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Probabilistic qualitative analysis for fault detection and identification of an on-line phosphate analyzer

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Abstract

On-line, real-time collection of measurements remains a key challenge in water quality monitoring and control due to unknown and varying quality of on-line sensor data. Today’s data quality assessment is typically based on a comparison of sensor-based measurements and grab samples of the sampled solution taken next to the on-line analyzer and analyzed in a laboratory. In this work, internal data is used for fault detection and identification of a phosphate analyzer to inspect the measuring process itself. These internal data is shown to be information-rich with respect to the analyzer’s status. Furthermore, this information is captured well by means of a newly developed method for qualitative analysis of time series. This method was developed with global optimality in mind and therefore lends itself to a probabilistic assessment of the qualitative representation of time series.

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Authors and Affiliations

  1. Laboratory for Intelligent Process Systems (LIPS), School of Chemical Engineering, Purdue University, 480 Stadium Mall Drive, West Lafayette, IN, 47907, USA

    Kris Villez & Venkat Venkatasubramanian

  2. EnviroSim Associates Ltd, McMaster Innovation Park, 114A-175 Longwood Road South, Hamilton, ON, L8P 0A1, Canada

    Leiv Rieger

  3. Department of Urban Water and Waste Management, University of Duisburg-Essen, Universitätsstr. 15, 45141, Essen, Germany

    Benjamin Keser

Authors
  1. Kris Villez
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  2. Leiv Rieger
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  3. Benjamin Keser
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  4. Venkat Venkatasubramanian
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Correspondence to Kris Villez.

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Villez, K., Rieger, L., Keser, B. et al. Probabilistic qualitative analysis for fault detection and identification of an on-line phosphate analyzer. Int J Adv Eng Sci Appl Math 4, 67–77 (2012). https://doi.org/10.1007/s12572-012-0056-0

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  • DOI: https://doi.org/10.1007/s12572-012-0056-0

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