Industrial Measurement Systems Research Articles

Technological mediation, mediating morality and moral imaginaries of design: Performance measurement systems in the pharmaceutical industry

In this paper, we seek to understand the ethics of accounting technology design. We commence by working with the concept of technological mediation, which is theorizing how technologies steer actions by evoking given behaviours and by contributing to perceptions and interpretations of reality that form the basis for choices and decisions to act. As such, this relation between people and technologies has important ethical consequences since it implies that technologies contribute actively to how humans do ethics. In this paper, we draw upon a postphenomenological approach (Ihde; Verbeek) to study and to theorise the moral mediations brought about by accounting technology, by examining how, in its design, technology can actively mediate the moral choices and guide the moral actions human beings make. Our central research question is ‘how is morality mediated in the design of accounting technologies?‘. This question is explored through an ethnographic study of the design of a new Performance Measurement and Compensation System in the Italian division of a multi-national pharmaceutical company. We offer two main contributions towards answering this question. First, by working within the theory of technological mediation, we develop the concept of a ‘moral imaginary’ as an approach to understanding designing the morality of things. Second, we elaborate a process model to theorise how moral mediations unfold in the design of accounting technology. From our conceptual motivation and the theoretical elaboration it inspired, we illuminate how the design of accounting technologies, in this case a PMS system, is a form of ‘engineering ethics’ through techno-moral mediation.

Industrial measurement system with LoRa interface

Industrial measurement system with LoRa interface

Vehicle Activity Dataset: A Multimodal Dataset to Understand Vehicle Emissions with Road Scenes for Eco-Routing

In the field of smart mobility, Artificial Intelligence (AI) approaches are influential and can make a highly beneficial contribution. Our project aims to develop a real-time ecological map of road traffic. This map will allow electric vehicles (EVs) and thermal vehicles (TVs) to display the cost of energy consumption and CO2 emissions on different road sections. In urban environments, road traffic emissions are a significant contributor to environmental pollution, with vehicle emissions being a major component. Addressing these impacts requires a thorough understanding of the operational behavior of vehicles on different road infrastructures within the region. This paper presents a novel, comprehensive dataset, the Vehicle Activity Dataset (VAD), designed to assess the emissions and fuel consumption characteristics of vehicles about their actual operating environment. Constructed from a large number of real-world driving scenarios, VAD incorporates emission data collected by an industrial Portable Emission Measurement System (PEMS), road scenes captured by an RGB camera, and the detection of different object classes within these images. The primary objective of VAD is to provide a comprehensive understanding of the relationship between vehicle emissions and the diverse range of objects present on the road. Experimental results in real road traffic environments through different studies demonstrate the robustness of the developed dataset.

Model of Performance Measurement and Management System in “The Visegrad Group”

This article summarizes the model of the performance measurement and management systems in Industry 4.0. in the Visegrad Group (V4) countries. The main goal of the research is to suggest a Performance model and management system model (PMMS). Methods of research were focused on using the questionnaires method with the Dillman method and five research questions (RQ1-RQ5). The object of research comprised of 60 industrial V4 companies with the direction of law form, count of the employee, revenues, and type of industry. The return of the questionnaire was 49 respondents of 60 respondents - companies. 42 companies monitor financial performance and 40 companies’ employee performance. Information sources for measuring performance are information systems that are use 39 companies and 39 companies use financial statements. 19 companies use less than 10 KPIs, and 28 companies monitor KPIs throughout the year. The new model of performance measurement and management system in Industry 4.0 is a competitive advantage in the global market and in the hyper-competitiveness environment.

Bridging the gap between AI and the industry — A study on bearing fault detection in PMSM-driven systems using CNN and inverter measurement

Deep-learning-based identification and diagnosis of faults in industrial assets has received much attention. It does not need a deep understanding of the target domain or complex signal-processing techniques for time-consuming feature identification and selection. The vast majority of approaches developed in this field rely on public data recorded in laboratory conditions with high-performance measurement equipment. Moreover, primarily vibration data is used for condition monitoring tasks, which is particularly sensitive to typical error patterns in rotating machinery. These conditions are difficult to maintain in industrial environments since using high-performance measurement systems would not be economically feasible. This paper demonstrates that modern deep learning can achieve extraordinary fault detection results with industrial standard low-cost measurement systems. We determine measurements taken from the industrial-sensory equipment that potentially capture the system’s fault patterns. Hereafter, we apply traditional failure detection and identification techniques to evaluate the suitability of the taken measurements for bearing fault detection. These techniques apply envelope analysis to reveal the fault patterns in the signals and a support vector machine to separate the fault classes. We can conclude that the used low-cost sensory equipment is capable to capture meaningful, fault-describing patterns. To improve the failure detection performance we are investigating a 2D, as well as 1D, convolutional neural network approach to identify the error patterns and classify the respective errors. We compare the deep-learning-based methods with the traditional methods. Furthermore, we assess which inverter signal carries the largest fault-describing information content. Experimental results indicate that the proposed deep learning methods outperform traditional fault diagnosis methods, hence, demonstrating the effectiveness in an industrial condition monitoring application.

