Affiliation:
1. University of Castilla-La Mancha, Spain & Universidade de Lisboa, Portugal
Abstract
Condition-based maintenance (CBM) may be considered an essential part of the Industry 4.0 environment because it can improve production processes through the use of the latest digital technologies. The literature includes a large number of contributions on new techniques for diagnosis, signal treatment, analysis of technical parameters, and prognosis. However, to obtain the expected benefits of a vibration analysis program, it is necessary to choose the instruments and introduction process best suited to the organization, and so guarantee the best results using data-driven decision making in accordance with the needs of Industry 4.0. Despite the importance of these decisions, no relevant models are found in the literature. This contribution describes a fuzzy multicriteria model for choosing the most suitable technology in vibration analysis. The goal is to create a model that is easy for organizations to use, and which reflects the judgements of a number of experts in maintenance and vibration analysis. The model has been applied to a Spanish state-run healthcare organization.
Reference45 articles.
1. Ballesteros, F. (2014). Equipos portátiles de medida de vibración para diagnóstico de maquinaria. Preditec-IRM, NT08/2.
2. A multi-criteria model for auditing a Predictive Maintenance Programme
3. Facilitating bid evaluation in public call for tenders: a socio-technical approach
4. Barm, H. M., Deshpande, A. A., & Patil, S. S. (2015). Availability Improvement by Early Detection of Motor Bearing Failure Using Comprehensive Condition Monitoring Techniques at DTPS. Vibration Engineering and Technology of Machinery, 1101-1111.
5. Selection of diagnostic techniques and instrumentation in a predictive maintenance program. A case study