离群值
主成分分析
异常检测
故障检测与隔离
计算机科学
概率逻辑
人工神经网络
冷冻机锅炉系统
数据挖掘
断层(地质)
人工智能
模式识别(心理学)
工程类
冷水机组
制冷剂
机械工程
气体压缩机
地震学
执行机构
地质学
作者
Viet Tra,Manar Amayri,Nizar Bouguila
标识
DOI:10.1016/j.buildenv.2022.109620
摘要
The application of fault detection and diagnosis (FDD) for chillers is very crucial for ensuring the smooth operation, stability, and durability of HVAC systems. Recent FDD models with outstanding diagnostic performance have been proposed for chiller systems. However, the high performance of FDD models is only guaranteed in condition observed data for FDD model construction are not contaminated, i.e., do not contain outliers, which is inevitable during data recording process. To resolve this problem, this paper proposes a novel outlier detection (OD) method named NN-MPPCA. The proposed method uses a neural network (NN) to model a mixture of probabilistic principal component analyzers (MPPCA) framework which parameters are updated via back-propagation. The combining mechanism of MPPCA guarantees NN-MPPCA to fit well complex data distributions, and unseen instances with low reconstruction probability under MPPCA framework are detected as outliers. Comprehensive experimental results of exhaustive comparisons have demonstrated the superiority of NN-MPPCA over other state-of-the-art OD algorithms as well as its effectiveness for chiller FDD application. Under the scenario that datasets contain 20% outliers (i.e., both local outliers and global outliers), the outlier detection performance of NN-MPPCA is highest with F1-scores achieving from 95.4% to 99.7%. Also, with NN-MPPCA applied in the data purification step, FDD models can achieve a diagnostic accuracy up to 99.5%; while they can only reach a diagnostic accuracy of 82.6% at most if contaminated data are directly used.
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