MTTR Prediction of railway rolling stock using regression algorithms

Ragala, Zaynabe; Retbi, Asmaâ; Bennani, Samir

doi:10.1109/iscv54655.2022.9806066

Cited by 2 publications

(4 citation statements)

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“…Maintenance in general remains a very resistant activity in the face of change and presents several constraints considering the companies, namely the age of the assets, change of the assets and their characteristics; the departure of the experts leaving the companies to face daily challenges to ensure this key performance indicator (KPI). The mean time to repair (MTTR) time to repair remains among the most critical indicators that need to be reduced [34]. Hence the essential added value of our project will be an additional and essential aid to technicians in their daily lives.…”

Section: Methodsmentioning

confidence: 99%

“…To make it happen, we built on previous work [34], in which we compared a set of regression algorithms to predict MTTR. The results obtained showed that linear, polynomial, and Lasso outperform other algorithms.…”

Section: Evaluation Of Mttrmentioning

confidence: 99%

“…The results obtained showed that linear, polynomial, and Lasso outperform other algorithms. In this phase of our work, we are going to analyze the duration of the interventions during one month, after the use of MARS, for the same type of failure treated in [34]. We the result presented in Table 1.…”

Section: Evaluation Of Mttrmentioning

confidence: 99%

“…We the result presented in Table 1. In order to analyze the effectiveness of the three regression algorithms-linear, polynomial, and Lasso in predicting the data, the dataset used in a previous study [34] will be updated with new data. The updated dataset will be used to apply the three algorithms and the results for mean squared error (MSE) and R² will be recorded.…”

Section: Evaluation Of Mttrmentioning

confidence: 99%

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Implementing sharing platform based on ontology using a sequential recommender system

Ragala,

Retbi,

Bennani

2023

IJECE

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<span lang="EN-US">While recommender systems have shown success in many fields, accurate recommendations in industrial settings remain challenging. In maintenance, existing techniques often struggle with the “cold start” problem and fail to consider differences in the target population's characteristics. To address this, additional user information can be incorporated into the recommendation process. This paper proposes a recommender system for recommending repair actions to technicians based on an ontology (knowledge base) and a sequential model. The approach utilizes two ontologies, one representing failure knowledge and the other representing asset attributes. The proposed method involves two steps: i) calculating score similarity based on ontology domain knowledge to make predictions for targeted failures and ii) generating Top-N repair actions through collaborative filtering recommendations for targeted failures. An additional module was implemented to evaluate the recommender system, and results showed improved performance.</span>

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Section: Methodsmentioning

confidence: 99%

Section: Evaluation Of Mttrmentioning

confidence: 99%

Section: Evaluation Of Mttrmentioning

confidence: 99%

Section: Evaluation Of Mttrmentioning

confidence: 99%

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