相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Fault detection based on Bayesian network and missing data imputation for building energy systems
Zhanwei Wang et al.
APPLIED THERMAL ENGINEERING (2021)
An incipient fault detection and self-learning identification method based on robust SVDD and RBM-PNN
Chuanfang Zhang et al.
JOURNAL OF PROCESS CONTROL (2020)
Robust support vector data description for novelty detection with contaminated data
Kunzhe Wang et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2020)
Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features
Hua Han et al.
APPLIED THERMAL ENGINEERING (2019)
Chiller fault diagnosis with field sensors using the technology of imbalanced data
Yuqiang Fan et al.
APPLIED THERMAL ENGINEERING (2019)
Feature selection based on Bayesian network for chiller fault diagnosis from the perspective of field applications
Zhanwei Wang et al.
APPLIED THERMAL ENGINEERING (2018)
Handling binary classification problems with a priority class by using Support Vector Machines
L. Gonzalez-Abril et al.
APPLIED SOFT COMPUTING (2017)
Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information
Zhanwei Wang et al.
APPLIED ENERGY (2017)
Robust least squares twin support vector machine for human activity recognition
Reshma Khemchandani et al.
APPLIED SOFT COMPUTING (2016)
Fault detection and diagnosis of chiller using Bayesian network classifier with probabilistic boundary
He Suowei et al.
APPLIED THERMAL ENGINEERING (2016)
A data-driven multidimensional visualization technique for process fault detection and diagnosis
Shriram Gajjar et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2016)
An improved fault detection method for incipient centrifugal chiller faults using the PCA-R-SVDD algorithm
Guannan Li et al.
ENERGY AND BUILDINGS (2016)
A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis
Dan Li et al.
ENERGY AND BUILDINGS (2016)
A new support vector data description method for machinery fault diagnosis with unbalanced datasets
Lixiang Duan et al.
EXPERT SYSTEMS WITH APPLICATIONS (2016)
Data density-based fault detection and diagnosis with nonlinearities between variables and multimodal data distributions
Hiromasa Kaneko et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2015)
Classification of silent speech using support vector machine and relevance vector machine
Mariko Matsumoto et al.
APPLIED SOFT COMPUTING (2014)
A robust pattern recognition-based fault detection and diagnosis (FDD) method for chillers
Yang Zhao et al.
HVAC&R RESEARCH (2014)
A Density-focused Support Vector Data Description Method
Poovich Phaladiganon et al.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL (2014)
Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD)
Yang Zhao et al.
APPLIED ENERGY (2013)
A modified support vector data description based novelty detection approach for machinery components
Shijin Wang et al.
APPLIED SOFT COMPUTING (2013)
SVDD-based outlier detection on uncertain data
Bo Liu et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2013)
Study on a hybrid SVM model for chiller FDD applications
H. Han et al.
APPLIED THERMAL ENGINEERING (2011)
Cross-level fault detection and diagnosis of building HVAC systems
Siyu Wu et al.
BUILDING AND ENVIRONMENT (2011)
Decoupling features for fault detection and diagnosis on centrifugal chillers (1486-RP)
Xinzhi Zhao et al.
HVAC&R RESEARCH (2011)
PCA-SVM-Based Automated Fault Detection and Diagnosis (AFDD) for Vapor-Compression Refrigeration Systems
Hua Han et al.
HVAC&R RESEARCH (2010)
A Novel Strategy for the Fault Detection and Diagnosis of Centrifugal Chiller Systems
Qiang Zhou et al.
HVAC&R RESEARCH (2009)
Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods
Xinhua Xu et al.
APPLIED THERMAL ENGINEERING (2008)
Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner
Minsung Kim et al.
INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID (2008)
Density-induced support vector data description
KiYoung Lee et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2007)
A rule-based fault detection method for air handling units
Jeffrey Schein et al.
ENERGY AND BUILDINGS (2006)
Improving support vector data description using local density degree
K Lee et al.
PATTERN RECOGNITION (2005)
Methods for fault detection, diagnostics, and prognostics for building systems - A review, part II
S Katipamula et al.
HVAC&R RESEARCH (2005)
Methods for fault detection, diagnostics, and prognostics for building systems - A review, part I
S Katipamula et al.
HVAC&R RESEARCH (2005)
Support vector data description
DMJ Tax et al.
MACHINE LEARNING (2004)