function for a market-based approach.

Eckart Uhlmann, Julian Polte, Claudio Geisert
Condition Monitoring Concept for Industrial Robots

Industrial robots are used in production technology for a wide variety of tasks. The most frequently used type worldwide is the so-called vertical articulated arm robot, often designed with 5 or 6 axes. Due to their relative movement, the axes are tribological systems, they are subject to wear and tear and must be maintained regularly. An important aspect of maintenance is the inspection, which aims to assess the current state of wear and tear. This paper presents a concept for condition monitoring by means of selftests for industrial robots. The basis is formed by MEMS-based vibration sensors, which are mounted on the axis joints. The vibration signals acquired during the self-test are analyzed in an Edge Gateway and the condition is classified using methods from the field of machine learning. The result of the classification and the features used for it are then sent to a cloud platform where they can be further analyzed. With this approach, service calls can be planned in advance and unplanned downtimes avoided. The article concludes with a critical discussion of the advantages and disadvantages of the presented concept and gives an outlook on still open research questions.

Dániel Erdősy, Tamás Bodolai, Angéla Váradiné Szarka
Analysing and simulating electronic devices as antennas

Electromagnetic compatibility is getting more and more interest as soon we are speaking about electronic devices. EMC must be planned, calculated, simulated, and measured to ensure that the device can work in the environment of other devices and does not disturb other devices by unwanted interference. In complex devices, simulation methods due to the large number of elements and complicated operations can be extremely costly and time consuming. We should consider how is it possible to reduce these costs, for example with simplifying our simulation terminology. This paper explains a general theory about using premeasured EMC properties in simulation to find out how a complex system (containing many devices) will work out. This theory is about how different radiated electromagnetic waves have effects on other devices. Electromagnetic compatibility (EMC) standards have been developed to test and limit unintentional coupling to and from electronic devices (interference), but not developed to deal with more devices crowded in a smaller environment. If we handle the different devices as antennas, in the simulation environment we must not deal with their internal structure and operation (till we need information of the inside). This way simulation time can be drastically reduced.

Maik Frye, Robert Heinrich Schmitt
Structured Data Preparation Pipeline for Machine Learning-Applications in Production

The application of machine learning (ML) is becoming increasingly common in production. However, many ML-projects fail due to the existence of poor data quality. To increase its quality, data needs to be prepared. Through the consideration of versatile requirements, data preparation (DPP) is a challenging task, while accounting for 80 % of ML-projects duration [1]. Nowadays, DPP is still performed manually and individually making it essential to structure the preparation in order to achieve highquality data in a reasonable amount of time. Thus, we present a holistic concept for a structured and reusable DPP-pipeline for ML-applications in production. In a first step, requirements for DPP are determined based on project experiences and detailed research. Subsequently, individual steps and methods of DPP are identified and structured. The concept is successfully validated through two production use-cases by preparing data sets and implementing ML-algorithms.

Loredana Cristaldi, Michy Alice, Enrico Ragaini
Prognostic and Health Management using Copula Correlation: a Power System application

A copula is a function that joins multivariate distribution functions to their margins (i.e. marginal distribution functions). Copulas are widely used in finance and economics for time series analysis and approaches based on them have found application in engineering as well, typically in civil and reliability engineering. In this paper, two data-driven prognostic algorithms based on copula application to power systems are proposed. The first is related to the estimation of the Remaining Useful Life (RUL) of a product, and the second aims at evaluating the performance and predict the behavior of an energy-consuming load. Obtained results are encouraging and candidate this approach as a useful method in Prognostics and Health Management (PHM) and energy monitoring applications.

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