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Developing a Web Platform for the Management of the Predictive Maintenance in Smart Factories

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Abstract

Industry 4.0 is a tsunami that will invade the whole world. The real challenge of the future factories requires a high degree of reliability both in machinery and equipment. Thereupon, shifting the rudder towards new trends is an inevitable obligation in this fourth industrial revolution where the maintenance system has radically changed to a new one called predictive maintenance 4.0 (PdM 4.0). This latter is used to avoid predicted problems of machines and increase their lifespan taking into account that if machines have not any predicted problem, they will never be checked. However, in order to get successful prediction of any kind of problems, minimizing energy and resources consumption along with saving costs, this PdM 4.0 needs many new emerging technologies such as the internet of things infrastructure, collection and distribution of data from different smart sensors, analyzing/interpreting a huge amount of data using machine/deep learning…etc. This paper is devoted to present the industry 4.0 and its specific technologies used to ameliorate the existing predictive maintenance strategy. An example is given via a web platform to get a clear idea of how PdM 4.0 is applied in smart factories.

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Notes

  1. Batna is the main city of Batna Province which is located in north eastern of Algeria. Batna is considered as the fifth largest city in Algeria.

  2. Takt time is the rate at which you need to complete a product to meet the customer's demand.

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Authors and Affiliations

  1. Automatic and Production Laboratory (LAP, Laboratoire d’Automatique et de Productique), Department of Industrial Engineering, Mostapha BenBoulaid University, 05000, Batna 2, Algeria

    Karima Aksa, Samia Aitouche, Hamza Bentoumi & Ibrahim Sersa

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  1. Karima Aksa
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  2. Samia Aitouche
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  3. Hamza Bentoumi
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  4. Ibrahim Sersa
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Correspondence to Karima Aksa.

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Aksa, K., Aitouche, S., Bentoumi, H. et al. Developing a Web Platform for the Management of the Predictive Maintenance in Smart Factories. Wireless Pers Commun 119, 1469–1497 (2021). https://doi.org/10.1007/s11277-021-08290-w

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