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Smart Greasing System in Mining Facilities: Proactive and Predictive Maintenance Case Study

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Smart Applications and Data Analysis (SADASC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1677))

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Abstract

Maintenance has attracted lately research attention. Interesting advances were brought to the process industry to boost Digital transformation. Specifically, Predictive maintenance has a big role in enhancing productivity and machine reliability. Machines lubrication is a systematic Maintenance activity preventing later parts degradation. Promoting this activity should be taken more seriously as more than 50% of roller bearing in mining industry defects are due to inadequate lubrication. Additionally, actual industrial lubrication practices lack assistance and efficiency. To improve lubrication and generally maintenance in the digitalization context, the process industry requires an upgrade of operations automation and the integration of a certain degree of intelligence. This smart level must be reached in components lubrication, leverage decision-making, and keep up with the mining industry challenges. This study proposes a smart greasing system to achieve proactivity in the ore mining industry lubrication activity as a Proof-of-Concept. A fuzzy logic controller is used to compute input parameters vibrations, temperature, and humidity, collected from smart sensors implemented in a crusher machine. The Controller calculates the dosage correction coefficient to change the grease output on the centralized greasing test bench. This Proof-of-Concept is destined to reduce bearing defects related to the lubrication problem. Last, the Authors present the vision of combining the smart greasing concept and the previously developed predictive maintenance system for a better mining process maintenance real-time monitoring.

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Authors and Affiliations

  1. Laboratory of Complex Cyber Physical Systems (CCPS)—The National Higher School of Arts and Crafts (ENSAM)—Sciences Faculty Ben M’Sik, Hassan II University, Mers Sultan BP 916, Casablanca, Morocco

    Mariya Guerroum, Mourad Zegrari & AbdelHafid Ait Elmahjoub

Authors
  1. Mariya Guerroum
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  2. Mourad Zegrari
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  3. AbdelHafid Ait Elmahjoub
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Correspondence to Mariya Guerroum .

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Editors and Affiliations

  1. University of Hassan II Casablanca, Casablanca, Morocco

    Mohamed Hamlich

  2. ISAE-ENSMA, Poitiers, France

    Ladjel Bellatreche

  3. Arts et Metiers Institute of Technology, Paris, France

    Ali Siadat

  4. University of Cordoba, Cordoba, Spain

    Sebastian Ventura

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Guerroum, M., Zegrari, M., Elmahjoub, A.A. (2022). Smart Greasing System in Mining Facilities: Proactive and Predictive Maintenance Case Study. In: Hamlich, M., Bellatreche, L., Siadat, A., Ventura, S. (eds) Smart Applications and Data Analysis. SADASC 2022. Communications in Computer and Information Science, vol 1677. Springer, Cham. https://doi.org/10.1007/978-3-031-20490-6_28

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  • DOI: https://doi.org/10.1007/978-3-031-20490-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20489-0

  • Online ISBN: 978-3-031-20490-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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