Approach for preventive maintenance planning of machine tools

This paper addresses a common problem to manufacturing companies: the maintenance of machine tools and their components. Preventive maintenance has always been a great challenge for companies, due to the need of predicting failures or production shutdowns, which requires knowledge and resources. However, the planning of machine tools maintenance presents itself as an even more complex problem due to the distinct lifetimes of their components. Age-based preventive replacement and Block replacement models define optimal replacement intervals for one item based on associated maintenance costs. A machine tool can be seen as a serial system of components or items. The concepts of group technology and clustering can be used to group components together in order to define common preventive maintenance intervals and reduce the number of production stops. In the literature, some contributions are found. However, the defined groups are static as well as the preventive maintenance intervals. This paper presents a conceptual model for the definition of dynamic clusters and intervals. It also presents an application to record the inputs, data collected in real time, needed to group components and set up preventive maintenance intervals. The developed application is being implemented in a metalworking company.We would like to thank the companies that are involved in the project and express our appreciation for the commitment of the employees involved. This work has been supported by Norte 010247 FEDER 017833 – TechParts I&D

Switching- and hedging- point policy for preventive maintenance with degrading machines: application to a two-machine line

Maintenance and production are frequently managed as separate activities although they do interact. Disruptive events such as machine failures may find the company unready to repair the machine immediately leading to time waste. Preventive Maintenance may be carried out and maintenance time reduced to the effective task duration, in order to prevent time waste. Companies and researchers have been focusing on policies able to mitigate the impact of Preventive Maintenance on system availability, by exploiting the knowledge about degradation profiles in machines and the joint information from the machine state and the buffer level. In this work, the mathematical proof of the optimal threshold-based control policy for Preventive Maintenance with inventory cost, maintenance cost, backlog cost is provided. The control policy is defined in terms of buffer thresholds and dependency of the thresholds on the degradation condition. The optimal control policy is proved to include a combination of switching points and hedging points, where the first ones activate the Preventive Maintenance for a given condition and the latter ones control the production rate in order to minimize the surplus. An extensive experimental campaign analyzes the impact of system parameters such as the Maintenance duration on the cost function. The results show that there exists cases in which the optimal policy is dominated by the effect of the hedging points or the switching points, alternatively. Therefore, the proposed method is used to provide suggestions to the management for operative decisions, in order to choose the policy fitting best the system

An Approach to Risk Quantification Based on Pseudo-Random Failure Rates

3rd IFAC Workshop on Advanced Maintenance Engineering, Services and Technology AMEST 2016: Biarritz, France, 19—21 October 2016. - IFAC-PapersOnLine, Volume 49, Issue 28, 2016, Pages 179-184The risk quantification is one of the most critical areas in asset management (AM). The relevant information from the traditional models can be shown in risk matrices that represent a static picture of the risk levels and are according to its frequency and its impact (consequences). These models are used in a wide spectrum of knowledge domains. In this paper, we describe a quantitative model using the reliability and failure probability (as frequency in our risk model), and the preventive and corrective costs (as consequences in our risk model). The challenge here will be the treatment of reliability based on failure rate values with different e random distributions (normal, triangular etc.) according to the available data. These possible values will enable the simulation of the behavior of the system in terms of reliability and, consequently, to use this information for making a risk based analysis. The traditional risk-cost-benefit models applied to maintenance usually provides an optimum for the time to apply a preventive task. But in this case, a time window is obtained showing minimum and maximum thresholds for the best time to apply the preventive maintenance task, together with other interesting statistics useful for the improvement of complex industrial asset management

Firm productivity, profit and business goal satisfaction: an assessment of maintenance decision effects on small and medium scale enterprises (SME’s)

