Fault diagnosis method based on fault data deep mining and learning

Abstract

The invention belongs to the technical field of fault diagnosis of generating set equipment, and in particular relates to a fault diagnosis method based on deep mining and learning of fault data, including: collecting historical data of generating sets, preprocessing, and using a deep long-term short-term memory network algorithm for learning and After training, after obtaining the fault data screening model, it traverses the massive historical database, and screens to form a fault data sample set; for the fault data sample set, the Medoids surrounding classification method is used to estimate the number of fault types, and the K-Means clustering algorithm is used for cluster analysis to form a multi-class Typical fault sample set; use LSTM neural network algorithm to train and learn multiple types of typical fault sample sets, and establish a fault diagnosis model; monitor the real-time operating data of the system based on the fault diagnosis model, discriminate the operating status of the system and record the newly generated Fault samples, using the updated multi-class typical fault sample sets to update the fault diagnosis model.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of generating set equipment, and in particular relates to a fault diagnosis method based on deep mining and learning of fault data. Background technique [0002] Equipment fault diagnosis technology is very important to the daily safety of industrial equipment. It is not only related to daily production arrangements and equipment maintenance, but also can detect hidden faults of equipment and eliminate them in time to avoid sudden failures of important equipment from affecting the overall safety and stability of the production process. . The premise of fault diagnosis is the monitoring and analysis of equipment state quantities. Taking power plants as an example, monitoring systems in power plants are widely used, and are equipped with historical databases that can store massive operating data of power plants. Power plant operation data is rich in content, and several months of data can o...

AI Technical Summary

Problems solved by technology

Power plant operation data is rich in content, and several months of data can often cover all working conditions of equipment operation. However, the data storage of power plants is huge and difficult to analyze. The data mining method combined with deep learning technology that has emerged in recent years can effectively solve this problem. a question

[0003] The essence of fault diagnosis and identification methods is the classification and regression problems in data mining and deep learning. In the past, fault diagnosis methods commonly used methods such as Fourier transform, wavelet transform, statistical analysis, and spectrum analysis. These processing methods often rely on signal processing technology and Diagnosis experience, manual extraction of fault features is cumbersome and complicated, which increases the complexity of diagnosis

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