A time series data anomaly detection method and system thereof

Abstract

The invention discloses a time-series data abnormality detection method and system thereof. The method includes receiving time-series data collected by one or more sensors arranged in a machine and taking the latest observed data in the time-series data as the data to be monitored; calculating the data to be monitored The relative outlier distance between each parameter in the time series and the corresponding parameters of the time series with the inherent length cached in the machine can further obtain the outlier data with the outlier flag; using the correlation between the parameter values ​​in the time series data with the inherent length, from the outlier The abnormal observation data is screened out from the group data and the abnormal parameters in the abnormal observation data are located. The invention improves the accuracy rate of anomaly detection of time series data and ensures the correctness of anomaly detection.

Description

technical field [0001] The invention belongs to the field of data processing, and in particular relates to a time series data anomaly detection method and system thereof. Background technique [0002] With the continuous development of sensing technology, more and more devices have realized the intelligence of devices by installing sensors. As time goes by, the data detected by the sensor forms a time series, that is, sequential data. Anomaly detection of time series data is an important basis and basis for early warning of equipment failure, anomaly location, and failure analysis. [0003] During the operation of the equipment, it usually produces two kinds of outlier data: 1) mutation data: a sudden change in the operation mode of the equipment will cause a sudden change in the sensing data, which is generated by the normal operation of the equipment; 2) abnormal data: During the operation of the equipment, one or several components fail, or the acquired data is abnormal...

AI Technical Summary

Problems solved by technology

[0004] The current anomaly detection of time series data mainly has the following shortcomings: 1) It cannot distinguish between mutation data and abnormal data

Both of these outlier data are considered abnormal data in most anomaly detection methods, leading to abnormal false positives

2) Unable to locate abnormal parameters

Some anomaly detection methods can only detect anomalies, but cannot locate the abnormal parameters

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