Predictive Maintenance 4.0 as next evolution step in industrial maintenance development

Poór, Peter; Basl, Josef; Ženíšek, David

doi:10.23919/scse.2019.8842659

Cited by 44 publications

(23 citation statements)

References 7 publications

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“…Predictive maintenance according to [ 29 ] can be described as a continuous process of verifying the functionality of individual components so as to signal in advance the deterioration of the technical condition of the equipment. Papers [ 30 , 31 ] present an overview of the use of machine learning methods in the field of predictive maintenance and also point to the issue of the correct choice of a particular method.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0

Tanuška

Spendla

Kebísek

et al. 2021

Sensors

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One of the big problems of today’s manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.

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Section: Literature Reviewmentioning

confidence: 99%

Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0

Tanuška

Spendla

Kebísek

et al. 2021

Sensors

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“…For FM, the industry 4.0 has participated in the improvement of maintenance towards a maintenance 4.0 [38] and all the technologies that connect human with machine. Industrial Thinking 4.0 and the Internet of Things (IoT) are revolutionizing operations through better visibility, connectivity and improved predictive maintenance (PdM) organization capabilities, resulting in improved reliability and service delivery [39].The integration of these three concepts Lean, FM and Industry 4.0 produces a facilities environment with maximum added value and supported by the latest technologies and tools.…”

Section: Lean Facility 40mentioning

confidence: 99%

Lean Facility Management 4.0: A Case Study

2020

IJETER

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In the Industry 4.0 era, there is a multiplicity of concepts, tools and approaches that aim at operational excellence. Within these tools, Lean has proven its effectiveness through successful case studies around the world. The purpose of this article is to present a case study on the application of the Lean Management as a quality improvement methodology with Industry 4.0 technology solutions for a company in the field of Facilities Management (FM), an area that is lacking in publication. Lean 4.0 has enabled this organization to minimize waste in its processes and more specifically the maintenance process where the use of IoT, CMMS work mobility and various Lean tools have contributed to improve operational performance, social and economic performance.

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“…In this context, maintenance is of paramount importance since avoiding failures, and thus, money loss, is a key requirement in a challenging market requiring high efficiency and availability. For these reasons, maintenance in Industry 4.0 is considered value creator [6]. Furthermore, the process of digitalization and the new forms of cooperation between companies introduced in Industry 4.0 lead to the definition of new business models introducing new services and solutions.…”

Section: Introductionmentioning

confidence: 99%

“…Hashemian and Bean [ 28 ] identify three major categories for predictive maintenance depending on both the kind of information acquired and the source adopted. Most of the time, old equipment requires the additions of new sensors [ 31 ] and the need for new tools for monitoring has made the adoption of PdM quite expensive in the past [ 6 , 27 ]. Nowadays, new technologies in the field of data acquisitions, like the IoT and sensor technology, make the adoption of PdM solutions more accessible [ 28 , 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

A Model for Predictive Maintenance Based on Asset Administration Shell

Cavalieri

Salafia

2020

Sensors

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Maintenance is one of the most important aspects in industrial and production environments. Predictive maintenance is an approach that aims to schedule maintenance tasks based on historical data in order to avoid machine failures and reduce the costs due to unnecessary maintenance actions. Approaches for the implementation of a maintenance solution often differ depending on the kind of data to be analyzed and on the techniques and models adopted for the failure forecasts and for maintenance decision-making. Nowadays, Industry 4.0 introduces a flexible and adaptable manufacturing concept to satisfy a market requiring an increasing demand for customization. The adoption of vendor-specific solutions for predictive maintenance and the heterogeneity of technologies adopted in the brownfield for the condition monitoring of machinery reduce the flexibility and interoperability required by Industry 4.0. In this paper a novel approach for the definition of a generic and technology-independent model for predictive maintenance is presented. Such model leverages on the concept of the Reference Architecture Model for Industry (RAMI) 4.0 Asset Administration Shell, as a means to achieve interoperability between different devices and to implement generic functionalities for predictive maintenance.

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Predictive Maintenance 4.0 as next evolution step in industrial maintenance development

Cited by 44 publications

References 7 publications

Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0

Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0

Lean Facility Management 4.0: A Case Study

A Model for Predictive Maintenance Based on Asset Administration Shell

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