Learning model for battery lifetime prediction of LoRa sensors in additive manufacturing

Today, an innovative leap for wireless sensor networks, leading to the realization of novel and intelligent industrial measurement systems, is represented by the requirements arising from the Industry 4.0 and Industrial Internet of Things (IIoT) paradigms. In fact, unprecedented challenges to measurement capabilities are being faced, with the ever-increasing need to collect reliable yet accurate data from mobile, battery-powered nodes over potentially large areas. Therefore, optimizing energy consumption and predicting battery life are key issues that need to be accurately addressed in such IoT-based measurement systems. This is the case for the additive manufacturing application considered in this work, where smart battery-powered sensors embedded in manufactured artifacts need to reliably transmit their measured data to better control production and final use, despite being physically inaccessible. A Low Power Wide Area Network (LPWAN), and in particular LoRaWAN (Long Range WAN), represents a promising solution to ensure sensor connectivity in the aforementioned scenario, being optimized to minimize energy consumption while guaranteeing long-range operation and low- cost deployment. In the presented application, LoRa equipped sensors are embedded in artifacts to monitor a set of meaningful parameters throughout their lifetime. In this context, once the sensors are embedded, they are inaccessible, and their only power source is the originally installed battery. Therefore, in this paper, the battery lifetime prediction and estimation problems are thoroughly investigated. For this purpose, an innovative model based on an Artificial Neural Network (ANN) is proposed, developed starting from the discharge curve of lithium-thionyl chloride batteries used in the additive manufacturing application. The results of experimental campaigns carried out on real sensors were compared with those of the model and used to tune it appropriately. The results obtained are encouraging and pave the way for interesting future developments.

Practical Online Characterization of the Properties of Hydrocracking Bottom Oil via Near-Infrared Spectroscopy

Providing real-time information on the chemical properties of hydrocracking bottom oil (HBO) as the feedstock for ethylene cracker while minimizing processing time, is important to improve the real-time optimization of ethylene production. In this study, a novel approach for estimating the properties of HBO samples was developed on the basis of near-infrared (NIR) spectra. The main noise and extreme samples in the spectral data were removed by combining discrete wavelet transform with principal component analysis and Hotelling’s T2 test. Kernel partial least squares (KPLS) regression was utilized to account for the nonlinearities between NIR data and the chemical properties of HBO. Compared with the principal component regression, partial least squares regression, and artificial neural network, the KPLS model had a better performance of obtaining acceptable values of root mean square error of prediction (RMSEP) and mean absolute relative error (MARE). All RMSEP and MARE values of density, Bureau of Mines correlation index, paraffins, isoparaffins, and naphthenes were less than 1.0 and 3.0, respectively. The accuracy of the industrial NIR online measurement system during consecutive running periods in predicting the chemical properties of HBO was satisfactory. The yield of high value-added products increased by 0.26 percentage points and coil outlet temperature decreased by 0.25 °C, which promoted economic benefits of the ethylene cracking process and boosted industrial reform from automation to digitization and intelligence.

Metrological Evaluation of Robust Relaxation-Oscillator Interface for Remote Resistive Sensors and Its Application Toward Realizing Few Industrial Measurement Systems

Metrological Evaluation of Robust Relaxation-Oscillator Interface for Remote Resistive Sensors and Its Application Toward Realizing Few Industrial Measurement Systems

Speed Measurement Method for Moving Conductors Based on Motion-Induced Eddy Current

Speed measurement plays an important role in many measurement and control systems in industry. A simple and efficient speed measurement method for smooth conductive specimens based on motion-induced eddy currents is studied in this paper. The speed sensor consists of a magnet and a Hall element. When the conductor moves beneath the magnet, eddy currents are generated, which further generate a secondary magnetic field that is dependent on the moving speed of the conductor. The Hall element picks up this field to determine the relative speed between the conductor and the sensor. First of all, a 2D analytical model for the induced eddy currents and corresponding magnetic fields has been proposed by solving the Maxwell’s equations using spatial Fourier transforms. Then, the change in current density and magnetic field with conductor speed is analyzed using both the analytical model and the finite element method. Speed sensing experiments were conducted on a rotating conductive disk, the results showed good agreement with the proposed analytical model. Finally, specifications such as accuracy, repeatability, linearity, and resolution of the prototype sensor have been tested.

