Dynamic management of periodicity between measurements in predictive maintenance

Hijes, Félix C. Gómez de León e; Robles, J. Sánchez; García, F.M. Martínez; García, Mariano Alarcón

doi:10.1016/j.measurement.2023.112721

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…Addressing the problem of monitoring intervals in predictive maintenance, F.C. Gómez de León Hijes et al [10] show a methodology to determine and manage the Time Interval Between Measurements (TIBeM), dynamically adapting to each machine and each situation. To be more realistic, Yu-Chung Tsao et al [11] proposed a nonlinear optimization solution procedure to determine the optimal tariff, generation capacity, investment plan, predictive maintenance budget, and insurance level while maximizing the company's profit.…”

Section: Literature Reviewmentioning

confidence: 99%

A Predictive Maintenance Strategy for Multi-Component Systems Based on Components’ Remaining Useful Life Prediction

Lv,

Zheng,

Yuan

et al. 2023

Mathematics

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Industries increasingly rely on intricate multi-component systems, necessitating efficient maintenance strategies to ensure system reliability and minimize downtime. Predictive maintenance, an emerging approach that utilizes data-driven techniques to forecast and prevent failures, holds significant potential in this regard. This paper presents a predictive maintenance strategy tailored specifically for multi-component systems. In order to accurately anticipate the remaining useful life (RUL) of components, we develop a method that combines data and model fusion based on a particle filtering approach and a degradation distribution model. By integrating degradation data with models, our method outperforms traditional model-based approaches in terms of prediction accuracy. Subsequently, we apply an optimized maintenance model to individual components based on the trigger threshold for RUL. This model determines the most optimal maintenance actions for each component, with the aim of minimizing maintenance costs. Furthermore, we introduce an optimized maintenance strategy that incorporates opportunistic maintenance to further reduce the overall maintenance cost of the system. This strategy leverages predicted RUL information to schedule proactive maintenance actions at the opportune moment, resulting in a significant cost reduction compared to traditional periodic maintenance approaches. To validate the feasibility and effectiveness of our proposed strategy, we utilize experimental data from open-source lithium-ion batteries at the NASA PCoE Center. Through this empirical validation, we provide real-world evidence showcasing the applicability and performance of our strategy in a multi-component system.

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Section: Literature Reviewmentioning

confidence: 99%

A Predictive Maintenance Strategy for Multi-Component Systems Based on Components’ Remaining Useful Life Prediction

Lv,

Zheng,

Yuan

et al. 2023

Mathematics

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“…For this reason, it is necessary not only to measure the strength and resistance to degradation of the belt material, but also to monitor its wear and performance. In this way, it will be possible to detect emerging damage and prevent failures, introducing so-called 'predictive maintenance' [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Rubber Belt Material Performance and Damage

Ryba,

Bzinkowski,

Siemiątkowski

et al. 2024

Materials

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Conveyors play a very important role in modern manufacturing processes, and one of the most popular types is the belt conveyor. The main elements of a conveyor include a conveyor belt, roller sets, a supporting frame and a drive and control system. The reliable operation of the conveyor depends on the strength and durability of individual elements (especially the belt). Conveyor belts are made from various materials and have received a lot of attention in the scientific and research community. This article presents tests of the strength of the rubber belt material and its damage under load. The belt consists of two internal layers covered with a PVC coating on the outside, and the nominal belt thickness was 2 mm. In the experiment, various configurations of longitudinal and transverse damage were verified, and statistical methods were used to analyze the results. The obtained test results provided a new understanding of the propagation of conveyor belt damage and helped to improve the strain gauge-based monitoring system.

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Reliability analysis and preventive maintenance policy for consecutive k$k$‐out‐of‐n:F$n: F$ balanced system under failure criterion operating in shock environment

Dong,

Bai

2024

Quality & Reliability Eng

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This paper presents a consecutive ‐out‐of‐: balance system in a shock environment, where the state transition of component is induced by external shocks. If a predetermined threshold number of effective shocks are applied to component in a critical state, the component will fail. The state of the system is defined by the number of consecutive failing component groups in the system, which leads to system failure when a critical number of consecutive failing components is reached. To minimize maintenance costs, we propose a preventive maintenance method with an optimization model. We use finite Markov chain imbedding and Phase‐type distribution to calculate component group failure rates and associated probability functions in discrete and continuous time, respectively. The validity and accuracy of the model are confirmed by numerical examples and Monte Carlo simulations.

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Dynamic management of periodicity between measurements in predictive maintenance

Cited by 4 publications

References 35 publications

A Predictive Maintenance Strategy for Multi-Component Systems Based on Components’ Remaining Useful Life Prediction

A Predictive Maintenance Strategy for Multi-Component Systems Based on Components’ Remaining Useful Life Prediction

Monitoring of Rubber Belt Material Performance and Damage

Reliability analysis and preventive maintenance policy for consecutive k$k$‐out‐of‐n:F$n: F$ balanced system under failure criterion operating in shock environment

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