Acceleration-Based Deep Learning Method for Vehicle Monitoring

Zhu, Yanjie; Sekiya, Hidehiko; Okatani, Takayuki; Yoshida, Ikumasa; Hirano, Shuichi

doi:10.1109/jsen.2021.3082145

Cited by 8 publications

(16 citation statements)

References 24 publications

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“…To the best of our knowledge, only Zhu et al [37] have so far published an accelerometer-based axle detection method. Here, a shallow Convolutional Neural Network (CNN) is used to detect potential axle sequences, which are then transformed with a continuous wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

“…Afterwards, the axles are detected in the transformed signals by peak-finding methods. The method of Zhu et al [37] requires accelerometers close to the supports. The acceleration signals of these sensors are dominated by the vehicle induced impulses when entering and leaving the bridge, leading to the clear axle recognition in the time domain.…”

Section: Introductionmentioning

confidence: 99%

“…at midspan or quarter span of beam-like bridges) can be employed also for the axle detection, without having to install additional sensors in the proximity of the supports. Continuous-Wavelet-Transformations (CWTs) were used in the present work as they are an effective tool for the analysis of acoustic and visual signals in general [7], furthermore previous work [6,16,34,36,37] have shown that CWTs are an effective tool for axle identification. The wavelet transformed signals are subsequently analysed using a Fully Convolu-tional Network (FCN) that is trained in a supervised manner to perform a binary classification task (axle/no axle).…”

Section: Introductionmentioning

confidence: 99%

“…This enables the signal processing of any length without having to be divided into time windows. Furthermore, the analysis in this way is not limited to certain mother wavelets or certain scales, as in the previously mentioned works that used the CWT [6,16,34,36,37]. To validate our method, we recorded a data set on a railway bridge with sensors distributed across the free span of the bridge on the main girders.…”

Section: Introductionmentioning

confidence: 99%

“…The impulses of the wheel sets are superimposed with the vibrations of the bridge, which does not allow their clear visual identification in the time domain. For many bridges and for common sensor setups for monitoring purposes, similar to the ones used for this study, the method of Zhu et al [37] would not be applicable.…”

Section: Introductionmentioning

confidence: 99%

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Virtual Axle Detector based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

Lorenzen¹,

Riedel²,

Rupp³

et al. 2022

Preprint

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In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is in most cases a prerequisite. To avoid the use of conventional axle detectors and bridge type specific methods, we propose a novel method for axle detection through the placement of accelerometers at any point of a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This enables our method to use acceleration signals at any location of the bridge structure serving as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Our results on the measurement data show that our model detects 95% of the axes, thus, 128,599 of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles can be detected with a maximum spatial error of 20 cm, with a maximum velocity of v max = 56, 3 m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Virtual Axle Detector based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

Lorenzen¹,

Riedel²,

Rupp³

et al. 2022

Preprint

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Virtual Axle Detector: Train Axle Localization based on Bridge Vibrations

Riedel,

Lorenzen,

Rupp

et al. 2023

ce papers

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Infrastructure worldwide is facing the challenge of aging bridges and increasing traffic loads. Prolonged serviceability and safety of these structures can be enabled by Structural Health Monitoring (SHM) methods. Knowledge of the actual operating loads is critical for evaluation of the remaining service life. However, direct measurement of the loads is challenging and requires a significant financial investment. Bridge Weigh‐In‐Motion (BWIM) methods use the structural response of bridge structures to determine loads, but generally rely on accurate knowledge of the position of loads as a function of time. Positions can be determined using conventional axle detectors, but their lifetime is limited, and their installation is expensive. To avoid these problems, we propose an improved Virtual Axle Detector (VAD) with Enhanced Receptive field (VADER) that can detect axles for all bridge types using accelerometers that can be placed anywhere along the bridge. The same data set with 3787 train passages recorded on a steel trough railway bridge under real operating conditions was used. Our results show that, in comparison with VAD, VADER reduces the number of undetected axles by over 79% and detects 99.5% of axles with an average spatial accuracy of 4.6 cm.

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