Predictive Maintenance in the Military Domain: A Systematic Review of the Literature

Author:

Dalzochio Jovani1ORCID,Kunst Rafael1ORCID,Barbosa Jorge Luis Victória1ORCID,Neto Pedro Clarindo da Silva1ORCID,Pignaton Edison2ORCID,ten Caten Carla Schwengber2ORCID,da Penha Alex de Lima Teodoro3ORCID

Affiliation:

1. University of Vale do Rio dos Sinos (UNISINOS), Brazil

2. Federal University of Rio Grande do Sul (UFRGS), Brazil

3. Brazilian Army, Brazil

Abstract

Military troops rely on maintenance management projects and operations to preserve the materials’ ordinary conditions or restore them to combat or military training. Maintenance management in the defense domain has its particularities, such as those related to the type of equipment operated, the environment and operating conditions, the need to maintain equipment readiness in cases of external aggression, and the security of the information. This study aims to understand the challenges, principles, scenarios, techniques, and open questions of predictive maintenance (PdM) in the military domain. We conducted a systematic literature review that resulted in the discussion of 43 articles, leading to the identification of 23 challenges and principles, 4 scenarios where predictive maintenance is crucial, besides discussing techniques used for PdM in the military domain. Our results contribute to understanding the perspective of PdM in the defense context.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Reference74 articles.

1. Brazilian Army. 2009. Administrative standards relating to weapons (normas administrativas relativas ao armamento (NARA)). Defense Ministry 1 1 (2009). https://pqrmnt7.eb.mil.br/images/Producao/Legislacao/NARA%202009.pdf.

2. U.S. Army. 2019. Army regulation 750-1 army materiel maintenance policy. p. 225. https://www.kansastag.gov/AdvHTML_Upload/files/AR%20750-1%20Army%20Materiel%20Maintenance%20Policy.pdf.

3. Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation;Atamuradov Vepa;Int. J. Prognost. Health Manage.,2017

4. Advanced diagnostics and prognostics for engine health monitoring

5. A Bayesian Optimized Discriminant Analysis Model for Condition Monitoring of Face Milling Cutter Using Vibration Datasets

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