Integration of Production Data in CM for Non-stationary Machinery: A Data Fusion Approach

被引:0
|
作者
Galar, Diego [1 ]
Morant, Amparo [1 ]
机构
[1] Lulea Univ Technol, Div Operat & Maintenance Engn, S-95187 Lulea, Sweden
关键词
process control; XML; cloud computing; CMMS; EAM; condition monitoring; asset;
D O I
暂无
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
A process control system deals with disperse information sources mostly related with operation and maintenance issues. For integration purposes, a data collection and distribution system based on the concept of cloud computing is proposed to collect data or information pertaining to the assets of a process plant from various sources or functional areas of the plant including, for example, the process control functional areas, the maintenance functional areas and the process performance monitoring functional areas. This data and information is manipulated in a coordinated manner by the cloud using XML for data exchange and is redistributed to other applications where is used to perform overall better or more optimal control, maintenance and business activities. From maintenance point of view, the benefit is that information or data may be collected by maintenance functions pertaining to the health, variability, performance or utilization of an asset. The end user, i.e. operators and maintainers are also considered. A user interface becomes necessary in order to enable users to access and manipulate the data and optimize plant operation. Furthermore, applications, such as work order generation applications may automatically generate work orders, parts or supplies orders, etc. based on events occurring within the plant due to this integration of data and creation of new knowledge as a consequence of such process
引用
收藏
页码:403 / 414
页数:12
相关论文
共 50 条
  • [1] Markovian Segmentation of Non-stationary Data Corrupted by Non-stationary Noise
    Habbouchi, Ahmed
    Boudaren, Mohamed El Yazid
    Senouci, Mustapha Reda
    Aissani, Amar
    [J]. ADVANCES IN COMPUTING SYSTEMS AND APPLICATIONS, 2022, 513 : 27 - 37
  • [2] Capital market integration in ASEAN: A non-stationary panel data analysis
    Chan, Kenneth S.
    Dang, Vinh Q. T.
    Lai, Jennifer T.
    [J]. NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE, 2018, 46 : 249 - 260
  • [3] A Fuzzy Clustering Approach to Non-stationary Data Streams Learning
    Abdullatif, A.
    Masulli, F.
    Rovetta, S.
    Cabri, A.
    [J]. ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, PT II, 2017, 10614 : 768 - 769
  • [4] Empirical distribution approach to the robustness measure for non-stationary data
    Raux, Guillaume
    Halverson, Don R.
    Lee, Hyeon-Cheol
    [J]. 2007 FOURTH INTERNATIONAL SYMPOSIUM ON WIRELESS COMMUNICATION SYSTEMS, VOLS 1 AND 2, 2007, : 592 - 596
  • [5] Modelling non-stationary 'Big Data'
    Castle, Jennifer L.
    Doornik, Jurgen A.
    Hendry, David F.
    [J]. INTERNATIONAL JOURNAL OF FORECASTING, 2021, 37 (04) : 1556 - 1575
  • [6] Combination of recognizers and fusion of features approach to missing data ASR under non-stationary noise conditions
    Joshi, Neil
    Guan, Ling
    [J]. 2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL IV, PTS 1-3, 2007, : 1041 - +
  • [7] Data Fusion for Enhanced Defect Detectability in Non-Stationary Thermal Wave Imaging
    Ghali, V. S.
    Suresh, B.
    Hemanth, A.
    [J]. IEEE SENSORS JOURNAL, 2015, 15 (12) : 6761 - 6762
  • [8] Does non-stationary spatial data always require non-stationary random fields?
    Fuglstad, Geir-Arne
    Simpson, Daniel
    Lindgren, Finn
    Rue, Harard
    [J]. SPATIAL STATISTICS, 2015, 14 : 505 - 531
  • [10] Longitudinal models for non-stationary exponential data
    Hasan, M. Tariqul
    [J]. IEEE TRANSACTIONS ON RELIABILITY, 2008, 57 (03) : 480 - 488