[EN] This study was carried out to identify which factors are most relevant to managers of SMEs in maintenance decision making, and to investigate how these factors influence the realization of business goals satisfactorily, using structural equation modelling, partial least square design (PLS-SEM) to establish significant relationships between manifest and latent variables. A study of maintenance cost vis a vis the number of maintenance works carried out and profits realized was conducted to ascertain correlations and identify which factors played key roles in profit maximization. Results showed that with increasing level of maintenance for SMEs, profit margins reduced significantly. Also, an R2 value of 0.83 showed that the latent variable, business goal satisfaction was explained to a high degree (83%) by the manifest variables. Rentals of equipment from third parties (0.27), halting production (0.11) and outsourcing (0.39) were less considered for business sustainability per correlation coefficients than funds (0.79), and the possibilities to carry out both corrective (0.64) and preventive (0.58) maintenance works.  F-square value greater than zero was realized (0.387) and this showed reliability of the both inner and outer models. These findings can be used in building a decision tool or framework that will best suit SMEs with high financial budget constraints.Owusu-Mensah, D.; Quaye, EK.; Brako, L. (2021). Firm productivity, profit and business goal satisfaction: an assessment of maintenance decision effects on small and medium scale enterprises (SME’s). Journal of Applied Research in Technology & Engineering. 2(1):23-31. https://doi.org/10.4995/jarte.2021.14615OJS233121Al-Tabbaa, O., Ankrah, S. (2016). 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Reliability Engineering & System Safety, 91(7), 839-848. https://doi.org/10.1016/j.ress.2005.08.006Hair, Jr, Joseph, F., Tomas, G., Hult, M., Ringle, C., Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.Jiang, R., Murthy, D.N.P. (2008). Maintenance: Decision Models for Management. Science press, Beijing, China.Joo, S-J. (2009). Scheduling preventive maintenance for modular designed components: A dynamic approach. European Journal of Operational Research, 192(2), 512-520. https://doi.org/10.1016/j.ejor.2007.09.033Lee, H. (2005). A cost/benefit model for investments in inventory and preventive maintenance in an imperfect production system. Computers and Industrial Engineering, 48(1), 55-68. https://doi.org/10.1016/j.cie.2004.07.008Liu, X., Wang, W., Peng, R. (2015). An integrated production: inventory and preventive maintenance model for a multiproduct production system. Reliab Eng Syst Safety, 137(2), 76-86. https://doi.org/10.1016/j.ress.2015.01.002Liu, X., Zheng, J., Fu, J., Ji, J., Chen, G. (2017). Multi-level optimization of maintenance plan for natural gas pipeline systems subject to external corrosion. Journal of Natural Gas Science and Engineering, 50, 64-73. https://doi.org/10.1016/j.jngse.2017.11.021Ma, J., Cheng, L., Li, D. (2018). Road Maintenance Optimization Model Based on Dynamic Programming in Urban Traffic Network. Journal of Advanced Transportation. Article ID 4539324, 11 pages. https://doi.org/10.1155/2018/4539324Marquez, A.C., Gupta, J.N.D. (2006). Contemporary maintenance management: process, framework and supporting pillars. Omega, 34(3), 313-326. https://doi.org/10.1016/j.omega.2004.11.003Nourelfath, M., Nahas, N. & Ben-Daya, M. (2015). Integrated preventive maintenance and production decisions for imperfect processes. Reliab Eng Syst Safety, 148, 21-31. https://doi.org/10.1016/j.ress.2015.11.015Olivotti D., Passlick J., Dreyer S., Lebek B., Breitner M.H. (2018) Maintenance Planning Using Condition Monitoring Data. In: Kliewer N., Ehmke J., Borndörfer R.(eds) Operations Research Proceedings 2017. https://doi.org/10.1007/978-3-319-89920-6_72Pallant, J. (2007). SPSS survival manual, 3rd. Edition. McGrath Hill.Parida, A., Kumar, U. (2016). Applications and Case Studies. Maintenance performance measurement (MPM): issues and challenges. Journal of Quality in Maintenance Engineering, 12(3), 239-251. https://doi.org/10.1108/13552510610685084Qiu, Q., Cui, L., Shen, J., Yang, L. (2017). Optimal maintenance policy considering maintenance errors for systems operating under performance-based contracts. Comput Industr Eng., 112, 147-155. https://doi.org/10.1016/j.cie.2017.08.025Ruschel, E., Santos, E.A.P. & Loures, E.D.F.R. (2017). Industrial maintenance decision-making: a systematic literature review. J Manuf Syst., 45, 180-194. https://doi.org/10.1016/j.jmsy.2017.09.003Shayesteh, E., Yu, J., Hilber, P. (2018). Maintenance optimization of power systems with renewable energy sources integrated. Energy, 149, 577-586. https://doi.org/10.1016/j.energy.2018.02.066Shen, J., Zhu, K. (2017). An uncertain single machine scheduling problem with periodic maintenance. Knowledge-Based Systems, 144, 32-41. https://doi.org/10.1016/j.knosys.2017.12.021Stebbins, R. A. (2001). Exploratory research in the social sciences (Vol. 48). Sage.Van, P.D., Bérenguer, C. (2012). Condition-based maintenance with imperfect preventive repairs for a deteriorating production system. Qual Reliab Eng., 28(6), 624-633. https://doi.org/10.1002/qre.1431Verbert, K., Schutter, B.D., Babuska, R. (2017). Timely condition-based maintenance planning for multi-component systems. Reliab Eng Syst Safety, 159, 310-321. https://doi.org/10.1016/j.ress.2016.10.032Yang, L., Ma, X., Zhao, Y. (2017). 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Design of a Total Productive Maintenance model for effective implementation : case study of a chemical manufacturing company

