Real-Time Maintenance Policy in Manufacturing Systems With Intermediate Buffers

2015 ◽  
Author(s):  
Xi Gu ◽  
Xiaoning Jin ◽  
Jun Ni
Keyword(s):  
Real Time ◽  
Manufacturing Systems ◽  
Maintenance Policy ◽  
Discrete Time Markov Chain ◽  
Markov Decision ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Time Buffer ◽  
Buffer Capacities ◽  
One Machine

Real-time maintenance decision making in large manufacturing system is complex because it requires the integration of different information, including the degradation states of machines, as well as inventories in the intermediate buffers. In this paper, by using a discrete time Markov chain (DTMC) model, we consider the real-time maintenance policies in manufacturing systems consisting of multiple machines and intermediate buffers. The optimal policy is investigated by using a Markov Decision Process (MDP) approach. This policy is compared with a baseline policy, where the maintenance decision on one machine only depends on its degradation state. The result shows how the structures of the policies are affected by the buffer capacities and real-time buffer levels.

Maintenance Decision Model for a Two-Machine Production Line With Multistage Degradation Machines

2018 ◽  
Author(s):  
Yunyi Kang ◽  
Feng Ju
Keyword(s):  
Decision Model ◽  
Production Systems ◽  
Maintenance Policy ◽  
Multi Stage ◽  
Markov Decision ◽  
Optimal Maintenance ◽  
Markov Decision Model ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Optimal Maintenance Policy

In this work, we develop preventative maintenance policies on two-machine-and-one-buffer production systems with machines subject to multi-stage degradation. Condition-based maintenance policies are generated for both machines, with consideration on both the machine degradation stages and the buffer level. Moreover, the policies are flexible, allowing a machine to be recovered to any better operating state, while merely recovering to the best operating state is possible in many previous work. A Markov decision model is formulated to find the optimal maintenance policy and computational experiments show that the policies improve the performance of a system in finite production runs.

Estimation of Real-Time Active Maintenance Opportunity Windows in Manufacturing Systems With Bernoulli Machines

2016 ◽  
Author(s):  
Xi Gu ◽  
Xiaoning Jin ◽  
Weihong Guo
Keyword(s):  
Real Time ◽  
Manufacturing Systems ◽  
Time Windows ◽  
The Real ◽  
Time Dynamics ◽  
Active Maintenance ◽  
Manufacturing Enterprises ◽  
Maintenance Policies ◽  
Demand Changes ◽  
One Machine

Effective maintenance operations are essential to improve the competitiveness of manufacturing enterprises. However, the existing maintenance policies usually ignore the real-time dynamics of the system and cannot respond promptly to the demand changes in the market. This paper investigates the hidden opportunities that one machine can be stopped for maintenance during production time, while the throughput requirement in a specific horizon can still be satisfied. We define these time windows as active maintenance opportunity windows (AMOWs), and predict them based on the real-time operational data in manufacturing systems with different configurations and Bernoulli machines.

Big Data Analytics for Predictive Maintenance Strategies

2017 ◽  
pp. 50-74 ◽  
Author(s):  
C. K. M. Lee ◽  
Yi Cao ◽  
Kam Hung Ng
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Health Condition ◽  
Predictive Maintenance ◽  
Maintenance Policy ◽  
Failure Patterns ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Faulty Component

Maintenance aims to reduce and eliminate the number of failures occurred during production as any breakdown of machine or equipment may lead to disruption for the supply chain. Maintenance policy is set to provide the guidance for selecting the most cost-effective maintenance approach and system to achieve operational safety. For example, predictive maintenance is most recommended for crucial components whose failure will cause severe function loss and safety risk. Recent utilization of big data and related techniques in predictive maintenance greatly improves the transparency for system health condition and boosts the speed and accuracy in the maintenance decision making. In this chapter, a Maintenance Policies Management framework under Big Data Platform is designed and the process of maintenance decision support system is simulated for a sensor-monitored semiconductor manufacturing plant. Artificial Intelligence is applied to classify the likely failure patterns and estimate the machine condition for the faulty component.

scholarly journals Energy Consumption-Based Maintenance Policy Optimization

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5674
Author(s):  
Ágota Bányai
Keyword(s):  
Energy Consumption ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Functional Model ◽  
Optimization Method ◽  
Maintenance Policy ◽  
Failure Data ◽  
Maintenance Policies ◽  
Value Determination ◽  
Policy Optimization

