Statistical Process Control Based on Kalman Filter in Manufacturing Process

Abstract:

In order to reduce the impact of data noise to quality control and make monitor results more precise in manufacturing process, the method of statistical process control based on Kalman filter was proposed. In this method, the statistical process control model based on Kalman filter was built and the quality control method of exponentially weighted moving average based on Kalman filter was put forward. While monitoring manufacturing process, first the technology of Kalman filter was used to smooth data and to reduce noise, and then control charts were built by the method of exponentially weighted moving average to monitor quality. Finally, the performance of the exponentially weighted moving average method based on Kalman filter and the tranditional exponentially weighted moving average method was compared. The performance result illustrates the feasibility and validity of the proposed quality monitor method.

• Periodical: Advanced Materials Research (Volumes 201-203)
• Pages: 986-989
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.201-203.986
• Online since: February 2011
• Authors: Pei Wang, Ding Hua Zhang, Shan Li, Ming Wei Wang, Bing Chen
• Keywords: Data Noise, Exponentially Weighted Moving Average, Kalman Filter (KF), Statistical Process Control (SPC)

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