Equipment failure early warning system based on model full life cycle management

Abstract

A device fault warning system on the basis of model life-cycle management, comprising a data preparation module, a real-time fault warning module, a model risk management module, a model self-learning module, and a model library. The data preparation module reads external real-time data, pre-processes the same, and transmits the processed external real-time data to the real-time fault warning module and the model risk management module, and an analysis is performed thereon. The real-time fault warning module predicts the risk of failure and generates warning information and maintenance suggestions. The model risk management module evaluates the reliability of model results. The model self-learning module reads the accumulated marked samples and re-trains the model in the real-time fault warning module. The system can realize life-cycle online monitoring for models and fault warnings of a device, and can realize dynamic update on the models, ensuring the constant reliability of the model results and introducing the running data and operational data to minimize warning errors.

Description

technical field [0001] This application relates to an equipment failure early warning system based on model-based full life cycle management. Background technique [0002] In recent years, with the popularization of the Internet and artificial intelligence technology in the field of wind power, the health status monitoring and operation and maintenance of high-value industrial equipment such as wind turbines, steam turbines, and CNC machine tools are also developing towards intelligence. Taking wind turbine equipment as an example, its fault early warning system uses equipment operation data such as SCADA (Data Acquisition and Supervisory Control System) data widely connected to wind farms to perform fault early warning and diagnosis on the health status of key components to guide predictive Improve equipment maintenance, reduce downtime accidents, and reduce operation and maintenance costs. [0003] Existing equipment failure warning systems based on SCADA data can only be...

AI Technical Summary

Problems solved by technology

[0003] Existing equipment failure warning systems based on SCADA data can only be modeled based on very limited equipment operation data and a very small number of failure labels, and the modeling is too dependent on machine learning modeling methods, resulting in models that are only available in Only within a certain range of adaptation can a correct early warning be carried out

However, the existing system lacks a quantitative assessment of the range of adaptation; and after the model goes online, it lacks the monitoring of the reliability of the early warning results of the model and the adaptive update of the model parameters

Due to the lack of these mechanisms, the accuracy of the model will decline rapidly over time after the model is launched, and it is difficult to achieve continuous and accurate early warning during the entire life cycle of the equipment

In the past, when such a problem occurred, it could only be completed by manual offline remodeling training

[0004] Some existing patents mainly focus on the modeling and analysis method of equipment failure early warning itself, but lack the design of the model's full life cycle management method and system, and the risk of the model in the system cannot be monitored online

Some existing patents have proposed methods for model self-training or automatic parameter adjustment of some sub-modules in the fault warning system, but the training model cannot be dynamically updated, and the continuous reliability of the model results cannot be guaranteed.

In addition, in the prior art, only operating parameters are included in the read equipment parameters, without taking into account the management and operating parameters, resulting in many invalid or distorted operating parameters being used as training models to generate early warning errors

Images