Pattern recognition for bivariate process mean shifts using feature-based artificial neural network
作者： Ibrahim Masood , Adnan Hassan
DOI: 10.1007/S00170-012-4399-2
关键词:
摘要: In multivariate quality control, the artificial neural networks (ANN)-based pattern recognition schemes generally performed better for monitoring bivariate process mean shifts and provided more efficient information diagnosing source variable(s) compared to traditional statistical control charting. However, these revealed disadvantages in term of reference patterns identifying joint effect excess false alarms stable condition. this study, feature-based ANN scheme was investigated recognizing correlated patterns. Feature-based input representation utilized into an training testing towards strengthening discrimination capability between normal shift Besides indicating effective diagnosis dealing with low correlation patterns, proposed promotes a smaller network size as raw data-based scheme.
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