A detection method for abnormal alarm data based on multivariate time series

Abstract

The invention discloses an abnormal alarm data detection method based on a multivariate time series. The abnormal alarm data detection method based on the multivariate time series comprises the steps that data of multiple correlated variables are extracted from historical data, the multivariate time series is established and standardized, and the symbol direction between the variables in the normal state is calculated; time series segmentation description based on key turning points is determined, the minimum time interval is set, and key turning point searching is conducted; the piecewise linearity of the multivariate time series is represented, a fitting error is determined according to the orthorhombic distance between a data point and each segment, a loss function threshold value is set, the number of the segments is optimized, and an optimized segmentation result is obtained; and based on the optimized segmentation result, correlation analysis is conducted on all the segments of the multivariate time series, the symbol direction between the segment variables is extracted, and abnormal data with the symbol direction inconsistent with the symbol direction in the normal state are detected. By adoption of the abnormal alarm data detection method based on the multivariate time series, favorable conditions are provided for designing of a dynamic alarm threshold value of a multivariable alarm system, and thus disturbance alarms are reduced.

Description

technical field [0001] The invention relates to a method for detecting abnormal alarm data based on multivariate time series. Background technique [0002] The alarm system plays a vital role in ensuring the safe production and high-efficiency operation of coal-fired generating units. Due to the mutual influence between related variables in the actual industrial process, the traditional single-variable alarm threshold design method may generate a large number of disturbing alarms (missing alarms). Alarms and false alarms) and lead to "too many alarms", which affects the attention of on-site operators and increases the difficulty of correct handling when abnormal production conditions occur. In order to realize the dynamic alarm threshold design of the multi-variable alarm system, it is necessary to find a detection method that automatically screens out the normal and abnormal data segments from the historical data. Contents of the invention [0003] In order to solve the ...

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Problems solved by technology

[0002] The alarm system plays a vital role in ensuring the safe production and high-efficiency operation of coal-fired generating units. Due to the mutual influence between related variables in the actual industrial process, the traditional single-variable alarm threshold design method may generate a large number of disturbing alarms (missing alarms). Alarms and false alarms) and lead to "too many alarms", which affects the attention of on-site operators and increases the difficulty of correct handling when abnormal production conditions occur

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