Optimizing Preventive Maintenance Schedule for a Distillery Plant

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
R.K. Garg ◽  
Ankur Bahl ◽  
Anish Sachdeava
2004 ◽  
Vol 6 (2) ◽  
pp. 133-156 ◽  
Author(s):  
V. K. Kanakoudis

Must the water networks be fail-proof or must they remain safe during a failure? What must water system managers try to achieve? The present paper introduces a methodology for the hierarchical analysis (in time and space) of the preventive maintenance policy of water supply networks, using water supply system performance indices. This is being accomplished through a technical–economic analysis that takes into account all kinds of costs referring to the repair or replacement of trouble-causing parts of the water supply network. The optimal preventive maintenance schedule suggested by the methodology is compared with the empirically based maintenance policy applied to the Athens water supply system.


Author(s):  
Dengji Zhou ◽  
Huisheng Zhang ◽  
Yi-Guang Li ◽  
Shilie Weng

The availability requirement of natural gas compressors is high. Thus, current maintenance architecture, combined periodical maintenance and simple condition based maintenance, should be improved. In this paper, a new maintenance method, dynamic reliability-centered maintenance (DRCM), is proposed for equipment management. It aims at expanding the application of reliability-centered maintenance (RCM) in maintenance schedule making to preventive maintenance decision-making online and seems suitable for maintenance of natural gas compressor stations. A decision diagram and a maintenance model are developed for DRCM. Then, three application cases of DRCM for actual natural gas compressor stations are shown to validate this new method.


Author(s):  
Markus Bohlin ◽  
Mathias Wa¨rja

High levels of availability and reliability are essential in many industries where production is subject to high costs due to downtime. Examples where gas turbines are used include the mechanical drive in natural gas pipelines and power generation on oil platforms, where it is common to use redundant gas turbines to mitigate the effects of service outage. In this paper, component-level maintenance of parallel multi-unit systems is considered, allowing production at a reduced level when some of the units are not operational. Units are themselves assumed to be composed out of components in a serial configuration; maintenance of one component implies shutdown of the unit. Parallel installations allow maintenance to be performed on one or a few gas turbines without taking down the entire installation. This allows maintenance to be optimized even further than in a serial system. However, the maintenance optimization process is made more complicated, since there now exist both positive and negative grouping effects. The positive grouping effects come from shared setup activities and costs, and the negative effects come from resource limitations, in this case the limited number of gas turbines which can be maintained at the same time. In the approach presented in this paper, each component has its individual preventive maintenance schedule, which is updated at inspections, changes in production and when indicated using remote condition monitoring. A minimal repair model for noncritical routine inspections and service tasks is assumed, which does not affect component state. In addition, previously developed procedures for estimating and measuring residual component lifetime for individual components during operation are used. The procedures are based on a Retirement For Cause (RFC) approach where components are not replaced until a potential failure has been detected. To maximize revenues for an operator, the available information is evaluated using software where scenario analysis and optimization is performed. To show the possible economic effects, gas turbine operation data is used together with maintenance and operator requirements as input for optimization of a production line consisting of a natural-gas compressor station having three SGT-600 gas turbines. Savings can be substantial compared to a traditional preventive maintenance plan.


2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Amal Witonohadi ◽  
Tiena Gustina Amran ◽  
Niken Herawati

<p>PT. BAI produce polymer filter system components, such as the spin pack, candle filters, leaf<br />discs, gaskets and so forth. Problems that occur in the form of decreased reliability of machine<br />downtime resulting in large engine stalled, causing the production process, the company should have<br />a preventive maintenance schedule in accordance with the conditions of machines on the production<br />floor to reduce machine downtime by using modularity design method approach to reduce<br />maintenance costs. Level of maintenance reliability measured using Overall Equipment Effectiveness<br />(OEE), whereas treatment schedule used to obtain parameter Mean Time to Failure (MTTF) and<br />Mean Time to Repair (MTTR). Maintenance cost calculator is then performed with corrective<br />maintenance, preventive maintenance and preventive maintenance with design modularity. By using<br />modularity design companies can combine several components into a module to do the replacement<br />components simultaneously causing maintenance costs to be as much as 29.1% less than the company<br />doing the corrective maintenance activities.</p>


Author(s):  
Chun Su ◽  
Longfei Cheng

By considering learning effects during the process of maintenance, this article investigates an availability-based warranty policy from the manufacturer’s perspective. Imperfect preventive maintenance activities are implemented to reduce the number of equipment failures so as to meet the product’s availability requirement and minimize warranty servicing cost during the warranty period. Hybrid hazard rate model is used to describe imperfect preventive maintenance actions; maintenance plan and warranty servicing cost are optimized by considering learning effects. Preventive maintenance schedule within a warranty period is optimized cycle by cycle, and the warranty cost rate is minimized under the requirement of availability within each cycle. Numerical example of a wind turbine gearbox is provided to verify the effectiveness of the proposed availability-based warranty policy. Sensitivity analysis shows that by considering learning effects on maintenance, the gearbox can maintain a higher availability level with a lower warranty cost than the case without consideration of learning effects.


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