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Abnormal alarm data detection method based on multivariate time series

A multivariate time series and abnormal alarm technology, which is applied to engine components, machines/engines, mechanical equipment, etc., can solve the problems of on-site operators' attention, missed alarms, and multiple alarms

Active Publication Date: 2017-02-01
SHANDONG UNIV OF SCI & TECH
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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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  • Abnormal alarm data detection method based on multivariate time series
  • Abnormal alarm data detection method based on multivariate time series
  • Abnormal alarm data detection method based on multivariate time series

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Embodiment Construction

[0045] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0046] figure 1 It is a flow chart of the method for detecting abnormal data of an alarm system based on industrial historical data in the present invention.

[0047] Such as figure 1 Shown, a kind of alarm system abnormal data detection method based on industrial history data, comprises the following steps:

[0048] Step S1, extract the data of multiple related variables in the time t before the current working point from the historical data, establish a multivariate time series T', and standardize it into a time series T, and calculate the relationship between each variable under normal conditions sign direction;

[0049] Step S2, setting the minimum time interval δ, and searching for key turning points based on the multivariate time series T;

[0050] Step S3, optimizing the number of segments K based on the fitting error of the linear segment of...

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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 ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): F02B77/08
CPCF02B77/08
Inventor 王建东朱迪黄越杨子江
Owner SHANDONG UNIV OF SCI & TECH
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