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Association rule mining method for multivariate time series monitoring data

A multivariate time series and time series technology, which is applied in the fields of electrical digital data processing, digital data information retrieval, special data processing applications, etc.

Pending Publication Date: 2020-04-21
CHONGQING UNIV
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

As far as complex engineering systems are concerned, there are many limitations in this regard, and people cannot accurately analyze and describe the mechanism for each system object that generates time series

Method used

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  • Association rule mining method for multivariate time series monitoring data
  • Association rule mining method for multivariate time series monitoring data
  • Association rule mining method for multivariate time series monitoring data

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Embodiment

[0104] The present invention selects data collected by various sensors in an industrial system as an example object. Due to the complex composition of industrial systems and the mutual influence between different components. A change in the parameters of a certain component is likely to affect other components and cause the entire industrial system to fail. Serious ones are likely to directly threaten the safety of life and property. However, it is difficult to intuitively discover the relationship existing in the data collected by different sensors in the first place, so as to detect problems early. Therefore, this patent provides a time characteristic association rule mining method of multivariate time series, which can effectively solve this problem.

[0105] like figure 1 As shown, a time-characteristic association rule mining method for multivariate time series of data collected by various sensors in an industrial system includes the following steps:

[0106] S1: Calc...

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Abstract

The invention discloses an association rule mining method for multivariate time series monitoring data. Objects come from a complex engineering system. The method comprises the following steps: firstly, determining an association relationship of time sequences through a time sequence correlation measurement method and clustering the time sequences of the association relationship; secondly, determining an abnormal value according to the relative change rate of the time sequence; determining an abnormal trend by setting an observation window; determining abnormal distribution through local datadensity, determining abrupt change points through linear fitting, finally, segmenting the sequence through trend, carrying out pattern representation on the time sequence through the slope of a fitting straight line and trend duration, setting the format of a time characteristic rule, and completing association rule mining through a frequent pattern.

Description

technical field [0001] The invention relates to the technical field of signal processing and automation, in particular to a method for mining multivariate time series association rules. Background technique [0002] A large number of time series data exist in the monitoring of industrial operation process. It is a kind of dynamic series that records the changing state of objects, directly or indirectly reflects the changing law of a certain phenomenon, and its time characteristics will be used as important indicators to reveal the operating status of system objects. and source. Analysis of time series through data mining techniques such as regression, statistics, fitting, clustering, correlation analysis, and pattern analysis can achieve the purposes of situation description, abnormal discovery, and working condition prediction of system objects in the dynamic process. [0003] Multivariate time series analysis is an important technique of interest in recent years for compl...

Claims

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Application Information

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IPC IPC(8): G06F16/2458G06F16/248
CPCG06F16/2465G06F16/2474G06F16/2477G06F16/248
Inventor 张可柴毅彭志杰宋鑫
Owner CHONGQING UNIV
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