Research on Equipment Fault Diagnosis Classification Model Based on Integrated Incremental Dynamic Weight Combination

• 作者: Aoqi Tan Bing Yu Haipeng Ji Xinduo Liu Zhijie Wang
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 地球科学
• 关键词: Neural network Support Vector Machine Integrated increment Unbalanced data processing Fault diagnosis
• 年，卷（期）: 2020,（2）
• 页码范围: 475-489
• 页数: 15页
• 分类号: P31

摘要

This study proposes a classification model of equipment fault diagnosis based on integrated incremental learning mechanism on the basis of characteristics of industrial equipment status data.The model first proposes a dynamic weight combination classification model based on long short-term memory(LSTM)and support vector machine(SVM).It solved the problem of fault feature extraction and classification in high noise equipment state data.Then,in this model,integrated incremental learning mechanism and unbalanced data processing technology were introduced to solve problems of massive unbalanced new data feature extraction and classification and sample category imbalance under equipment status data.Finally,an equipment fault diagnosis classification model based on integrated incremental dynamic weight combination is formed.Experiments prove that the model can effectively overcome the problems of excessive data volume,unbalanced,high noise,and inability to correlate data samples in the process of equipment fault diagnosis.