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.