The benefits of predictive maintenance in manufacturing excellence: a case study to establish reliable methods for predicting failures

被引:0
作者
Anwar Meddaoui
Mustapha Hain
Adil Hachmoud
机构
[1] Hassan II University,
[2] ENSAM,undefined
[3] Sidi Mohamed Ben Abdellah University,undefined
[4] EST,undefined
来源
The International Journal of Advanced Manufacturing Technology | 2023年 / 128卷
关键词
Predictive maintenance; Machine learning; Artificial Neural Network; Random Forest;
D O I
暂无
中图分类号
学科分类号
摘要
In the course of manufacturing excellence, decision makers are consistently confronted with the task of making choices that will enhance and meet industrial plant’s requirements. To this end, it is essential to maintain machines and equipment in a timely manner, which can prove to be one of the primary challenges. Predictive maintenance (PdM) strategy can enable real-time maintenance, providing numerous benefits such as reduced downtime, lower costs, and improved production quality. This article tries to demonstrate efficient physical parameters used in PdM field. The paper presents a case study operated in industrial production process to compare between the most used algorithm in predicting equipment failures. Future research can improve prediction accuracy with other artificial intelligence tools.
引用
收藏
页码:3685 / 3690
页数:5
相关论文
共 50 条
[21]   A Novel End-to-End Provenance System for Predictive Maintenance: A Case Study for Industrial Machinery Predictive Maintenance [J].
Gultekin, Emrullah ;
Aktas, Mehmet S. .
COMPUTERS, 2024, 13 (12)
[22]   A PREDICTIVE MODEL FOR OIL WELL MAINTENANCE: A CASE STUDY IN KAZAKHSTAN [J].
Aktaukenov, D. ;
Alshaalan, M. ;
Omirbekova, Z. ;
Pinsky, E. .
SOCAR PROCEEDINGS, 2024, (01) :48-56
[23]   Deployment of a Smart and Predictive Maintenance System in an Industrial Case Study [J].
Alves, Filipe ;
Badikyan, Hasmik ;
Moreira, Antonio H. J. ;
Azevedo, Joao ;
Moreira, Pedro Miguel ;
Romero, Luis ;
Leitao, Paulo .
2020 IEEE 29TH INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE), 2020, :493-498
[24]   Case Study for Predicting Failures in Water Supply Networks Using Neural Networks [J].
de Sousa Medeiros, Viviano ;
dos Santos, Moises Dantas ;
Brito, Alisson Vasconcelos .
WATER, 2024, 16 (10)
[25]   Advanced ML for predictive maintenance: a case study on remaining useful life prediction and reliability enhancement [J].
Anwar Meddaoui ;
Adil Hachmoud ;
Mustapha Hain .
The International Journal of Advanced Manufacturing Technology, 2024, 132 :323-335
[26]   An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study [J].
Bekar, Ebru Turanoglu ;
Nyqvist, Per ;
Skoogh, Anders .
ADVANCES IN MECHANICAL ENGINEERING, 2020, 12 (05)
[27]   Advanced ML for predictive maintenance: a case study on remaining useful life prediction and reliability enhancement [J].
Anwar, Meddaoui ;
Adil, Hachmoud ;
Mustapha, Hain .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2024, 132 (1-2) :323-335
[28]   Design of experiments and machine learning to improve robustness of predictive maintenance with application to a real case study [J].
Salmaso, Luigi ;
Pegoraro, Luca ;
Giancristofaro, Rosa Arboretti ;
Ceccato, Riccardo ;
Bianchi, Alberto ;
Restello, Silvio ;
Scarabottolo, Davide .
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2022, 51 (02) :570-582
[29]   Scaling Up Deep Learning Based Predictive Maintenance for Commercial Machine Fleets: a Case Study [J].
Ulmer, Markus ;
Zgraggen, Jannik ;
Pizza, Gianmarco ;
Huber, Lilach Goren .
2022 9TH SWISS CONFERENCE ON DATA SCIENCE (SDS), 2022, :40-46
[30]   Perspectives on Smart Maintenance Technologies - A Case Study in Large Manufacturing Companies [J].
Giliyana, San ;
Salonen, Antti ;
Bengtsson, Marcus .
SPS 2022, 2022, 21 :255-266