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.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Electrical and Electronic Engineering