Using Neural Networks to Detect and Classify Out‐of‐control Signals in Autocorrelated Processes

Abstract

This paper presents an artificial neural network model for detecting and classifying three types of non-random disturbances referred to as level shift, additive outlier and innovational outlier which are common in autocorrelated processes. To the best of our knowledge, this is the first time that a neural network has been considered for simultaneous detection and classification of such non-random disturbances. An AR (1) model is considered to characterize the quality characteristic of interest in a continuous process where autocorrelated observations are generated over time. The performance of the proposed procedure is evaluated through the use of a numerical example. Preliminary results indicate that the procedure can be used effectively to detect and classify unusual shocks in autocorrelated processes.

X-MR control chart for autocorrelated fuzzy data using D p,q -distance

“…Woodall and Faltin [31] then gave a brief summary on the effect of autocorrelation on the performance of control charts and explained methods to deal with autocorrelation. Noorossana et al [23] presented an artificial neural network model for detecting and classifying three types of non-random disturbances referred to as level shift, additive outlier, and innovational outlier, which were common in autocorrelated processes. An autoregressive of order one, AR(1) model, was considered to characterize the quality characteristic of interest in a continuous process where autocorrelated observations were generated over time.…”

Further potentials of CAQ tools for fault handling in automotive manufacturing processes

“…The first is their ability to detect correlations between subsets of the input signals, which is a problem that received the attention of many researchers [25,26]. The second aspect is the ability to model nonlinear relations between the monitored quality characteristics [25].…”

Using Minimum Quantization Error chart for the monitoring of process states in multivariate manufacturing processes

“…His results showed that BPN-based monitoring scheme performed better than residual control chart, X, EWMA, ARMAST control charts in major cases based on the in-control and out-of-control ARL performance. Noorsossana, Farrokhi, and Saghaei (2003) were the first to propose an NN model to detect and classify non-random disturbances in auto-correlated processes. Their experimental results showed that this NN model is an effective method for cause-selecting problems.…”