同济大学学报（自然科学版）2024，Vol.52Issue(3)：454-461,8.DOI:10.11908/j.issn.0253-374x.22214
贫数据中基于模型自训练的空气处理设备故障诊断
Fault Detection and Diagnosis of Air Handling Unit via Model Self-training in Poor-data Scenario
摘要
Abstract
A comparative analysis was conducted to evaluate four feature-selection algorithms in the context of diagnosing air handling unit(AHU)faults using deep belief network(DBN)with poor-data.The results indicate that the feature subset filtered by the maximum correlation minimum redundancy algorithm exhibits superior performance in terms of diagnostic accuracy and stability.Subsequently,a fault diagnosis model was developed by integrating DBN into a self-training framework,and a case study was performed to validate its efficacy.The findings demonstrate that the diagnosis accuracy of DBN self-training model can be improved by up to 19.5%than that of pure DBN.Furthermore,two self-training pseudo-label sampling strategies,namely uniform sampling and proportional sampling,were proposed.While both strategies contribute to increased diagnostic accuracy with a reduction in sampling number,the maximum difference observed among different sampling numbers is 3.42%.Notably,the uniform sampling strategy consistently outperforms the proportional sampling strategy,with a maximum accuracy difference of 1.39%across all scenarios with poor-data,which indicates that,in situations where the fault labels are seriously lacking,the uniform sampling strategy with the smaller sampling number is beneficial to improve the diagnosis performance of DBN self-training model.关键词
故障检测与诊断/空气处理设备/贫数据/特征选择/深度置信网络自训练模型Key words
fault detection and diagnosis/air handling unit/poor data/feature selection/deep belief network self-training model分类
建筑与水利引用本文复制引用
孟华,裴迪,阮应君,钱凡悦,邓永康,郑铭桦..贫数据中基于模型自训练的空气处理设备故障诊断[J].同济大学学报（自然科学版）,2024,52(3):454-461,8.基金项目
国家重点研发计划(2020YFD1100504) （2020YFD1100504）
