Towards prediction of machine failures: overview and first attempt on specific automotive industry application

Ciancio, Vincent; Homri, Lazhar; Dantan, Jean‐Yves; Siadat, Ali

doi:10.1016/j.ifacol.2020.11.047

Cited by 5 publications

(6 citation statements)

References 19 publications

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“…Figure 1 helps understanding where this Intelligent Maintenance System (IMS) intervenes on the different steps of the framework proposed in Ciancio et al (2020).…”

Section: Motivationsmentioning

confidence: 99%

“…Their goal is to keep the material melted and at the correct temperature. Similarly to the welding units application presented in Ciancio et al (2020), the system is composed of one or multiple band heaters, and one thermocouple per 'zone'. The zones correspond to the physical location of each heating system, from the feed hopper to the start of the head.…”

Section: Principle Of the Processmentioning

confidence: 99%

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Development of a flexible data management system, to implement predictive maintenance in the Industry 4.0 context

Ciancio

Homri

Dantan

et al. 2023

International Journal of Production Research

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In recent years, the way that maintenance is carried out has evolved due to the incorporation of digital tools and Industry 4.0 concepts. By connecting to and communicating with their production system, companies can now gather information about the current and future health of the equipment, enabling more efficient control through a process called predictive maintenance (PdM). The goal of PdM is to reduce unplanned downtimes and proactively address maintenance needs before failures occur. However, it can be challenging for industrial practitioners to implement an intelligent maintenance system that effectively manages data. This paper presents a methodology for developing and implementing a PdM system in the automotive industry, using open standards and scalable data management capabilities. The platform is validated through the presentation of two industry use cases.

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“…Figure 1 helps understanding where this Intelligent Maintenance System (IMS) intervenes on the different steps of the framework proposed in Ciancio et al (2020).…”

Section: Motivationsmentioning

confidence: 99%

Section: Principle Of the Processmentioning

confidence: 99%

Development of a flexible data management system, to implement predictive maintenance in the Industry 4.0 context

Ciancio

Homri

Dantan

et al. 2023

International Journal of Production Research

Self Cite

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“…This is possible through the estimation of the health state and the prediction of Remaining Useful Life (RUL). • Health Management (HM): once the fault and health state of a device have been assessed, decision making can take place (Ciancio et al, 2020;Vieira et al, 2018;Mourtzis & Vlachou, 2018). Health management services must first integrate the fault and health information.…”

Section: Servicesmentioning

confidence: 99%

“…The most frequently measured variables were vibration (Surico et al, 2020;Aqueveque et al, 2021;Magadán et al, 2020) and temperature (Ciancio et al, 2020;Iqbal et al, 2019;Short & Twiddle, 2019), followed by electric current (Ge et al, 2019;Strauß et al, 2018;Barksdale et al, 2018). The most analyzed components were motors, induction or otherwise (Rubio et al, 2018;Eiskop et al, 2017;Talmoudi et al, 2019), followed by bearings (Richter et al, 2019;Bernal et al, 2018).…”

Section: Proactive Maintenancementioning

confidence: 99%

Smart retrofitting in maintenance: a systematic literature review

2022

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The last decade saw the rise of digitalization and data-supported decision making in the manufacturing industry: the Fourth Industrial Revolution. This trend, also known as Industry 4.0, allows manufacturing enterprises to discover manufacturing uncertainties and measure their real manufacturing capability. One of the ways in which Industry 4.0 trends have been exploited is in the improvement of maintenance, which went from following planning-focused paradigms to more proactive-focused stances. Enabling the Industry 4.0 vision for maintenance purposes has historically required companies to either replace or upgrade their existing legacy devices. It is through the latter course of action that Smart retrofitting in maintenance (SRM) intends to bring value to enterprises. This work aims to present a systematic literature review on SRM, utilizing the oft-cited PRISMA methodology. Through this analysis, a definition of SRM that reflects the current state of the art is proposed. Furthermore, the research in SRM applied in the context of different maintenance strategies is assessed (i.e. reactive, planned, proactive and strategic maintenance), and the most common drivers and challenges in SRM are presented. Finally, a roadmap for the implementation of SRM is proposed. The analysis of the SRM literature reveals that there are important research opportunities in the exploitation of SRM for strategic maintenance and asset management. The authors hope that this document leads to the consolidation of a new research area that aims to add value to maintenance in enterprises through the application of smart retrofitting in preexisting legacy devices.

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“…Lee et al [4] also stress that a proper integration of predictive maintenance can lead to a significant reduction of operational costs, which is one of the main objectives of I4.0, including, for example: schedule of maintenance actions; reduction of unexpected stoppage; reduction of unavailability by stopping when it is really necessary (i.e., when the degradation state requires it); development of data history for each equipment; elimination of unnecessary replacement of components if the machine is operating satisfactorily, and so on. Although CBM and PdM have been studied in the context of I4.0 in recent years, few researchers have paid attention to this type of maintenance techniques in the automotive industry [49]. Nevertheless, a major issue in production planning of the In recent years, several techniques have been developed to monitor and control the desired parameters of the equipment, such as analysis of vibrations, temperatures, pressures, ultrasounds, currents, and voltages [41][42][43][44][45].…”

Section: Condition-based and Predictive Maintenancementioning

confidence: 99%

The Role of Industry 4.0 and BPMN in the Arise of Condition-Based and Predictive Maintenance: A Case Study in the Automotive Industry

et al. 2021

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This article addresses the evolution of Industry 4.0 (I4.0) in the automotive industry, exploring its contribution to a shift in the maintenance paradigm. To this end, we firstly present the concepts of predictive maintenance (PdM), condition-based maintenance (CBM), and their applications to increase awareness of why and how these concepts are revolutionizing the automotive industry. Then, we introduce the business process management (BPM) and business process model and notation (BPMN) methodologies, as well as their relationship with maintenance. Finally, we present the case study of the Renault Cacia, which is developing and implementing the concepts mentioned above.

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Towards prediction of machine failures: overview and first attempt on specific automotive industry application

Cited by 5 publications

References 19 publications

Development of a flexible data management system, to implement predictive maintenance in the Industry 4.0 context

Development of a flexible data management system, to implement predictive maintenance in the Industry 4.0 context

Smart retrofitting in maintenance: a systematic literature review

The Role of Industry 4.0 and BPMN in the Arise of Condition-Based and Predictive Maintenance: A Case Study in the Automotive Industry

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