Evaluate the Impact of Sensor Accuracy on Model Performance in Data-Driven Building Fault Detection and Diagnostics Using Monte Carlo Simulation

Liang Zhang, Matt Leach

Research output: Contribution to journalArticlepeer-review

12 Scopus Citations

Abstract

The performance of data-driven fault detection and diagnostics (FDD) is heavily dependent on sensors. However, sensor inaccuracy and sensor faults are pervasive in building operation: inaccurate and missing sensor readings deteriorate FDD performance; sensor inaccuracy will also affect the selection of sensor for data-driven FDD in the model training process, which is another key factor of data-driven FDD performance. Sensor accuracy and sensor selection individually are well-studied research topics in this field, but the impact of sensor accuracy on sensor selection and its further impact on FDD performance has not been evaluated and quantified. In this paper, we developed a novel analysis methodology that comprehensively evaluates sensor fault on sensor selection and FDD accuracy. Monte Carlo simulation is applied to deal with multiple stochastic sensor inaccuracy and provide probabilistic analysis results of the impact of sensor inaccuracy on sensor selection and FDD accuracy. This methodology focuses on the net impact of fault states across a full sensor set. The developed methodology can be used for the early-stage sensor design and operation-stage sensor maintenance. A case study is conducted to demonstrate the analysis methodology using a commercial building model crated to Flexible Research Platform located at Oak Ridge National Laboratory, USA. [Figure not available: see fulltext.]

Original languageAmerican English
Pages (from-to)769-778
Number of pages10
JournalBuilding Simulation
Volume15
Issue number5
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2021, Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature.

NREL Publication Number

  • NREL/JA-5500-80670

Keywords

  • data-driven modeling
  • fault detection and diagnostics
  • Monte Carlo simulation
  • sensor accuracy
  • sensor fault
  • sensor selection

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