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  • 1.
    Catelani, Marcantonio
    et al.
    Department of Information Engineering, University of Florence, Florence, 50139, Italy.
    Ciani, Lorenzo
    Department of Information Engineering, University of Florence, Florence, 50139, Italy.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Industry and Transport Division, Tecnalia Research and Innovation, Miñano, 01510, Spain.
    Patrizi, Gabriele
    Department of Information Engineering, University of Florence, Florence, 50139, Italy.
    Optimizing Maintenance Policies for a Yaw System Using Reliability-Centered Maintenance and Data-Driven Condition Monitoring2020In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 69, no 9, p. 6241-6249Article in journal (Refereed)
    Abstract [en]

    System downtime and unplanned outages massively affect plant productivity; therefore, the reliability, availability, maintainability, and safety (RAMS) disciplines, together with fault diagnosis and condition monitoring (CM), are mandatory in energy applications. This article focuses on the optimization of a maintenance plan for the yaw system used in an onshore wind turbine (WT). A complete reliability-centered maintenance (RCM) procedure is applied to the system to identify which maintenance action is the optimal solution in terms of cost, safety, and availability. The scope of the research is to propose a new customized decision-making diagram inside the RCM assessment to reduce the subjectivity of the procedure proposed in the standard and save the cost by optimizing maintenance decisions, making the projects more cost-efficient and cost-effective. This article concludes by proposing a new diagnostic method based on a data-driven CM system to efficiently monitor the health and detect damages in the WT by means of measurements of critical parameters of the tested system. This article highlights how a reliability analysis, during the early phase of the design, is a very helpful and powerful means to guide the maintenance decision and the data-driven CM.

  • 2.
    Ge, Chenjie
    et al.
    Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden.
    de Oliveira, Roger Alves
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Gu, Irene Y-Hua
    Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden.
    Bollen, Math H. J.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Deep Feature Clustering for Seeking Patterns in Daily Harmonic Variations2021In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 70, article id 2501110Article in journal (Refereed)
    Abstract [en]

    This article proposes a novel scheme for analyzing power system measurement data. The main question that we seek answers in this study is on “whether one can find some important patterns that are hidden in the large data of power system measurements such as variational data.” The proposed scheme uses an unsupervised deep feature learning approach by first employing a deep autoencoder (DAE) followed by feature clustering. An analysis is performed by examining the patterns of clusters and reconstructing the representative data sequence for the clustering centers. The scheme is illustrated by applying it to the daily variations of harmonic voltage distortion in a low-voltage network. The main contributions of the article include: 1) providing a new unsupervised deep feature learning approach for seeking possible underlying patterns of power system variation measurements and 2) proposing an effective empirical analysis approach for understanding the measurements through examining the underlying feature clusters and the associated reconstructed data by DAE.

  • 3.
    Hostettler, Roland
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lundberg Nordenvaad, Magnus
    Division of Systems and Control, Uppsala University, Uppsala, Sweden.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    The pavement as a waveguide: modeling, system identification, and parameter estimation2014In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 63, no 8, p. 2052-2063Article in journal (Refereed)
    Abstract [en]

    This paper presents modeling of wave propagation in pavements from a system identification point of view. First, a model based on the physical structure is derived. Second, experiment design and evaluation are discussed and maximum-likelihood estimators for estimating the model parameters are introduced, assuming an error-in-variables setting. Finally, the proposed methods are applied to measurement data from two experiments under varying environmental conditions. It is found that the proposed methods can be used to estimate the dispersion curves of the considered waveguide and the results can be used for further analysis

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    FULLTEXT01
  • 4.
    Hyyppä, Kalevi
    Division of Plasma Physics, Royal Institute of Technology, Stockholm, Sweden.
    Dielectric absorption in memory capacitors1972In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. IM-21, no 1, p. 53-56Article in journal (Refereed)
    Abstract [en]

    Experience from experiments with a sample and hold circuit showed that dielectric absorption constitutes a fundamental limitation of the accuracy in analog memories. To gain more insight into the problem several materials commonly used as dielectrics in capacitors have been investigated. The theory of dielectric absorption is discussed. Results from measurements on capacitors with paper, cellulose acetate, parylene, polyester, polycarbonate, and polystyrene dielectrics are reported. The components in Dow's model, which describes the dielectric absorption, are calculated for the polycarbonate dielectric. A compensation circuit is suggested, which considerably reduces the effect of dielectric absorption in analog memories and electronic integrators.

