Condition monitoring data stream anomaly detection method based on improved gaussian process regression model

Abstract

The invention relates to a condition monitoring data stream anomaly detection method, in particular to a condition monitoring data stream anomaly detection method based on an improved gaussian process regression model. The problem that an existing method for processing monitoring data stream anomaly detection is poor in effect is solved. The method comprises the steps that firstly, the historical data sliding window size is determined; secondly, the types of a mean value function and a covariance function are determined; thirdly, the hyper-parameter initial value is set to be the random number from 0 to 1; fourthly, q data closest to the current time t are extracted; fifthly, the gaussian process regression model is determined; sixthly, prediction is conducted by means of the nature of the gaussian process regression model; seventhly, PI of normal data at the time t+1; eighthly, monitoring data are compared with the PI; ninthly, whether the real monitoring data need to be marked to be abnormal or not is judged; tenthly, beta (xt+1) corresponding to the monitoring value at the time t+1 is calculated; eleventhly, the real value or prediction value and the t+1 are added into DT; twelfthly, new DT is created. The condition monitoring data stream anomaly detection method based on the improved gaussian process regression model is applied in the field of network communication.

Description

technical field [0001] The invention relates to a method for abnormality detection of state monitoring data flow. Background technique [0002] As system complexity increases, it becomes increasingly important to use condition monitoring data to estimate equipment or system performance. Taking satellites as an example, the telemetry data generated during the satellite's in-orbit operation is the only basis for ground personnel to estimate the satellite's health status. Similarly, the monitoring data of the mining vehicle can also provide important reference information for the state estimation of the corresponding system or subsystem. In addition, compared with normal data, abnormal data often indicates abnormal events or potential failure information that may occur in the system, and abnormal data is more worthy of further analysis. Therefore, anomaly detection has attracted extensive attention of researchers in many fields, such as reliability, automatic testing, machine...

AI Technical Summary

Problems solved by technology

[0019] The present invention aims to solve the problem that the existing methods have low effect on abnormal detection of monitoring data flow, and provides an abnormal detection method of state monitoring data flow based on improved Gaussian process regression model

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