Joint optimization of replacement and spare ordering for critical rotary component based on condition signal to date

Chen, Xiaohui; Xu, Dawei; Xiao, Lei

doi:10.17531/ein.2017.1.11

Cited by 15 publications

(6 citation statements)

References 26 publications

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“…Chen et al (Chen, Xu, & Xiao, 2016) introduced a method to predict RUL of an individual component when the degradation cannot be reflected by historical data in totality. The estimated RUL is formulated into failure probability function, upon which maintenance interventions and spare part ordering are optimized jointly.…”

Section: Logistics Jointly With Maintenancementioning

confidence: 99%

Review of Post-Prognostics Decision-Making in Prognostics and Health Management

Bougacha

Varnier

Zerhouni

2021

IJPHM

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Mainly, the prognostics and health management (PHM) process is based on three processes: the data acquisition and health assessment process in which sensors signals are acquired and processed, the diagnostic and prognostic process in which the source of failure is detected and the remaining useful life (RUL) is predicted and finally the decision- making process that refers to the term management in prognostics and health management. This paper reviews in the literature about the different aspects of decision-making in the context of PHM. The selected papers are subject to con- tent assessment and grouped according to the decision type. Additionally, this paper presents a synthesis of the previous works that helps identify new trends and deficiencies in the decision-making process. The synthesis can guide efforts for future work.

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Section: Logistics Jointly With Maintenancementioning

confidence: 99%

Review of Post-Prognostics Decision-Making in Prognostics and Health Management

Bougacha

Varnier

Zerhouni

2021

IJPHM

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“…Maintenance policy: In recent years, several studies about maintenance policies and supply chain for smart and intelligent systems have been conducted. Cheng [3] used a Neural Network to predict the remaining useful life (RUL) of a multi-component system, in which economic dependence exists among the components. The proposed method included two-level failure probability thresholds, based on which Condition-based Maintenance (CBM) was applied.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analyzing Supporting Systems for Smart Manufacturing Systems with Stochastic, Technical and Economic Dependences

Farsi

Zio

2020

IJE

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Smart manufacturing systems are triggerring the next industrial revolution. They are intended to be collaborative manufacturing systems that respond in real time to meet the system's changing demands and conditions. Different types of dependencies among system components are introduced to enable this and to improve system performance, including structural, stochastic, technical and economic dependences. Supporting systems are also introduced to this aim, through specified interfaces. In this paper, the role of maintenance policy, spare part inventory and buffer size as supporting systems of smart systems is considered. Load-sharing dependence, adaptive control with feedback and economic dependence are specifically considered, and their effect is studied via Monte Carlo simulation. Results show that smart systems with properly designed supporting systems have undoubtedly increased system complexity and dependencies, but can indeed increase availability and production volume, and system efficiency overall, with total cost reduced.

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“…High machine availability can only be achieved by ensuring the effective implementation of maintenance operations. One of the most important factors affecting the effectiveness of maintenance operations is the availability of spare parts [5]. That is why manufacturing companies put great emphasis on the determination of suitable inventory control policies for spare parts.…”

Section: Introductionmentioning

confidence: 99%

A spare parts criticality evaluation method based on fuzzy AHP and Taguchi loss functions

Ilgın¹

2019

Eksploatacja i Niezawodność – Maintenance and Reliability

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Effective and efficient operation of a manufacturing system highly depends on the timely and correct implementation of maintenance operations. One of the most important factors affecting the successful implementation of maintenance operations is the determination of suitable inventory control policies for maintenance spare parts. Effective spare parts inventory management requires the criticality evaluation of spare parts. In this study, a novel spare parts criticality evaluation approach is proposed. First, the evaluation criteria are determined based on literature review and expert opinion and Fuzzy Analytical Hierarchy Process (AHP) is used to determine the criteria weights. Next, Taguchi loss functions and simulation modeling are employed for the calculation of loss values for the spare parts. Finally, a criticality ranking of the spare parts is obtained based on the weighted loss values which are calculated using criteria weights and loss values. The applicability of the proposed approach was tested by applying it to a spare part criticality evaluation problem faced by a manufacturing company.

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Joint optimization of replacement and spare ordering for critical rotary component based on condition signal to date

Abstract: optimization of replacement and spare ordering for critical rotary component based on condition signal to date. eksploatacja i niezawodnosc - Maintenance and Reliability 2017; 19 (1): 76-85, http://dx.doi.org/10.17531/ein.2017

Cited by 15 publications

References 26 publications

Review of Post-Prognostics Decision-Making in Prognostics and Health Management

Review of Post-Prognostics Decision-Making in Prognostics and Health Management

Modeling and Analyzing Supporting Systems for Smart Manufacturing Systems with Stochastic, Technical and Economic Dependences

A spare parts criticality evaluation method based on fuzzy AHP and Taguchi loss functions

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