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Portable and non-intrusive fill-state detection for liquid-freight containers based on vibration signals

(2022) SENSORS. 22(20).
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Abstract
Remote, automated querying of fill-states of liquid-freight containers can significantly boost the operational efficiency of rail- and storage-yards. Most existing methods for fill-state detection are intrusive, or require sophisticated instrumentation and specific testing conditions, making them unsuitable here, due to the noisy and changeable surroundings and restricted access to the interior. We present a non-intrusive system that exploits the influence of the fill-state on the container's response to an external excitation. Using a solenoid and accelerometer mounted on the exterior wall of the container, to generate pulsed excitation and to measure the container response, the fill-state can be detected. The decision can be either a binary (empty/non-empty) label or a (quantised) prediction of the liquid level. We also investigate the choice of the signal features for the detection/classification, and the placement of the sensor and actuator. Experiments conducted in real settings validate the algorithms and the prototypes. Results show that the placement of the sensor and actuator along the base of the container is the best in terms of detection accuracy. In terms of signal features, linear predictive cepstral coefficients possess sufficient discriminative information. The prediction accuracy is 100% for binary classification and exceeds 80% for quantised level prediction.
Keywords
fill-state detection, non-intrusive measuring, impulse response, level, prediction

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Citation

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MLA
Song, Yanjue, et al. “Portable and Non-Intrusive Fill-State Detection for Liquid-Freight Containers Based on Vibration Signals.” SENSORS, vol. 22, no. 20, 2022, doi:10.3390/s22207901.
APA
Song, Y., Van Hoecke, E., & Madhu, N. (2022). Portable and non-intrusive fill-state detection for liquid-freight containers based on vibration signals. SENSORS, 22(20). https://doi.org/10.3390/s22207901
Chicago author-date
Song, Yanjue, Ernest Van Hoecke, and Nilesh Madhu. 2022. “Portable and Non-Intrusive Fill-State Detection for Liquid-Freight Containers Based on Vibration Signals.” SENSORS 22 (20). https://doi.org/10.3390/s22207901.
Chicago author-date (all authors)
Song, Yanjue, Ernest Van Hoecke, and Nilesh Madhu. 2022. “Portable and Non-Intrusive Fill-State Detection for Liquid-Freight Containers Based on Vibration Signals.” SENSORS 22 (20). doi:10.3390/s22207901.
Vancouver
1.
Song Y, Van Hoecke E, Madhu N. Portable and non-intrusive fill-state detection for liquid-freight containers based on vibration signals. SENSORS. 2022;22(20).
IEEE
[1]
Y. Song, E. Van Hoecke, and N. Madhu, “Portable and non-intrusive fill-state detection for liquid-freight containers based on vibration signals,” SENSORS, vol. 22, no. 20, 2022.
@article{8773205,
  abstract     = {{Remote, automated querying of fill-states of liquid-freight containers can significantly boost the operational efficiency of rail- and storage-yards. Most existing methods for fill-state detection are intrusive, or require sophisticated instrumentation and specific testing conditions, making them unsuitable here, due to the noisy and changeable surroundings and restricted access to the interior. We present a non-intrusive system that exploits the influence of the fill-state on the container's response to an external excitation. Using a solenoid and accelerometer mounted on the exterior wall of the container, to generate pulsed excitation and to measure the container response, the fill-state can be detected. The decision can be either a binary (empty/non-empty) label or a (quantised) prediction of the liquid level. We also investigate the choice of the signal features for the detection/classification, and the placement of the sensor and actuator. Experiments conducted in real settings validate the algorithms and the prototypes. Results show that the placement of the sensor and actuator along the base of the container is the best in terms of detection accuracy. In terms of signal features, linear predictive cepstral coefficients possess sufficient discriminative information. The prediction accuracy is 100% for binary classification and exceeds 80% for quantised level prediction.}},
  articleno    = {{7901}},
  author       = {{Song, Yanjue and Van Hoecke, Ernest and Madhu, Nilesh}},
  issn         = {{1424-8220}},
  journal      = {{SENSORS}},
  keywords     = {{fill-state detection,non-intrusive measuring,impulse response,level,prediction}},
  language     = {{eng}},
  number       = {{20}},
  pages        = {{19}},
  title        = {{Portable and non-intrusive fill-state detection for liquid-freight containers based on vibration signals}},
  url          = {{http://doi.org/10.3390/s22207901}},
  volume       = {{22}},
  year         = {{2022}},
}

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