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Extracting useful signals from flawed sensor data: Developing hybrid data-driven approaches with physical factors.

Cheng Yang1, Glen T Daigger1, Evangelia Belia2

  • 1Civil and Environmental Engineering, University of Michigan, 2350 Hayward St, G.G. Brown Building, Ann Arbor, MI 48109, USA.

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|October 22, 2020
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Summary

Hybrid approaches extract valuable data from flawed water quality sensor signals. Incorporating physical factors with machine learning improves signal processing for better water and wastewater management.

Keywords:
AutomationData qualityMachine LearningPattern separationSensorsSimilarity

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Area of Science:

  • Environmental science
  • Water quality monitoring
  • Data science

Background:

  • Water quality sensors are increasingly available but prone to errors from complex influents and instability.
  • Extracting reliable data from flawed sensor measurements is a significant challenge in water and wastewater systems.
  • Traditional methods focus on sensor maintenance, but data-driven approaches offer alternative solutions.

Purpose of the Study:

  • To demonstrate a hybrid approach for processing flawed sensor signals by integrating physical factors with data-driven tools.
  • To provide a case study using five-day biochemical oxygen demand (BOD5) sensor data to showcase signal processing improvements.
  • To propose an Improved Standard Signal Processing Architecture (ISSPA) for enhanced sensor data utilization.

Main Methods:

  • Utilized a hybrid methodology combining physical factors (process knowledge, constraints, observations) with data-driven tools.
  • Applied machine learning algorithms to process flawed five-day biochemical oxygen demand (BOD5) sensor measurements.
  • Incorporated physical insights throughout the signal processing pipeline, from raw data to validated output.

Main Results:

  • Successfully extracted and validated useful signals from flawed BOD5 sensor measurements.
  • Demonstrated that integrating physical factors significantly improved the performance of machine learning tools in signal processing.
  • The proposed Improved Standard Signal Processing Architecture (ISSPA) offers a structured framework for hybrid signal processing.

Conclusions:

  • Hybrid approaches are effective for extracting valuable information from imperfect sensor data in water systems.
  • Physical factors are crucial for enhancing the accuracy and reliability of data-driven signal processing techniques.
  • The developed methodology and ISSPA provide a pathway for more robust water quality monitoring and management.