Automobile Maintenance Modelling Using gcForest

Chen, Chong; Liu, Ying; Sun, Xianfang; Cairano-Gilfedder, Carla Di; Titmus, Scott

doi:10.1109/case48305.2020.9216745

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…for training. The experimental results showed that prediction accuracy was promoted with the enrichment of GIS data [34]. However, the data integration of maintenance data and GIS data can be further investigated.…”

Section: Problem Statementmentioning

confidence: 99%

An integrated deep learning-based approach for automobile maintenance prediction with GIS data

Chen

Liu

Sun

et al. 2021

Reliability Engineering & System Safety

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Section: Problem Statementmentioning

confidence: 99%

An integrated deep learning-based approach for automobile maintenance prediction with GIS data

Chen

Liu

Sun

et al. 2021

Reliability Engineering & System Safety

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“…An algorithm then predicts the breakdowns of equipment in a vehicle fleet. Among these algorithms is the Multi-Grained Cascade Forest (gcForest), which was originally developed for image classification and subsequently became a useful tool for automotive maintenance [2]. These researchers conducted a study to introduce gcForest into automotive maintenance based on maintenance history data from geographic information systems (GIS).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus, the objective of this paper is to provide the automotive maintenance process with more effective and efficient based on the exploitation of historical failure data using artificial intelligence, deep learning and machine learning Therefore, we will establish a model to predict the equipment failure time through an approach similar to that of the work of Chen et al [2]. The rest of the paper is organized as follows: Section 2 presents a review of the literature.…”

Section: Introductionmentioning

confidence: 99%

Failure Prediction and Intelligent Maintenance of a Transportation Company’s Urban Fleet

Foké¹,

Kenné²,

Diego³

2023

JTTs

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The present work deals with intelligent vehicle fleet maintenance and prediction. We propose an approach based primarily on the history of failures data and on the geographical data system. The objective here is to predict the date of failures for a fleet of vehicles in order to allow the maintenance department to efficiently deploy the proper resources; we further provide specific details regarding the origins of failures, and finally, give recommendations. This study used the Société de transport de Montréal (STM)'s historical bus failure data as well as weather data from Environment Canada. We thank Facebook's Prophet, Simple Feed-forward, and Beats algorithms (Uber), we proposed a set of computer codes that allow us to identify the 20% of buses that are responsible for the 80% of failures by mean of the failure history. Then, we deepened our study on the unreliable equipments identified during the diffusion of our computer code This allowed us to propose probable predictions of the dates of future failures. To ensure the validity of the proposed algorithm, we carried out simulations with more than 250,000 data. The results obtained are similar to the predicted theoretical values.

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“…The objective of this proposal is different than ours: in Derse et al (2021), the aim is to detect outliers in the ECU system, which can be due to an intrusion or fault in a sensor, where our analysis focuses on fault prediction regarding in-vehicle equipment. In Chen et al (2020), prediction of time-between-failures is performed. Data are combined with GIS information so that contextual information like weather conditions is used.…”

Section: Pdm In Vehicle Fleetsmentioning

confidence: 99%

A context-aware unsupervised predictive maintenance solution for fleet management

Giannoulidis

Gounaris

2022

J Intell Inf Syst

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We deal with the problem of predictive maintenance (PdM) in a vehicle fleet management setting following an unsupervised streaming anomaly detection approach. We investigate a variety of unsupervised methods for anomaly detection, such as proximity-based, hybrid (statistical and proximity-based) and transformers. The proposed methods can properly model the context in which each member of the fleet operates. In our case, the context is both crucial for effective anomaly detection and volatile, which calls for streaming solutions that take into account only the recent values. We propose two novel techniques, a 2-stage proximity-based one and context-aware transformers along with advanced thresholding. In addition, to allow for testing PdM techniques for vehicle fleets in a fair and reproducible manner, we build a new fleet-like benchmarking dataset based on an existing dataset of turbofan simulations. Our evaluation results show that our proposals reduce the maintenance costs compared to existing solutions.

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Automobile Maintenance Modelling Using gcForest

Cited by 8 publications

References 27 publications

An integrated deep learning-based approach for automobile maintenance prediction with GIS data

An integrated deep learning-based approach for automobile maintenance prediction with GIS data

Failure Prediction and Intelligent Maintenance of a Transportation Company’s Urban Fleet

A context-aware unsupervised predictive maintenance solution for fleet management

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