An efficient and secure data collection scheme for predictive maintenance of vehicles

Hu, Peng; Chu, Xixi; Lv, Laishui; Kai-zhong, Zuo; Ni, Tianjiao; Wang, Taochun; Shen, Zhangyi

doi:10.1016/j.adhoc.2023.103157

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…This allows researchers to quickly test and verify RL algorithms in a simulation environment without the need for actual robots or real environments. MuJoCo supports various types of tasks and environmental settings, including robot control, object grasping, and movement [32]; [33,34]. It also allows users to customize the environment as needed to meet various research and application needs.…”

Section: Methodsmentioning

confidence: 99%

Reward shaping using directed graph convolution neural networks for reinforcement learning and games

Sang,

Ahmad Khan,

Yin

et al. 2023

Front. Phys.

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Game theory can employ reinforcement learning algorithms to identify the optimal policy or equilibrium solution. Potential-based reward shaping (PBRS) methods are prevalently used for accelerating reinforcement learning, ensuring the optimal policy remains consistent. Existing PBRS research performs message passing based on graph convolution neural networks (GCNs) to propagate information from rewarding states. However, in an irreversible time-series reinforcement learning problem, undirected graphs will not only mislead message-passing schemes but also lose a distinctive direction structure. In this paper, a novel approach called directed graph convolution neural networks for reward shaping φDCN has been proposed to tackle this problem. The key innovation of φDCN is the extension of spectral-based undirected graph convolution to directed graphs. Messages can be efficiently propagated by leveraging a directed graph Laplacian as a substitute for the state transition matrix. As a consequence, potential-based reward shaping can then be implemented by the propagated messages. The incorporation of temporal dependencies between states makes φDCN more suitable for real-world scenarios than existing potential-based reward shaping methods based on undirected graph convolutional networks. Preliminary experiments demonstrate that the proposed φDCN exhibits a substantial improvement compared to other competing algorithms on both Atari and MuJoCo benchmarks.

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Section: Methodsmentioning

confidence: 99%

Reward shaping using directed graph convolution neural networks for reinforcement learning and games

Sang,

Ahmad Khan,

Yin

et al. 2023

Front. Phys.

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show abstract

“…The digitization of industry [45], including maintenance processes, has been a significant focus in recent years [46][47][48]. By leveraging data analytics, Internet of Things devices, and automation, organizations can achieve higher levels of efficiency and productivity in maintenance operations at work [49]. In the context of road transport companies, Caban et al conducted statistical analyses of maintenance parameters for vehicles [32].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Correct Frequency of the Service Inspections of German Passenger Cars—A Case Study on Kazakhstan and Poland

Nurkusheva,

Bembenek,

Berdychowski

et al. 2024

Vehicles

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This article presents a case study on estimating the real service inspection intervals for German-brand passenger cars in Kazakhstan and Poland. This study aimed to identify disparities between the official recommendations of manufacturers for car maintenance and the real data collected in these two countries. The following passenger cars were examined: Audi A6, Q5, and Q8; Porsche Cayenne and Cayenne coupe; and Volkswagen Passat, Polo, Teramont, Tiguan, Touareg, Arteon, Golf, T-Cross, Tiguan all space, Touran, T-Roc, and Up. To assess the difference between real and recommended values, the manufacturer criteria of a recommended mileage of 15,000 and 30,000 km or a time frame of 365 and 730 days to the first service inspection were applied. The data analysis showed that in Kazakhstan, 31.4% of cars did not meet the warranty conditions, while in Poland, it was 21.0%. The dominant criterion that was not met was the time criterion. The assessment of these factors emphasizes the importance of customizing vehicle maintenance schedules to the specific conditions and driving behaviors prevalent in each country. The practical contribution of the article lies in uncovering the discrepancies between official manufacturer recommendations for car maintenance and the actual data collected in Kazakhstan and Poland. By identifying specific models, Volkswagen Touareg and Tiguan in Kazakhstan and Volkswagen Up in Poland, for which the maintenance intervals deviated significantly from those recommended, this study offers valuable insights for optimizing service schedules and improving the efficiency of maintenance practices in these countries. From a scientific perspective, this article contributes by providing empirical evidence of real-world maintenance behaviors for German-brand passenger cars.

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Security enhanced privacy-preserving data aggregation scheme for intelligent transportation system

Zuo,

Chu,

et al. 2024

J Supercomput

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An efficient and secure data collection scheme for predictive maintenance of vehicles

Cited by 6 publications

References 27 publications

Reward shaping using directed graph convolution neural networks for reinforcement learning and games

Reward shaping using directed graph convolution neural networks for reinforcement learning and games

An Analysis of the Correct Frequency of the Service Inspections of German Passenger Cars—A Case Study on Kazakhstan and Poland

Security enhanced privacy-preserving data aggregation scheme for intelligent transportation system

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