Abnormal detection method of aero-engine pumping system based on improved dbscan algorithm

Abstract

The invention provides an abnormality detection method of an aeroengine pumping system based on an improved DBSCAN algorithm. The invention selects characteristic parameters according to the working characteristics of the aeroengine pump adjustment system, and then performs characteristic extraction from the launch parameter data to obtain the input characteristic matrix of the algorithm. At the same time, according to the characteristics of the input data of the algorithm, an innovative anomaly detection algorithm combining dynamic time warping and a noise-based density-based clustering algorithm is proposed. Compared with the traditional anomaly detection algorithm based on normal distribution, the result of anomaly detection by the algorithm proposed by the present invention is more accurate. The invention overcomes the defect that the noise-based density-based clustering algorithm cannot mine the relationship between time series in dealing with time-series problems, and combines the noise-based density-based clustering algorithm with dynamic time warping to improve the algorithm processing Capabilities for time series problems.

Description

technical field [0001] Aiming at the aero-engine pump regulation system, the present invention utilizes a method combining density-based clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, DBSCAN for short) and dynamic time warping (Dynamic Time Warping, abbreviated as DTW) to regulate the pump regulation system. The abnormality detection is carried out during the start-up process, and the health monitoring capability of the pump adjustment system is effectively improved. Background technique [0002] As a complex aero-thermodynamic system, aero-engine is of great significance to ensure and improve the safety and reliability of engine work by monitoring its working state and changing trend of performance parameters, and avoiding failures in a timely and effective manner. Engine data is the basis for avoiding faults. However, with the gradual improvement of system reliability, engine fault data is difficult to obtain in a short period of time, s...

AI Technical Summary

Problems solved by technology

In view of the high sensitivity of the radius parameter ε in the traditional algorithm, it is difficult to select global parameters for multi-layer density data sets, which leads to poor clustering results and other shortcomings.

In 2020, Guo et al. proposed a GS-DBSCAN algorithm based on similarity measurement to solve the problem that the traditional DBSCAN algorithm has poor clustering effect on high-dimensional data sets and the selection of parameters is sensitive.

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