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

Authors:
Jian-bo Yu;Li-feng Xi
Affiliations:
School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, PR China;School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 12
Cited 9

Multivariate generalizations of cumulative sum quality-control schemes

Technometrics
Synthetic neural networks for process control

Computers and Industrial Engineering
Neural networks and the bias/variance dilemma

Neural Computation
A multivariate exponentially weighted moving average control chart

Technometrics
A multi-layer neural network model for detecting changes in the process mean

Computers and Industrial Engineering
Bagging predictors

Machine Learning
Ensembling neural networks: many could be better than all

Artificial Intelligence
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes

Computers and Industrial Engineering
A hybrid learning-based model for on-line detection and analysis of control chart patterns

Computers and Industrial Engineering
Evolutionary ensembles with negative correlation learning

IEEE Transactions on Evolutionary Computation
Using mutual information for selecting features in supervised neural net learning

IEEE Transactions on Neural Networks

Applying back propagation network to cold chain temperature monitoring

Advanced Engineering Informatics
Using neural networks to detect the bivariate process variance shifts pattern

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

Neurocomputing
Survey paper: Re-thinking diagnosis for future automation systems: An analysis of current diagnostic practices and their applicability in emerging IT based production paradigms

Computers in Industry
Pattern recognition of manufacturing process signals using Gaussian mixture models-based recognition systems

Computers and Industrial Engineering
A survey of control-chart pattern-recognition literature (1991-2010) based on a new conceptual classification scheme

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

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

Journal of Intelligent Manufacturing
Gower distance-based multivariate control charts for a mixture of continuous and categorical variables

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

In multivariate statistical process control, most multivariate quality control charts are shown to be effective in detecting out-of-control signals based upon an overall statistics. But these charts do not relieve the need for pinpointing source(s) of the out-of-control signals, which would be very useful for quality practitioners to locate the assignable causes that give rise to the out-of-control situation. In this study, a learning-based model has been investigated for monitoring and diagnosing out-of-control signals in a bivariate process. In this model, a selective neural network (NN) ensemble approach (DPSOEN, Discrete Particle Swarm Optimization) was developed for performing these tasks. The simulation results demonstrate that the proposed model outperforms the conventional multivariate control scheme in terms of average run length (ARL), and can accurately classify source(s) of out-of-control signals. Extensive experiment is also carried out to examine the effects of six statistical features on the performance of DPSOEN. Analysis from this study provides guidelines in developing NN ensemble-based Statistical process control recognition systems in multivariate processes.