Smart Structures and Systems

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A semi-supervised interpretable machine learning framework for sensor fault detection

  • Martakis, Panagiotis (Department of Civil, Environmental and Geomatic Engineering, ETH Zurich) ;
  • Movsessian, Artur (School of Engineering, Institute for Infrastructure and Environment, University of Edinburgh) ;
  • Reuland, Yves (Department of Civil, Environmental and Geomatic Engineering, ETH Zurich) ;
  • Pai, Sai G.S. (Intellithink Industrial IoT Labs) ;
  • Quqa, Said (Department of Civil, Chemical, Environmental, and Materials Engineering, University of Bologna) ;
  • Cava, David Garcia (School of Engineering, Institute for Infrastructure and Environment, University of Edinburgh) ;
  • Tcherniak, Dmitri (Bruel & Kjaer Sound and Vibration Measurements) ;
  • Chatzi, Eleni (Department of Civil, Environmental and Geomatic Engineering, ETH Zurich)
  • Received : 2021.05.16
  • Accepted : 2021.09.08
  • Published : 2022.01.25
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Abstract

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

Keywords

Acknowledgement

The authors would like to thank the organizations of the International Project Competition for SHM (IPC-SHM 2020) ANCRiSST, Harbin Institute of Technology (China), and University of Illinois at Urbana-Champaign (USA) for their generously providing the invaluable data from actual structures. The authors also would like to thank the chairs of IPC-SHM 2020 Prof. Hui Li and Prof. Billie F. Spencer Jr for their leadership on the competition. The work presented in this paper was financially supported by the Real-time Earthquake Risk Reduction for a Resilient Europe 'RISE' project, financed under the European Union Horizon 2020 research and innovation program, under grant agreement No 821115, as well as the ETH Risk Center project 'DynaRisk', financed under grant agreement ETH-11 18-1.

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