Industrial Performance Measurement Systems Coherence: A Comparative Analysis of Current Methodologies, Validation and Introduction to Key Activity Indicators

This paper discusses and integrates the concept of complexity in the industrial performance measurement and management systems (PMM) theory, providing a comprehensive overview of the different methodologies used within the decision systems research area. It also discusses the importance of introducing Key Activity Indicators (KAI) within PMM, specifically related to the Operations and Supply Chain management research and industrial areas. Moreover, it provides validation of the methodology through a case study concerning the production environment of a multinational pharmaceutical company. The main research objective is to design appropriate industrial PMM systems with the aim of increasing the industrial efficiency and effectiveness of manufacturing and service organizations. An analysis of the central industrial performance measurement systems design methods is conducted, classifying them into macro-categories and conducting a comparative study. Based on the analysis of the different proposed methods, organisations will be able to choose the best one based on their needs to design effective decision systems. The research work allows organisations to evaluate, assess, and design effective industrial performance measurement systems. Moreover, the proposed methodology can be easily integrated within an Industry 4.0 context, and benefit from the digitalization environment to obtain continuous feedback on the effectiveness of the industrial PMM.

Application of combinatorial optimization algorithm in industrial robot hand eye calibration

In order to improve the measurement accuracy of laser profiler and industrial robot measurement system and reduce the influence of random error in calibration process, a hand eye calibration method based on combinatorial optimization is proposed. Firstly, the laser profiler is positioned by the robot to scan a standard sphere from different postures. Then, combinations from all the measurement data are selected to calculate several transformation matrices and the corresponding profile datasets in base coordinate system can be derived consequently. Since all the profile data come from the same stationary standard sphere, sphere fitting is applied to each dataset and the optimal hand eye transformation matrix can be derived which corresponds to the minimum fitting error. Experimental results show that the novel method can effectively improve the calibration accuracy and sphere loss which is weighted sum of sphere fitting error and radius error is reduced from 1.976 to 0.539 after combinatorial optimization.

A multidimensional scorecard of KPIs for retrofit measures of buildings: A systematic literature review

The debate on retrofit of buildings has flourished in recent years and it has raised the interest of a growing number of researchers and practitioners. However, current research still lacks a holistic and systemic approach for measuring building retrofitting. The paper presents the results of a systematic literature review on the current state-of-the-art of retrofit measures and their related KPIs for buildings, with the aim to develop a comprehensive and integrated scorecard. In particular, the paper considers several perspectives of building retrofitting, spanning from the areas of sustainability performance to measurement systems in the building industry, and presents and discusses a multidimensional scorecard of KPIs that clusters—from an economic, environmental, and social perspective—the current state-of-the-art of retrofit measures on buildings. Further avenues for research in this field are illustrated and discussed.

A Comprehensive Review on Time Sensitive Networks with a Special Focus on Its Applicability to Industrial Smart and Distributed Measurement Systems.

The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramatically reshaped the requirements for control and communication systems within the factory systems of the future. The aforementioned technological revolution strongly affects industrial smart and distributed measurement systems as well, pointing to ever more integrated and intelligent equipment devoted to derive accurate measurements. Moreover, as factory automation uses ever wider and complex smart distributed measurement systems, the well-known Internet of Things (IoT) paradigm finds its viability also in the industrial context, namely Industrial IoT (IIoT). In this context, communication networks and protocols play a key role, directly impacting on the measurement accuracy, causality, reliability and safety. The requirements coming both from Industry 4.0 and the IIoT, such as the coexistence of time-sensitive and best effort traffic, the need for enhanced horizontal and vertical integration, and interoperability between Information Technology (IT) and Operational Technology (OT), fostered the development of enhanced communication subsystems. Indeed, established technologies, such as Ethernet and Wi-Fi, widespread in the consumer and office fields, are intrinsically non-deterministic and unable to support critical traffic. In the last years, the IEEE 802.1 Working Group defined an extensive set of standards, comprehensively known as Time Sensitive Networking (TSN), aiming at reshaping the Ethernet standard to support for time-, mission- and safety-critical traffic. In this paper, a comprehensive overview of the TSN Working Group standardization activity is provided, while contextualizing TSN within the complex existing industrial technological panorama, particularly focusing on industrial distributed measurement systems. In particular, this paper has to be considered a technical review of the most important features of TSN, while underlining its applicability to the measurement field. Furthermore, the adoption of TSN within the Wi-Fi technology is addressed in the last part of the survey, since wireless communication represents an appealing opportunity in the industrial measurement context. In this respect, a test case is presented, to point out the need for wirelessly connected sensors networks. In particular, by reviewing some literature contributions it has been possible to show how wireless technologies offer the flexibility necessary to support advanced mobile IIoT applications.

