Deep learning-based prediction of piled-up status and payload distribution of bulk material

Yao, Zongwei; Huang, Qiuping; Ji, Ze; Li, XueFei; Bi, Qiushi

doi:10.1016/j.autcon.2020.103424

Cited by 17 publications

(7 citation statements)

References 38 publications

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“…The authors used data collected at the WIM station (vehicle class and weight data) to first reduce the high-dimensional traffic features using the PCA technique and then applied the K-means algorithm to establish appropriate TCC groups. [51] addressed the problem of payload distribution in goods transportation vehicles under complex environments. The payload distribution in the truck is fundamental to ensuring its long useful life.…”

Section: Related Workmentioning

confidence: 99%

Predicting Weighing Deviations in the Dispatch Workflow Process: A Case Study in a Cement Industry

Barros

Cunha²,

Martins³

et al. 2023

IEEE Access

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The emergence of the Industry 4.0 concept and the profound digital transformation of the industry plays a crucial role in improving organisations' supply chain (SC) performance, consequently achieving a competitive advantage. The order fulfilment process (OFP) consists of one of the key business processes for the organization SC and represents a core process for the operational logistics flow. The dispatch workflow process consists of an integral part of the OFP and is also a crucial process in the SC of cement industry organizations. In this work, we focus on enhancing the order fulfilment process by improving the dispatch workflow process, specifically with respect to the cement loading process. Thus, we proposed a machine learning (ML) approach to predict weighing deviations in the cement loading process. We adopted a realistic and robust rolling window scheme to evaluate six classification models in a realworld case study, from which the random forest (RF) model provides the best predictive performance. We also extracted explainable knowledge from the RF classifier by using the Shapley additive explanations (SHAP) method, demonstrating the influence of each input data attribute used in the prediction process.INDEX TERMS Anomaly prediction, Dispatch workflow process, Machine learning, Order fulfilment process, Weighing systems.

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Section: Related Workmentioning

confidence: 99%

Predicting Weighing Deviations in the Dispatch Workflow Process: A Case Study in a Cement Industry

Barros

Cunha²,

Martins³

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Since vision cameras are more susceptible to environmental disturbances, some scholars perform indoor experiments to circumvent environmental disturbances (Yao et al., 2021), but this is not in line with reality and will affect the practicality of the proposed method.…”

Section: Related Workmentioning

confidence: 99%

A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

Guan

Chen

Wang

et al. 2022

Computer aided Civil Eng

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The development of autonomous detection technology is imperative in the field of construction. The bucket fill factor is one of the main indicators for evaluating the productivity of construction vehicles. Bucket detection is a prerequisite for bucket trajectory planning. However, previous studies have been conducted under ideal environments, a specific single environment, and several normal environments without considering the actual harsh environments at construction sites. Therefore, seven extreme environments are set in this paper to fill this gap, and an effective method is proposed. First, a novel framework for image restoration under extreme environments is proposed. It applies to all tasks conducted by vision on construction sites. Second, a combination of segmentation and classification networks is used for the first time in this area. Multitask learning is used to discover a positive correlation between fill factor estimation and bucket detection. Furthermore, probabilistic methods and transfer learning were introduced, and excellent results were achieved (97.40% accuracy in fill factor estimation and 99.76% accuracy in bucket detection for seven extreme environments).

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“…Recently, Zhang et al proposed a digging trajectory planning method based on LiDAR point cloud (Zhang et al, 2022). Yao et al (2021) applied computer vision and deep learning for prediction of payload distribution. However, research on autonomous driving of electric shovel is relatively inadequate which affects the entire automation process.…”

Section: Introductionmentioning

confidence: 99%

Research on unmanned electric shovel autonomous driving path tracking control based on improved pure tracking and fuzzy control

Wang

et al. 2023

Journal of Field Robotics

Self Cite

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This paper proposes a path tracking control method combining pure tracking algorithms and self‐adaptive fuzzy control for autonomous driving of an unmanned electric shovel. An improved pure tracking controller was designed based on the kinematic model of heavy crawler taking both the value of deviation and its variation as inputs with the crawler speed on each side as output. The proposed controller and MPC algorithm were simulated using MATLAB for comparison. The results show that the proposed controller has more anthropomorphic characteristics than the MPC method. To verify the actual control effect of the controller, experiments were carried out using a prototype electric shovel for different working conditions. The experimental results proved that the controller is able to meet the control requirements for unmanned electric shovel path tracking.

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Deep learning-based prediction of piled-up status and payload distribution of bulk material

Abstract: The proposed method can automatically predict the Piled-up Status and Payload Distribution (PSPD) of bulk materials in terms of material mass and dumping position from pure images.

Cited by 17 publications

References 38 publications

Predicting Weighing Deviations in the Dispatch Workflow Process: A Case Study in a Cement Industry

Predicting Weighing Deviations in the Dispatch Workflow Process: A Case Study in a Cement Industry

A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

Research on unmanned electric shovel autonomous driving path tracking control based on improved pure tracking and fuzzy control

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