High-power industrial rolling mills rely heavily on the sustained operation of cycloconverters, a type of variable-frequency drive. This research proposes a methodology, which observes and diagnoses the operation of cycloconverters as either normal or abnormal by use of time-frequency signature analysis. Various features of the cycloconverter's input current in the time-frequency domain are identified and used to derive parameters that describe these two states in a quantitative manner. A reference model using the parameters is then developed, and comparisons in the time-frequency domain to real data are implemented. Based on these comparisons, a statistical decision boundary is delineated that is used to classify the health status of the cycloconverter.
|Number of pages||9|
|Journal||IEEE Transactions on Industrial Informatics|
|Publication status||Published - 2018 Oct|
Bibliographical noteFunding Information:
Manuscript received July 30, 2017; accepted August 28, 2017. Date of publication September 18, 2017; date of current version October 3, 2018. This work was supported under the framework of international co-operation program managed by National Research Foundation of Korea (NRF) #2017K1A4A3013579 and by the NRF grant funded by the Ministry of Science, ICT & Future Planning #NRF-2017R1A2A1A05001022. Paper no. TII-17-1686. (Corresponding author: Yong-June Shin.) T. M. Thompkins is with Triumph Integrated Systems, Seattle, WA 98053 USA (e-mail: email@example.com).
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Electrical and Electronic Engineering