Fault detection based on auto-regressive extreme learning machine for nonlinear dynamic processes

Canonical Correlation Analysis-Based Explicit Relation Discovery for Statistical Process Monitoring

(2020) Shengjun Meng et al.   JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS

Soft sensor modeling of industrial process data using kernel latent variables-based relevance vector machine

(2020) Hongbin Liu et al.   APPLIED SOFT COMPUTING

A novel randomized machine learning approach: Reservoir computing extreme learning machine

(2020) Ömer Faruk Ertuğrul   APPLIED SOFT COMPUTING

Ensemble-learning based neural networks for novelty detection in multi-class systems

(2020) Felix T.S. Chan et al.   APPLIED SOFT COMPUTING

Statistical monitoring for non-Gaussian processes based on MICA-KDR method

(2019) Ting Lan et al.   ISA TRANSACTIONS

Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes

(2019) Qingchao Jiang et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Electrocardiogram soft computing using hybrid deep learning CNN-ELM

(2019) Shuren Zhou et al.   APPLIED SOFT COMPUTING

A novel fault diagnosis technique for wind turbine gearbox

(2019) Wenfeng Hu et al.   APPLIED SOFT COMPUTING

Design teacher and supervised dual stacked auto-encoders for quality-relevant fault detection in industrial process

(2019) Shifu Yan et al.   APPLIED SOFT COMPUTING

Parallel supervised additive and multiplicative faults detection for nonlinear process

(2019) Yang Tao et al.   JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS

Neural component analysis for fault detection

(2018) Haitao Zhao   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

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

Mixed kernel based extreme learning machine for electric load forecasting

(2018) Yanhua Chen et al.   NEUROCOMPUTING

Process Data Analytics via Probabilistic Latent Variable Models: A Tutorial Review

(2018) Zhiqiang Ge   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

A missing variable approach for decentralized statistical process monitoring

(2018) Chudong Tong et al.   ISA TRANSACTIONS

A new pruning method for extreme learning machines via genetic algorithms

(2016) Alisson S.C. Alencar et al.   APPLIED SOFT COMPUTING

Quality-related fault detection using linear and nonlinear principal component regression

(2016) Guang Wang et al.   JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS

A New Method of Dynamic Latent-Variable Modeling for Process Monitoring

(2014) Gang Li et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis

(2013) Jicong Fan et al.   INFORMATION SCIENCES

Dynamic model-based fault diagnosis for (bio)chemical batch processes

(2012) Pieter Van den Kerkhof et al.   COMPUTERS & CHEMICAL ENGINEERING

Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models

(2008) Jie Yu et al.   AICHE JOURNAL

Dynamic model-based batch process monitoring

(2007) Sang Wook Choi et al.   CHEMICAL ENGINEERING SCIENCE