Control Chart Pattern Recognition Using Artificial Neural Networks

Şeref Sağıroğlu · Erkan Beşdok · Mehmet Erler

51 citations0/0 files storedDergiPark (Istanbul University)

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

Abstract

Precise and fast control chart pattern (CCP) recognition is important for monitoring process environments to achieve appropriate control and to produce high quality products. CCPs can exhibit six types of pattern: normal, cyclic, increasing trend, decreasing trend, upward shift and downward shift. Except for normal patterns, all other patterns indicate that the process being monitored is not functioning correctly and requires adjustment. This paper describes a new type of neural network for speeding up the training process and to compare three training algorithms in terms of speed, performance and parameter complexity for CCP recognition. The networks are multilayered perceptrons trained with a resilient propagation, backpropagation (BP) and extended delta-bar-delta algorithms. The recognition results of CCPs show the BP algorithm is accurate and provides better and faster results.

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1994Control chart pattern recognition using learning vector quantization networks 1997Feature-based control chart pattern recognition

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2013Statistical process control using optimized neural networks: A case study 2021Control chart recognition based on the parallel model of CNN and LSTM with GA optimization 2012Recognition of control chart patterns using an intelligent technique 2011On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model 2023Control chart pattern recognition for imbalanced data based on multi-feature fusion using convolutional neural network 2019Control chart patterns recognition using ANFIS with new training algorithm and intelligent utilization of shape and statistical features 2011Control chart pattern recognition using neural networks and efficient features: a comparative study 2014On artificial neural networking-based process monitoring under bootstrapping using runs rules schemes

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