Kamran Paynabar

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Quality control charts have proven to be very effective in detecting out-of-control states. When a signal is detected a search begins to identify and eliminate the source(s) of the signal. A critical issue that keeps the mind of the process engineer busy at this point is determining the time when the process first changed. Knowing when the process first(More)
This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents(More)
A novel algorithm is developed for feature selection and parameter tuning in quality monitoring of manufacturing processes using cross-validation. Due to the recent development in sensing technology, many on-line signals are collected for manufacturing process monitoring and feature extraction is then performed to extract critical features related to(More)
Image and video sensors are increasingly being deployed in complex systems due to the rich process information that these sensors can capture. As a result, image data play an important role in process monitoring and control in different application domains such as manufacturing processes, food industries, medical decision-making, and structural health(More)
In this expository paper we give an overview of some statistical methods for the monitoring of social networks. We discuss the advantages and limitations of various methods as well as some relevant issues. One of our primary contributions is to give the relationships between network monitoring methods and monitoring methods in engineering statistics and(More)
In modern manufacturing systems, online sensing is being increasingly used for process monitoring and fault diagnosis. In many practical situations, the output of the sensing system is represented by time-ordered data known as profiles or waveform signals. Most of the work reported in the literature has dealt with cases in which the production process is(More)
This article proposes a new method to develop multiscale monitoring control charts for an autocorrelated process that has an underlying unknown ARMA(2, 1) model structure. The Haar wavelet transform is used to obtain effective monitoring statistics by considering the process dynamic characteristics in both the time and frequency domains. Three control(More)
Placing sensors in every station of a process or every element of a system to monitor its state or performance is usually too expensive or physically impossible. Therefore, a systematic method is needed to select important sensing variables. The method should not only be capable of identifying important sensors/signals among multi-stream signals from a(More)