Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts

Methods for interpreting the out-of-control signal of multivariate control charts: A comparison study

(2017) Sotiris Bersimis et al.   QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL

Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis

(2017) Marco Reis et al.   Processes

Signal interpretation in Hotelling’sT2control chart for compositional data

(2016) Marina Vives-Mestres et al.   IIE TRANSACTIONS

Statistical Learning Methods Applied to Process Monitoring: An Overview and Perspective

(2016) Maria Weese et al.   JOURNAL OF QUALITY TECHNOLOGY

Applying emerging soft computing approaches to control chart pattern recognition for an SPC–EPC process

(2016) Yuehjen E. Shao et al.   NEUROCOMPUTING

Integrating Auto-Associative Neural Networks with Hotelling T2 Control Charts for Wind Turbine Fault Detection

(2015) Hsu-Hao Yang et al.   Energies

An adaptive step-down procedure for fault variable identification

(2015) Jinho Kim et al.   INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

Hybrid Artificial Neural Networks Modeling for Faults Identification of a Stochastic Multivariate Process

(2013) Yuehjen E. Shao et al.   Abstract and Applied Analysis

Multivariate process monitoring and fault identification using multiple decision tree classifiers

(2013) Shuguang He et al.   INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

Fault Identification in Industrial Processes Using an Integrated Approach of Neural Network and Analysis of Variance

(2013) Yuehjen E. Shao et al.   MATHEMATICAL PROBLEMS IN ENGINEERING

Out-of-Control Signals in Three-Part CompositionalT2Control Chart

(2013) Marina Vives-Mestres et al.   QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL

On line detection of mean and variance shift using neural networks and support vector machine in multivariate processes

(2012) Mojtaba Salehi et al.   APPLIED SOFT COMPUTING

A Hybrid ICA-SVM Approach for Determining the Quality Variables at Fault in a Multivariate Process

(2012) Yuehjen E. Shao et al.   MATHEMATICAL PROBLEMS IN ENGINEERING

Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India

(2012) Girish K. Jha et al.   NEURAL COMPUTING & APPLICATIONS

Predicting stock market returns from malicious attacks: A comparative analysis of vector autoregression and time-delayed neural networks

(2011) Lara Khansa et al.   DECISION SUPPORT SYSTEMS

Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques

(2011) Shu-Guang He et al.   JOURNAL OF INTELLIGENT MANUFACTURING

On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model

(2011) Mojtaba Salehi et al.   NEUROCOMPUTING

Short-term Maharashtra state electrical power load prediction with special emphasis on seasonal changes using a novel focused time lagged recurrent neural network based on time delay neural network model

(2010) S.M. Kelo et al.   EXPERT SYSTEMS WITH APPLICATIONS

Intelligent monitoring and diagnosis of manufacturing processes using an integrated approach of KBANN and GA

(2008) Jianbo Yu et al.   COMPUTERS IN INDUSTRY

Shift detection and source identification in multivariate autocorrelated processes

(2008) H. Brian Hwarng et al.   INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

A boosting approach for understanding out-of-control signals in multivariate control charts

(2008) E. Alfaro et al.   INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines

(2007) C CHENG et al.   EXPERT SYSTEMS WITH APPLICATIONS

A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes

(2007) Jian-bo Yu et al.   EXPERT SYSTEMS WITH APPLICATIONS