A Recurrent Neural Network (RNN)-Based Approach to Predictive Maintenance of Power Production Equipment

Abstract

The invention provides a method for predictive maintenance of electric power production equipment based on recurrent neural network (RNN). A data modeling platform based on Spark is established to support parallel data preprocessing and RNN modeling; Predefined RNN structure supports for multiple data input interfaces (HDFS, NFS, S3); Data pre-processing can standardize the historical data of several main function modules such as coal burner, pump system and fan according to the user-defined data cleaning logic; Iterative row modeling and model tuning, modeling process includes extracting datafeatures in the RNN way and combined with user-tagged fault state to model the diagnosis model, tuning process includes verifying the data set to detect the success rate of pre-judgement and the user-defined neural network correction strategy to reconstruct RNN. The above technical scheme provides an iterative modeling based on the time series data of a recurrent neural network (RNN) power production equipment, and provides fault prediction for a power production operator to perform predictive maintenance by identifying a fault occurrence mode.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular relates to a predictive maintenance method for power production equipment based on RNN. Background technique [0002] In recent years, predictive maintenance has gradually become a method for energy production enterprises to ensure production and improve operational efficiency. Predictive maintenance is through online monitoring and analysis of equipment operating status, in order to be able to provide timely warnings before equipment performance degradation or failure, put forward executable suggestions for operators, or raise alarms for maintenance personnel, so as to ensure that potential faulty equipment can be timely maintenance or troubleshooting. Since the state detection of the equipment with innovative maintenance is carried out online, it will not affect the normal operation of the equipment. As a method of on-demand maintenance, predictive maintenance can reduc...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problems existing in the existing predictive maintenance technical framework (i.e. data processing and analysis framework), the purpose of the present invention is to provide an efficient and flexible modeling and The optimization solution can perform flexible and efficient modeling and model optimization for the maintenance scenarios of power production equipment, so as to form effective failure mode recognition and alarm, and assist operation and management personnel to intervene and maintain

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