  • 5.
    Nordin, Daniel
    et al.
    Luleå University of Technology.
    Hyyppä, Kalevi
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Single-stage photodiode op-amp solution suited for a self-mixing FMCW system2003In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 52, no 6, p. 1820-1824Article in journal (Refereed)
    Abstract [en]

    The current delivered by the photodiode in a self-mixing frequency modulated continuous wave or optical frequency domain reflectometry system consists of a dc-current resulting from the local oscillator, the reflected signal, dark current in the photodiode, and current generated from background light. The current also contains the useful harmonic signal with a beat frequency corresponding to the range and radial velocity of a target. To avoid saturation and clipping due to the dc current generated in the photodiode, it is desirable to minimize the gain at dc while maintaining a high gain in the beat frequency region. We have investigated some different solutions and present a modified current-to-voltage converter using bootstrapping and added voltage gain, which addresses this problem using only one OP-amp and no dc shorting inductors.

    Download full text (pdf)
    fulltext
  • 6.
    Putra, S. A.
    et al.
    Telkom Univ, Cybernet Res Grp, Sch Ind & Syst Engn, Enterprise Intelligent Syst Lab, Bandung 40257, Indonesia.
    Trilaksono, B. R.
    Inst Teknol Bandung ITB, Bandung 40132, Indonesia.
    Riyansyah, M.
    Inst Teknol Bandung ITB, Bandung 40132, Indonesia.
    Laila, Dina Shona
    Coventry Univ, Sch Mech Aerosp & Automot Engn, Coventry, W Midlands, England; Univ Teknol Brunei, Fac Engn, Elect & Elect Engn Dept, Bandar Seri Begawan BE1410, Brunei.
    Multiagent Architecture for Bridge Capacity Measurement System Using Wireless Sensor Network and Weight in Motion2021In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 70, article id 2502714Article in journal (Refereed)
  • 7.
    Salles, Rafael S.
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    de Oliveira, Roger Alves
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Rönnberg, Sarah K.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Mariscotti, Andrea
    Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy.
    Analytics of Waveform Distortion Variations in Railway Pantograph Measurements by Deep Learning2022In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 71, article id 2516211Article in journal (Refereed)
    Abstract [en]

    Waveform distortion in general represent a widespread problem in electrified transports due to interference, service disruption, increased losses and ageing of components. Given the multitude of moving sources and the extremely variable operating conditions, short time records must be considered for analysis, and this increases in turn its complexity, from which the need for effective automated processing, as offered by a deep learning (DL) approach. This paper proposes an application of unsupervised DL to measurements of railway pantograph quantities to identify waveform distortion patterns. Data consists of pantograph current from a Swiss 15 kV 16.7 Hz railway system. Three DL input types are considered: waveforms, harmonic spectra, and supraharmonic spectra. The applied DL method applied is the deep autoencoder (DAE) followed by feature clustering, using techniques to define a suitable number of clusters. Short-term distortion is evaluated over sub-10 min intervals of harmonic and supraharmonic spectra down to sub-second intervals. Results are explained among others by connecting the distribution of the clusters (determined by self-supervised method) to the dynamic operating conditions of the rolling stock. Resulting DAE performance are superior in terms of accuracy and comprehensiveness of spectral components compared to a more traditional principal component analysis (PCA) that was chosen as reference for comparison.

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