Abstract: In today’s industries, the concept of Total Productive Maintenance (TPM) has been widely accepted and implemented yet it’s still possible to find industries facing maintenance challenges. The focus of this paper was to develop an effective TPM model to improve the maintenance system at a chemical manufacturing company in Zambia. The researchers set objectives to assess the current maintenance system, to determine the overall equipment effectiveness and to identify key performance indicators and success factors of TPM. Data relevant to the research was collected using designed questionnaires, structured interviews, direct observations and company records. The results of the research came double folded by reviewing that, the maintenance department employed 67.6% breakdown maintenance, 24.3% preventive maintenance and 8.1% not applicable. The research also reviewed that 78% of the time the operators were not involved in maintenance activities with only 14% operator involvement. As regards to the effectiveness of the maintenance technique( s) used, 19% was recorded poor, 65% fair, 8% good and 8% not applicable. Overall equipment effectiveness (OEE) was calculated at 37% which was below the world class standard by 50%. Equipment downtime was a major cause of plant under utilization with 52% caused by shortage of spares, 32% shortage of raw materials, 8% due to power problems and 8% not applicable. TPM awareness deduced 70.5% of the employees been aware of the TPM concept while 14.7% indicated the concept of TPM would help improve the current maintenance system and 14.7% were not sure. 29.5% of the employees were not aware of TPM with 64.3% not sure that the TPM concept can help improve the current maintenance system. Based on these results, knowledge and information sharing, operator involvement and training should be considered. The researchers then designed a TPM model which would result in effective implementation of TPM for higher competitiveness in the dynamic business environment

Mantenimiento preventivo de motores marinos de Diesel mediante aceites fósiles recuperados