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review.

The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.

DESIGN OF AN AUTOMATED SYSTEM FOR MEASURING CAR BODIES

The work presents and describes selected measurement methods of bodywork geometry. The principle of operation of 3D scanners has been described together with their application in the implementation of vision measurement systems in the automotive industry. The paper also presents methods used to automate measurements in diagnostics and vehicle repair processes. In addition, the work presents the concept of a measurement system which combines the ideas of vision measurements and mechanical repair devices.

Application of laser tracker in the industrial measurement field

In recent years, with the rapid development of modern three-dimensional space technology, laser tracker has become a mature technology, high efficiency, stable and reliable performance of high-precision large-size measurement instruments in the industrial measurement system. In some major industries, the 3d high-precision measurement method represented by laser tracker has gradually replaced the traditional measurement method and is widely used in the installation and detection of large precision equipment. This paper mainly introduces the structure, working principle, advantage, and laser tracker applications in main industries.

Verification of a Capacitive Voltage Divider With 6-μrad Uncertainty Up To 100 kV

A reference setup for system calibration of industrial transformer loss measurement (TLM) systems consists of three main components, a voltage divider, a current transformer, and a power meter, and their phase displacements should each not be more than 10 $\mathrm {\mu }$ rad to achieve an overall system uncertainty of better than 20 $\mathrm {\mu }\text{W}$ /VA. We have extensively verified the uncertainty level of the current-comparator-based capacitive voltage divider (CVD) used in the TLM reference setup of the Van Swinden Laboratorium (VSL), both from the component level and the system as a whole. Different practical conditions relevant for on-site measurements are considered, e.g., measurement cable lengths, cable types, and grounding. The verification measurement results show an agreement of better than $(6 \pm 6) \times 10^{-6}$ in ratio error and $(4 \pm 6)~\mathrm {\mu }$ rad in phase displacement between the CVD component and system calibrations up to 100 kV. Requirements for achieving this agreement are adequate grounding of the CVD and the use of triax cable between the high-voltage (HV) capacitor and the CVD low-voltage electronics in case large distances have to be covered on-site. The $(4 \pm 6)~\mathrm {\mu }$ rad agreement in phase displacement is well within the required 10 $\mathrm {\mu }$ rad limit for voltage measurements as part of on-site TLM system calibrations with 20 $\mathrm {\mu }\text{W}$ /VA overall uncertainty at low power factors.

Measurement and inspection of electrical discharge machined steel surfaces using deep neural networks

We propose an industrial measurement and inspection system for steel workpieces eroded by electrical discharge machining, which uses deep neural networks for surface roughness estimation and defect detection. Specifically, a convolutional neural network (CNN) is used as a regressor in order to obtain steel surface roughness and a CNN based on spatial pooling pyramid is applied for defect classification. In addition, a new method for the region of interest selection based on morphological reconstruction and mean shift filtering is proposed for defect detection and localization. The regressor and classifier based on deep neural networks proposed here outperform state-of-the-art methods using handcrafted feature extraction. We achieve a mean absolute percentage error of 7.32% on roughness estimation; on defect detection, our approach yields an accuracy of 97.26% and an area under the ROC curve metric of 99.09%.

실시간질량분석장치 (SIFT-MS) 장착 이동측정시스템에 의한 산업단지 유해대기오염물질 오염도 측정 연구

실시간질량분석장치 (SIFT-MS) 장착 이동측정시스템에 의한 산업단지 유해대기오염물질 오염도 측정 연구

Reconfigurable Wide Input Range, Fully-Differential Indirect Current-Feedback Instrumentation Amplifier with Digital Offset Calibration for Self-X Measurement Systems

Abstract This manuscript presents an implementation of the configurable indirect current-feedback instrumentation amplifier (CFIA) for sensor interface readout circuit. Configuration is achieved by designing digital weighted scalable arrays for some selected elements to serve as tuning knobs controlled by the evolutionary optimization algorithm. This scheme resulting in a programmable circuit for different aspects to support self-x functionality. The robustness and flexibility of the proposed circuit fit to the demands of measurement and sensory systems in industry 4.0 and other intelligent systems applications. The circuit is designed by Cadence tools using ams 0.35 μm CMOS technology.