The optimal predictive, preventive, corrective and opportunistic maintenance policies play an important role in the success of sustainable maintenance operations. This study discusses a new energy efficiency-related maintenance policy optimization method, which is based on failure data and status information from both the physical system and the digital twin-based discrete event simulation. The study presents the functional model, the mathematical model and the solution algorithm. The maintenance optimization method proposed in this paper is made up of four main phases: computation of energy consumption based on the levelized cost of energy, computation of GHG emission, computation of value determination equations and application of the Howard’s policy iteration techniques. The approach was tested with a scenario analysis, where different electricity generation sources were taken into consideration. The computational results validated the optimization method and show that optimized maintenance policies can lead to an average of 38% cost reduction regarding energy consumption related costs. Practical implications of the proposed model and method regard the possibility of finding optimal maintenance policies that can affect the energy consumption and emissions from the operation and maintenance of manufacturing systems.

Big Data Analytics for Predictive Maintenance Strategies

Web Services ◽  
2019 ◽  
pp. 1646-1665
Author(s):  
C. K. M. Lee ◽  
Yi Cao ◽  
Kam Hung Ng
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Health Condition ◽  
Predictive Maintenance ◽  
Maintenance Policy ◽  
Failure Patterns ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Faulty Component

Maintenance aims to reduce and eliminate the number of failures occurred during production as any breakdown of machine or equipment may lead to disruption for the supply chain. Maintenance policy is set to provide the guidance for selecting the most cost-effective maintenance approach and system to achieve operational safety. For example, predictive maintenance is most recommended for crucial components whose failure will cause severe function loss and safety risk. Recent utilization of big data and related techniques in predictive maintenance greatly improves the transparency for system health condition and boosts the speed and accuracy in the maintenance decision making. In this chapter, a Maintenance Policies Management framework under Big Data Platform is designed and the process of maintenance decision support system is simulated for a sensor-monitored semiconductor manufacturing plant. Artificial Intelligence is applied to classify the likely failure patterns and estimate the machine condition for the faulty component.

Joint Production and Maintenance Decision-Making in Mixed-Model Assembly Systems

2017 ◽  
Author(s):  
Xi Gu ◽  
Weihong Guo
Keyword(s):  
Decision Making ◽  
Mixed Model ◽  
Joint Production ◽  
Assembly Systems ◽  
Markov Decision ◽  
Operational Decision Making ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Mixed Model Assembly ◽  
Mass Personalization

Mixed-model assembly systems (MMASs) have been well recognized for their ability to handle product variants, and thus are particularly useful to meet the requirement brought by mass personalization. However, operational decision-making in MMASs is challenging due to the system complexity. Production selection and maintenance are two important operational decisions. In this paper, we investigate the joint production and maintenance policies in MMASs that consist of both common and variant operation stations. Markov Decision Process (MDP) is used to formulate the problem and numerical examples are presented to illustrate the structure of the policy in an MMAS that produces two types of product variants.

scholarly journals A Real-Time Maintenance Policy for Multi-Stage Manufacturing Systems Considering Imperfect Maintenance Effects

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 62174-62183 ◽  
Author(s):  
Jing Huang ◽  
Qing Chang ◽  
Jing Zou ◽  
Jorge Arinez
Keyword(s):  
Real Time ◽  
Manufacturing Systems ◽  
Imperfect Maintenance ◽  
Maintenance Policy ◽  
Multi Stage

Condition-Based Maintenance Decision-Making for Multiple Machine Systems

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
Saumil Ambani ◽  
Lin Li ◽  
Jun Ni
Keyword(s):  
Decision Making ◽  
Single Machine ◽  
Resource Constraints ◽  
Quantitative Description ◽  
Condition Based Maintenance ◽  
Maintenance Policy ◽  
Machine System ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Optimal Maintenance Policy

Maintenance decision-making has emerged as an important area of industrial research. Over the past two decades, maintenance policies have evolved from simple reactive maintenance to complex versions of condition-based maintenance (CBM). A quantitative description of a machine’s health, as found in CBM, is essential to plan maintenance effectively as it helps avoid excessive or insufficient maintenance. In spite of several advancements in the degradation monitoring techniques, most CBM decision-making methods still focus on a single machine system. Maintenance analysis of a single machine provides good insights, but lacks practical applications. In this paper, we develop a continuous time Markov chain degradation model and a cost model to quantify the effects of maintenance on a multiple machine system. An optimal maintenance policy for a multiple machine system in the absence of resource constraints is obtained. In the presence of resource constraints, two prioritization methods are proposed to obtain effective maintenance policies for a multiple machine system. A case study focusing on a section of an automotive assembly line is used to illustrate the effectiveness of the proposed method.

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