A preventive maintenance system is designed to meet quality, efficiency, and safety requirements. These requirements have basically been formalized. In particular, it refers to the security requirements stipulated by international conventions. Failure to comply with these requirements is mainly formal and will leave the system in a state of incompetence (Bielawski, Piotr, 2021), so the specific objective of the preventive maintenance process is to take advantage of knowledge of breakdowns and accidents to achieve the maximum possible safety. with the lowest possible cost In this perspective, this study proposes a risk assessment methodology for the evaluation of preventive maintenance planning based on a reliability model for marine engine systems, which allows the use of flexible intervals between interventions. maintenance, and ultimately, the systematization of preventive maintenance methods for marine engines using recovered fossil oils. This model of preventive maintenance of marine engines using recovered fossil oils was generated to condition the monitoring of the crankshaft position, deviations and symptoms of the condition of the tribological joint and diagnostic models based on defined and measurable deviations and vibration symptoms. The generated model allowed to monitor the condition of the big end bearing, the crosshead bearing, the big end bearing and the main bearings. specific for sets of ship machines with engine with crankshaft-piston mechanism, symptoms and deviations, and relationships between them. Keywords: preventive maintenance, diesel, marine engines, fossil oils.Un sistema de mantenimiento preventivo está diseñado para cumplir con los requisitos de calidad, eficiencia y seguridad. Estos requisitos se han formalizado básicamente. En particular, se refiere a los requisitos de seguridad estipulados por los convenios internacionales. El incumplimiento de estos requisitos es principalmente formal y dejará al sistema en un estado de incompetencia (Bielawski, Piotr, 2021) por lo que el objetivo específico del proceso de mantenimiento preventivo radica en aprovechar el conocimiento de averías y accidentes para conseguir la máxima seguridad posible con el menor coste posible En esta perspectiva, este estudio propone una metodología de evaluación de riesgos para la evaluación de la planificación del mantenimiento preventivo basada en un modelo de confiabilidad para los sistemas de motores marinos, que permite el uso de intervalos flexibles entre las intervenciones de mantenimiento, y en última instancia, la sistematización de métodos de mantenimiento preventivo de motores marinos mediante aceites fósiles recuperados.  Este modelo de mantenimiento preventivo de motores marinos mediante aceites fósiles recuperados se generó para condicionar el monitoreo de la posición del cigüeñal, desviaciones y síntomas de la condición de la unión tribológica y modelos de diagnóstico basados en desviaciones definidas y medibles y síntomas de vibración. El modelo generado permitió monitorear el estado del cojinete de la cabeza de la biela, el cojinete de la cruceta, el cojinete de la cabeza de la biela y los cojinetes principales específico para conjuntos de máquinas de embarcaciones con motor con mecanismo de cigüeñal-pistón, síntomas y desviaciones, y relaciones entre ellos. Palabras clave: mantenimiento preventivo, diésel, motores marinos, aceites fósiles. Abstract A preventive maintenance system is designed to meet quality, efficiency, and safety requirements. These requirements have basically been formalized. In particular, it refers to the security requirements stipulated by international conventions. Failure to comply with these requirements is mainly formal and will leave the system in a state of incompetence (Bielawski, Piotr, 2021), so the specific objective of the preventive maintenance process is to take advantage of knowledge of breakdowns and accidents to achieve the maximum possible safety. with the lowest possible cost In this perspective, this study proposes a risk assessment methodology for the evaluation of preventive maintenance planning based on a reliability model for marine engine systems, which allows the use of flexible intervals between interventions. maintenance, and ultimately, the systematization of preventive maintenance methods for marine engines using recovered fossil oils. This model of preventive maintenance of marine engines using recovered fossil oils was generated to condition the monitoring of the crankshaft position, deviations and symptoms of the condition of the tribological joint and diagnostic models based on defined and measurable deviations and vibration symptoms. The generated model allowed to monitor the condition of the big end bearing, the crosshead bearing, the big end bearing and the main bearings. specific for sets of ship machines with engine with crankshaft-piston mechanism, symptoms and deviations, and relationships between them. Keywords: preventive maintenance, diesel, marine engines, fossil oils. Información del manuscrito:Fecha de recepción: 21 de enero de 2022.Fecha de aceptación: 14 de marzo de 2022.Fecha de publicación: 24 de marzo de 2022

A Digital Triplet for Utilizing Offline Environments to Train Condition Monitoring Systems for Rolling Element Bearings

