Kernel Principal Component Analysis for Structural Health Monitoring and Damage Detection of an Engineering Structure Under Operational Loading Variations

New reduced kernel PCA for fault detection and diagnosis in cement rotary kiln

(2020) F. Bencheikh et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Fault detection and diagnosis in air handling using data-driven methods

(2020) Atena Montazeri et al.   Journal of Building Engineering

Fault identification and diagnosis based on KPCA and similarity clustering for nuclear power plants

(2020) Hang Wang et al.   ANNALS OF NUCLEAR ENERGY

In-flight and wireless damage detection in a UAV composite wing using fiber optic sensors and strain field pattern recognition

(2019) Joham Alvarez-Montoya et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A novel hybrid of auto-associative kernel regression and dynamic independent component analysis for fault detection in nonlinear multimode processes

(2018) Jungwon Yu et al.   JOURNAL OF PROCESS CONTROL

Adaptive kernel density-based anomaly detection for nonlinear systems

(2018) Liangwei Zhang et al.   KNOWLEDGE-BASED SYSTEMS

Machine learning algorithms for damage detection: Kernel-based approaches

(2016) Adam Santos et al.   JOURNAL OF SOUND AND VIBRATION

Some Recent Developments in SHM Based on Nonstationary Time Series Analysis

(2016) Keith Worden et al.   PROCEEDINGS OF THE IEEE

An Improved Gaussian Mixture Model for Damage Propagation Monitoring of an Aircraft Wing Spar under Changing Structural Boundary Conditions

(2016) Lei Qiu et al.   SENSORS

The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform

(2014) Renping Shao et al.   MEASUREMENT

Damage detection by using FBGs and strain field pattern recognition techniques

(2012) Julián Sierra-Pérez et al.   Smart Materials and Structures

Impedance based damage detection under varying temperature and loading conditions

(2011) Hyung Jin Lim et al.   NDT & E INTERNATIONAL