OWave control chart for monitoring the process mean |
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| Affiliation: | 1. Universidade Federal de Santa Catarina, Departamento de Automação e Sistemas, 88040-900 Florianópolis, SC, Brazil;2. Dpto. de Informática, Universidad de Almería - CIESOL, Campus de Excelencia Internacional Agroalimentario, ceiA3. Crta. Sacramento s/n, 04120 La Cañada, Spain;1. Centre for Process System Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK;2. ABB Corporate Research Center, ul. Starowiślna 13a, 31-038 Kraków, Poland;1. Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26506, USA;2. Department of Aerospace Engineering Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA;1. University of Bayreuth, Mathematical Institute, Germany;2. Ruhr-University Bochum, Institute of Automation and Computer Control, Germany |
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| Abstract: | In this paper a control chart for monitoring the process mean, called OWave (Orthogonal Wavelets), is proposed. The statistic that is plotted in the proposed control chart is based on weighted wavelets coefficients, which are provided through the Discrete Wavelets Transform using Daubechies wavelets family. The statistical behavior of the wavelets coefficients when the mean shifts are occurring is presented, and the distribution of wavelets coefficients in the case of normality and independence assumptions is provided. The on-line algorithm of implementing the proposed method is also provided. The detection performance is based on simulation studies, and the comparison result shows that OWave control chart performs slightly better than Fixed Sample Size and Sampling Intervals control charts (, EWMA, CUSUM) in terms of Average Run Length. In addition, illustrative examples of the new control chart are presented, and an application to Tennessee Eastman Process is also proposed. |
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| Keywords: | Statistical process control Fault detection Multi-scale analysis Wavelets Mean shifts Probability distribution |
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