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  • 1.
    Haidong, Shao
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Junsheng, Cheng
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Hongkai, Jiang
    School of Aeronautics, Northwestern Polytechnical University, Xi’an, China.
    Yu, Yang
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Zhantao, Wu
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
    Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing2020Inngår i: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 188, artikkel-id 105022Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Early fault prognosis of bearing is a very meaningful yet challenging task to improve the security of rotating machinery. For this purpose, a novel method based on enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy is proposed in this paper. First, complex wavelet packet energy moment entropy is defined as a new monitoring index to characterize bearing performance degradation. Second, deep gated recurrent unit network is constructed to capture the nonlinear mapping relationship hidden in the defined monitoring index. Finally, a modified training algorithm based on learning rate decay strategy is developed to enhance the prognosis capability of the constructed deep model. The proposed method is applied to analyze the simulated and experimental signals of bearing. The results demonstrate that the proposed method is more superior in sensibility and accuracy to the existing methods.

  • 2.
    He, Zhiyi
    et al.
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University.
    Shao, Haidong
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University.
    Wang, Ping
    AECC Hunan Aviation Powerplant Research Institute. AECC Key Laboratory of Aero-engine Vibration Technology.
    Lin, Jing (Janet)
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Cheng, Junsheng
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University.
    Yang, Yu
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University.
    Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples2020Inngår i: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 191, artikkel-id 105313Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Lack of typical fault samples remains a huge challenge for intelligent fault diagnosis of gearbox. In this paper, a novel approach named deep transfer multi-wavelet auto-encoder is presented for gearbox intelligent fault diagnosis with few training samples. Firstly, new-type deep multi-wavelet auto-encoder is designed for learning important features of the collected vibration signals of gearbox. Secondly, high-quality auxiliary samples are selected based on similarity measure to well pre-train a source model sharing similar characteristics with the target domain. Thirdly, parameter knowledge acquired from the source model is transferred to target model using very few target training samples. Transfer diagnosis cases for different fault severities and compound faults of gearbox confirm the feasibility of the proposed approach even if the working conditions have significant changes.

  • 3.
    Jones, Richard W.
    et al.
    Luleå tekniska universitet.
    Lowe, Andrew
    Ilixir Ltd.
    Harrison, M.J.
    Auckland Hospital.
    A framework for intelligent medical diagnosis using the theory of evidence2002Inngår i: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 15, nr 1-2, s. 77-84Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In designing fuzzy logic systems for fault diagnosis, problems can be encountered in the choice of symptoms to use fuzzy operators and an inability to convey the reliability of the diagnosis using just one degree of membership for the conclusion. By turning to an evidential framework, these problems can be resolved whilst still preserving a fuzzy relational model structure. The theory of evidence allows for utilisation of all available information. Relationships between sources of evidence determine appropriate combination rules. By generating belief and plausibility measures it also communicates the reliability of the diagnosis, and completeness of information. In this contribution medical diagnosis is considered using the theory of evidence, in particular the diagnosis of inadequate analgesia is considered

  • 4.
    Perera, Charith
    et al.
    Centre for Research in Computing, The Open University, Milton Keynes.
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    A Knowledge-Based Resource Discovery for Internet of Things2016Inngår i: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 109, s. 122-136Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In the sensing as a service paradigm, Internet of Things (IoT) Middleware platforms allow data consumers to retrieve the data they want without knowing the underlying technical details of IoT resources (i.e. sensors and data processing components). However, configuring an IoT middleware platform and retrieving data is a significant challenge for data consumers as it requires both technical knowledge and domain expertise. In this paper, we propose a knowledge driven approach called Context Aware Sensor Configuration Model (CASCOM) to simplify the process of configuring IoT middleware platforms, so the data consumers, specifically non-technical personnel, can easily retrieve the data they required. In this paper, we demonstrate how IoT resources can be described using semantics in such away that they can later be used to compose service work-flows. Such automated semantic-knowledge-based IoT resource composition approach advances the current research. We demonstrate the feasibility and the usability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized to any other middleware platform.

  • 5.
    Zhang, Liangwei
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Karim, Ramin
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Adaptive Kernel Density-based Anomaly Detection for Nonlinear Systems2018Inngår i: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 139, nr 1, s. 50-63Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents an unsupervised, density-based approach to anomaly detection. The purpose is to define a smooth yet effective measure of outlierness that can be used to detect anomalies in nonlinear systems. The approach assigns each sample a local outlier score indicating how much one sample deviates from others in its locality. Specifically, the local outlier score is defined as a relative measure of local density between a sample and a set of its neighboring samples. To achieve smoothness in the measure, we adopt the Gaussian kernel function. Further, to enhance its discriminating power, we use adaptive kernel width: in high-density regions, we apply wide kernel widths to smooth out the discrepancy between normal samples; in low-density regions, we use narrow kernel widths to intensify the abnormality of potentially anomalous samples. The approach is extended to an online mode with the purpose of detecting anomalies in stationary data streams. To validate the proposed approach, we compare it with several alternatives using synthetic datasets; the approach is found superior in terms of smoothness, effectiveness and robustness. A further experiment on a real-world dataset demonstrated the applicability of the proposed approach in fault detection tasks.

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