A data similarity based analysis to consequential alarms of industrial processes |
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Institution: | 1. Noah’s Ark Lab, Huawei Technologies, Hong Kong SAR, China;2. Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China;3. Caritas Institute of Higher Education, Hong Kong SAR, China |
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Abstract: | The analysis of consequential alarms is beneficial to avoiding alarm flooding and finding out root alarms in an industrial process. In this context, a novel similarity computation method taking into account of correlation delays between process alarms is introduced firstly. Subsequently, the Granger causality method is suggested to further clarify mutual impacts of similar alarm variables based on process data. Through the combination of alarm data similarity analysis and process data causality analysis, the consequential alarms can be effectively identified along with their evolution paths. An industrial case is employed to illustrate the benefits of the contribution. |
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Keywords: | Consequential alarms Data similarity Causality Alarm flooding |
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