Ibrahim Masood

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Control chart pattern recognition has become an active area of research since late 1980s. Much progress has been made, in which there are trends to heighten the performance of artificial neural network (ANN)-based control chart pattern recognition schemes through feature-based and wavelet-denoise input representation techniques, and through modular and(More)
Monitoring and diagnosis of mean shifts in manufacturing processes become more challenging when involving two or more correlated variables. Unfortunately, most of the existing multivariate statistical process control schemes are only effective in rapid detection but suffers high false alarm, that is, imbalanced monitoring performance. The problem becomes(More)
An intelligent control chart pattern recognition system is essential for efficient monitoring and diagnosis process variation in automated manufacturing environment. Artificial neural networks (ANN) have been applied for automated recognition of control chart patterns since the last 20 years. In early study, the development of control chart patterns(More)
Keywords: Balanced monitoring Bivariate quality control Statistical features Synergistic artificial neural network Two-stage monitoring In manufacturing industries, it is well known that process variation is a major source of poor quality products. As such, monitoring and diagnosis of variation is essential towards continuous quality improvement. This(More)
In multivariate quality control, the rutificial neu-ral networks (ANN)-based pattem recognition schemes generally performed better for monitoring bivariate process mean shifts and provided more efficient information for diagnosing the source variable(s) compared to the traditional multivariate statistical process control charring. However, these schemes(More)
Artificial neural network (ANN)-based recognizers have been developed for monitoring and diagnosis bivariate process mean shift in multivariate statistical process control (MSPC). They have better average run lengths (ARLs) performance in monitoring process mean shifts and gave an useful diagnosis information compared to the traditional MSPC schemes such as(More)
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