Search for: multivariate-statistical-quality-control
A heuristic threshold policy for fault detection and diagnosis in multivariate statistical quality control environments, Article International Journal of Advanced Manufacturing Technology ; Volume 67, Issue 5-8 , July , 2013 , Pages 1231-1243 ; 02683768 (ISSN) ; Niaki, S. T. A ; Sharif University of Technology
In this paper, a heuristic threshold policy is developed to detect and classify the states of a multivariate quality control system. In this approach, a probability measure called belief is first assigned to the quality characteristics and then the posterior belief of out-of-control characteristics is updated by taking new observations and using a Bayesian rule. If the posterior belief is more than a decision threshold, called minimum acceptable belief determined using a heuristic threshold policy, then the corresponding quality characteristic is classified out-of-control. Besides using a different approach, the main difference between the current research and previous works is that the...
Article International Journal of Advanced Manufacturing Technology ; Volume 68, Issue 9-12 , 2013 , Pages 2283-2294 ; 02683768 (ISSN) ; Niaki, S. T. A ; Sharif University of Technology
A new approach is developed in this paper to detect general mean shifts of multivariate quality control systems and to determine the quality characteristic(s) responsible for the shift. This approach takes advantage of both a decomposition method and an EWMA-based control statistics that are employed for multivariate normal distributions. In order to evaluate the performance of the proposed methodology, simulation studies are provided to estimate the in- and out-of-control average run lengths under different mean and variance shift scenarios. Simulation experiments are also given to compare the performances of the proposed procedure with the ones of the well-known MEWMA and MCUSUM methods....
Decision-making in detecting and diagnosing faults of multivariate statistical quality control systems, Article International Journal of Advanced Manufacturing Technology ; Volume 42, Issue 7-8 , 2009 , Pages 713-724 ; 02683768 (ISSN) ; Fallah Nezhad, M. S ; Sharif University of Technology
A new methodology is proposed in this paper to both monitor an overall mean shift and classify the states of a multivariate quality control system. Based on the Bayesian rule (Montgomery, Introduction to statistical quality control, 5th edn. Wiley, New York, USA, 2005), the belief that each quality characteristic is in an out-of-control state is first updated in an iterative approach and the proof of its convergence is given. Next, the decision-making process of the detection and classification the process mean shift is modeled. Numerical examples by simulation are provided in order to understand the proposed methodology and to evaluate its performance. Moreover, the in-control and...