Parallel Algorithm for Control Chart Pattern Recognition

Mohd Arif Wani

16 citations0/0 files storedInternational Conference on Machine Learning and Applications

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Advanced Statistical Process Monitoring Neural Networks and Applications Fault Detection and Control Systems

Abstract

Fast control chart pattern recognition aids in instantaneous detection of abnormal functioning of a system. In this paper, we present a parallel algorithm for fast control chart pattern recognition. It addresses three major issues of control chart pattern recognition: (i) transparency (ii) accuracy and (iii) fast detection of abnormal patterns. The algorithm uses novel shape features extracted from a control chart pattern (CCP) instead of the unprocessed CCP data or its statistical properties. These shape features can be extracted in parallel. A parallel algorithm that is based on distributed and synergistic neural network structure for recognition of CCPs is described. The paper presents the results of analyzing several hundred control chart patterns and gives a comparison with those reported in previous work.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 16 citations exist upstream.

References

1994X -bar and R control chart interpretation using neural computing 1997Feature-based control chart pattern recognition 1993X¯ CONTROL CHART PATTERN IDENTIFICATION THROUGH EFFICIENT OFF-LINE NEURAL NETWORK TRAINING 1992Control chart pattern recognition using neural networks 1989Synthetic neural networks for process control 1991A comparison of neural networks to SPC charts 2004OPTIMIZING FEEDFORWARD NEURAL NETWORKS FOR CONTROL CHART PATTERN RECOGNITION THROUGH GENETIC ALGORITHMS

Cited by

2011Application of the PSO-RBFNN model for recognition of control chart patterns 2023Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns 2016Recognition of unnatural variation patterns in metal-stamping process using artificial neural network and statistical features

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.