A new chart-independent method for fast identification of control level of industrial processes using continuous data

Authors

H. Khoshdast Mining Engineering Department, Higher Education Complex of Zarand, Shahid Bahonar University of Kerman, Kerman, Iran

M. Mahmoodabadi Mining Engineering Department, Higher Education Complex of Zarand, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract:

A new method is developed for a fast identification of the stability situation of industrial processes. The proposed method includes two factor ratios of the control constants for the upper and lower control limits to process these constants. An indication ratio is then defined as the ratio of the maximum data range value to the difference between the maximum and average values for individual data points. It is shown that if the indication ratio comes into values between the corresponding control factor ratios, the process will be under control, and otherwise, if the indication ratio decreases to smaller than the lower control factor ratio or gets more than the upper control factor ratio, the process will be expected to be out-of-control. Validation of the method was successfully resulted using two series of quality control datasets obtained from Zarand Iron Ore Complex (Zarand, Iran) and Miduk Copper Complex (Shahr Babak, Iran). The results obtained show that the process responses predicted by the proposed method are in agreement with those indicated by the conventional chart-based method. The developed method eliminates the need for drawing the process control charts used to assess the process control level. The superiority of the proposed method over the chart-based method becomes apparent especially when a large number of control charts are necessary to be drawn and interpreted for engineering decision-making purposes.

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Journal title

Journal of Mining and Environment

volume 8 issue 2

pages 203- 213

publication date 2017-04-01

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Keywords

Process Monitoring Control Chart Factor Ratio Industrial Process Continuous Data

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