Multidimensional data anomaly detection method and device based on XGBoost

Abstract

The invention belongs to the field of power plant safety control systems, and particularly relates to a multi-dimensional data anomaly detection method and device based on XGBoost. The method is characterized by comprising a first step of data acquisition and cleaning, a second step of performing standardization processing on cleaned data, and unifying dimensions between data of different dimensions; step 3, feature extraction and dimension reduction; step 4, exception detection model training: training the dimension reduction data by using an XGBoost method, and establishing a prediction model of equipment exception; and step 5, abnormity on-line detection is carried out, and if a given threshold value is exceeded, it is determined that abnormity occurs. The method is suitable for processing and predicting important abnormal events of equipment, the thought and technology of ensemble learning are fully utilized, important features in multi-dimensional data information detected by an equipment sensor are effectively utilized, and then online abnormal detection based on real-time measuring point data of a power plant is achieved. The method is large in collected data volume, small in analysis error and high in early warning result accuracy.

Description

technical field [0001] The invention belongs to the field of thermal power generation, and relates to a multidimensional data anomaly detection method and device derived from sensor measurement points, in particular to an XGBoost-based multidimensional data anomaly detection method and device. Background technique [0002] With the rapid development of thermal power plant information construction, equipment fault diagnosis and predictive maintenance are getting more and more attention. There are a large number of large-scale equipment in thermal power plants. The equipment structure is complex and the working environment is harsh, which is easy to cause various failures. If it cannot be found and repaired in time, it will seriously affect the safety and reliability of its operation. Moreover, once the key equipment of the power plant fails and shuts down, it will affect the stable operation of the thermal power plant system, cause huge economic losses, and even affect the st...

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Problems solved by technology

[0006] Aiming at the problems existing in the prior art, the present invention is based on the real multi-dimensional measuring point data of thermal power plant equipment, and aims at the problems of slow speed and low accuracy when dealing with multi-dimensional data in the current equipment anomaly detection method, and proposes a multi-dimensional detection method based on XGBoost Time series data anomaly detection method and device

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