Self-adaptive two-stage alarming method aiming at axial displacement abrupt change monitoring

Abstract

The invention discloses a self-adaptive two-stage alarming method aiming at axial displacement abrupt change monitoring. The method comprises the steps of firstly connecting a displacement signal, building a data set and pre-processing to obtain a training data set of the displacement; then extracting features of the training data set to form a feature data set which is composed of N data sets andfusing the training data set and the feature data set based on moment information; estimating each statistical parameter of the feature data set based on a probability density and building a first-stage alarming model; then sequencing all the data of the training data set according to an ascending sequence to obtain a data set, calculating interquartile range and a median of the data set, determining an initial threshold of the displacement training data set based on a multiplied interquartile principle and building a second-stage alarming model; and judging and updating based on the two-stage alarming models along with development of the running time of an engine. The self-adaptive two-stage alarming method aiming at axial displacement abrupt change monitoring realizes self-adaptive alarming of the axial signals of the engine, has relatively low calculation amount, needs a small amount of historical data and can realize online monitoring.

Description

technical field [0001] The invention belongs to the technical field of equipment signal processing and monitoring and diagnosis, and in particular relates to an adaptive two-level alarm method for axial displacement mutation monitoring. Background technique [0002] The rocket engine is a kind of jet engine used in the aerospace field. It uses the discharge of high-temperature and high-speed gas to generate thrust. It is characterized by high cost, complex design structure and harsh working environment. Before the rocket engine is delivered to use, it often faces extremely rigorous tests. In order to prevent rocket motor explosion and casualties during the test, the status monitoring and fault warning of rocket motors are particularly important. However, the current rocket engine has a series of problems such as complex design structure, difficult signal acquisition, poor working conditions of sensors, limited types of collected signals, and low signal-to-noise ratio, so th...

AI Technical Summary

Problems solved by technology

The traditional method is based on the probability density estimation to learn the probability distribution to form an adaptive alarm curve, but this method requires a large amount of previous data accumulation and cannot effectively monitor engine state changes under small sample conditions

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