IDLab, Ghent University-imec, 9000 Gent, Belgium.
Sensors (Basel). 2022 Oct 17;22(20):7901. doi: 10.3390/s22207901.
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 (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.
远程、自动查询液体货物集装箱的填充状态，可以显著提高铁路和仓库的运营效率。大多数现有的填充状态检测方法都是侵入式的，或者需要复杂的仪器和特定的测试条件，由于周围环境嘈杂多变，并且无法进入内部，因此不适合这里。我们提出了一种非侵入式系统，利用填充状态对容器对外界激励的响应的影响。使用安装在容器外壁上的电磁阀和加速度计，产生脉冲激励并测量容器响应，可以检测填充状态。决策可以是一个（空/满）标签，也可以是（量化）液位预测。我们还研究了用于检测/分类的信号特征的选择，以及传感器和执行器的位置。在真实环境中进行的实验验证了算法和原型。结果表明，传感器和执行器沿着容器底部的放置在检测精度方面是最好的。就信号特征而言，线性预测倒谱系数具有足够的鉴别信息。二进制分类的预测精度为 100%，量化水平预测的精度超过 80%。
