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Abstract
Data collected by sensors has hidden value that can be used to infer valuable knowledge about the system, such as identifying faults in transmission or functioning faults in various system components. Solutions for exploring and exploiting data need to be developed to extract such knowledge. This paper shows how the identification of transmission regularities can be used to extract knowledge about the overall system state.The focus of this work is defining a methodology for detecting transmission periodicity. In our approach, we evaluated other strategies, addressed various limitations they have, and narrowed their utility on real-world data. We further expand the scope by defining strategies for the identification of transmission gaps and duplicates. Finally, we validate the algorithms on samples of real industrial data obtained from monitoring different parts of home appliances.