Manufacturing competitiveness is related to making a quality product while incurring the lowest costs. Unexpected downtime caused by equipment failure negatively impacts manufacturing competitiveness due to the ensuing defects and delays caused by the downtime. Manufacturers have adopted condition monitoring (CM) techniques to reduce unexpected downtime to augment maintenance strategies. The CM adoption has transitioned maintenance from Breakdown Maintenance (BM) to Condition-Based Maintenance (CbM) to anticipate impending failures and provide maintenance actions before equipment failure. CbM is the umbrella term for maintenance strategies that use condition monitoring techniques such as Preventive Maintenance (PM) and Predictive Maintenance (PdM). Preventive Maintenance involves providing periodic checks based on either time or sensory input. Predictive Maintenance utilizes continuous or periodic sensory inputs to determine the machine health state to predict the equipment failure. The overall goal of the work is to improve bearing diagnostic and prognostic predictions for equipment health by utilizing surrogate systems to generate failure data that represents production equipment failure, thereby providing training data for condition monitoring solutions without waiting for real world failure data. This research seeks to address the challenges of obtaining failure data for CM systems by incorporating a third system into monitoring strategies to create a Digital Triplet (DTr) for condition monitoring to increase the amount of possible data for condition monitoring. Bearings are a critical component in rotational manufacturing systems with wide application to other industries outside of manufacturing, such as energy and defense. The reinvented DTr system considers three components: the physical, surrogate, and digital systems. The physical system represents the real-world application in production that cannot fail. The surrogate system represents a physical component in a test system in an offline environment where data is generated to fill in gaps from data unavailable in the real-world system. The digital system is the CM system, which provides maintenance recommendations based on the ingested data from the real world and surrogate systems. In pursuing the research goal, a comprehensive bearing dataset detailing these four failure modes over different collection operating parameters was created. Subsequently, the collections occurred under different operating conditions, such as speed-varying, load-varying, and steadystate. Different frequency and time measures were used to analyze and identify differentiating criteria between the different failure classes over the differing operating conditions. These empirical observations were recreated using simulations to filter out potential outliers. The outputs of the physical model were combined with knowledge from the empirical observations to create ”spectral deltas” to augment existing bearing data and create new failure data that resemble similar frequency criteria to the original data. The primary verification occurred on a laboratory-bearing test stand. A conjecture is provided on how to scale to a larger system by analyzing a larger system from a local manufacturer. From the subsequent analysis of machine learning diagnosis and prognosis models, the original and augmented bearing data can complement each other during model training. The subsequent data substitution verifies that bearing data collected under different operating conditions and sizes can be substituted between different systems. Ostensibly, the full formulation of the digital triplet system is that bearing data generated at a smaller size can be scaled to train predictive failure models for larger bearing sizes. Future work should consider implementing this method for other systems outside of bearings, such as gears, non-rotational equipment, such as pumps, or even larger complex systems, such as computer numerically controlled machine tools or car engines. In addition, the method and process should not be restricted to only mechanical systems and could be applied to electrical systems, such as batteries. Furthermore, an investigation should consider further data-driven approximations to specific bearing characteristics related to the stiffness and damping parameters needed in modeling. A final consideration is for further investigation into the scalability quantities within the data and how to track these changes through different system levels

Advanced Techniques for Assets Maintenance Management

16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018 Bergamo, Italy, 11–13 June 2018. Edited by Marco Macchi, László Monostori, Roberto PintoThe aim of this paper is to remark the importance of new and advanced techniques supporting decision making in different business processes for maintenance and assets management, as well as the basic need of adopting a certain management framework with a clear processes map and the corresponding IT supporting systems. Framework processes and systems will be the key fundamental enablers for success and for continuous improvement. The suggested framework will help to define and improve business policies and work procedures for the assets operation and maintenance along their life cycle. The following sections present some achievements on this focus, proposing finally possible future lines for a research agenda within this field of assets management