Online intelligent fault diagnosis method and system for rotating machinery for lifelong learning

Abstract

The invention discloses a rotating machinery online intelligent fault diagnosis method and system for lifelong learning. The method comprises the following steps: building an initial intelligent diagnosis model through collected historical fault data, and carrying out the online updating of the intelligent diagnosis model through a sample incremental learning mode when new fault sample data is collected step by step, and when new fault mode data is collected step by step, updating the intelligent diagnosis model on line in a mode of mode incremental learning, collecting a data sample of the rotating machine to be diagnosed, inputting the data sample into the intelligent diagnosis model, and diagnosing the fault of the rotating machine. According to the method, lifetime learning of the intelligent diagnosis model is realized through initial model training, sample incremental learning and mode incremental learning, data stream information can be effectively processed, storage consumption of data is greatly reduced, and practical engineering application is facilitated.

Description

technical field [0001] The invention belongs to the technical field of safe service of rotating machinery, and in particular relates to a lifelong learning-oriented online intelligent fault diagnosis method and system for rotating machinery, which can be used for fault prediction and health management of rotating machinery. Background technique [0002] Rotating machinery, such as bearings, gears, turbine discs, etc., are frequently faulty parts of modern industrial equipment, and their service safety directly affects the operation status of the complete machine equipment. Therefore, efficient and accurate diagnosis of the fault status of rotating machinery is crucial to ensure the operation Safety, reducing maintenance costs and improving production efficiency are of great engineering significance. [0003] Intelligent fault diagnosis of rotating machinery aims to mine fault information in monitoring data and identify the fault status of rotating machinery through artificia...

AI Technical Summary

Problems solved by technology

However, their shortcoming is that they all adopt the "offline modeling, online deployment" mode, which relies on a complete fault database to build a complete intelligent diagnosis model

In actual engineering, it is difficult to collect sufficient fault data, and it is difficult to establish a complete database

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