Major Preventive Maintenance Research Articles

Optimization of annual planned rail maintenance

AbstractResearch on preventative rail maintenance to date majors on small or artificial problem instances, not applicable to real‐world use cases. This article tackles large, real‐world rail maintenance scheduling problems. Maintenance costs and availability of the infrastructure need to be optimized, while adhering to a set of complex constraints. We develop and compare three generic approaches: an evolution strategy, a greedy metaheuristic, and a hybrid of the two. As a case study, we schedule major preventive maintenance of a full year in the complete rail infrastructure of the Netherlands, one of the busiest rail networks of Europe. Empirical results on two real‐world datasets show the hybrid approach delivers high‐quality schedules.

Panel Data Models for Pavement Friction of Major Preventive Maintenance Treatments

Panel Data Models for Pavement Friction of Major Preventive Maintenance Treatments

An aggregate production planning mathematical model, under a peak-demand electrical control policy

The impact of energy costs on the production planning decisions of most manufacturing companies is certainly crucial. For electrical consumption, there is a link between production activities and the regions energy policies, for example where electricity price variations are a tactical problem to be solved via equipment sequencing to address the known electricity costs. The goal of this study is to apply an aggregate production planning mathematical model, under a peak-demand electrical control policy, for an energy intensive manufacturer of grinding media, in Chile. The objective function is to maximise the profit of a company in a horizon time of T periods, where a penalty is incurred when production lines are used at peak hours (per electric market regulations/contracts). Furthermore, the model determines the optimum period for a major preventive maintenance for each of its process lines. The case studied is a plant which produces steel balls that are used for mineral grinding; the plant manufactures ten types steel balls from round steel bars (different diameters of balls/bar), a process with high energy demand (induction bar heating). The proposed model was implemented in the Lingo software, allowing for a consistent aggregate production planning to maximising the company's profits.

Performance Analysis of a Rod Breakdown System

The paper presents performance analysis of a rod breakdown system consisting of two machines operating in parallel. Ten years maintenance data of the system depicts three maintenance practices: repair, major preventive maintenance and minor preventive maintenance. Machines are repaired on normal failures, whereas major/minor preventive maintenance is carried out at scheduled basis. The machines are queued for major preventive maintenance. An operative machine requires a cooling period of half hour before undergoing major preventive maintenance. Repair is prioritized over major preventive maintenance. The system is analysed using semi-Markov processes and regenerative point techniques. Performance indices namely mean time to system failure, system availability, no. of system repairs and busy period of the repairman are obtained. Simulated results are presented to demonstrate the effect of varying failure/repair rates on the system performance.

An adaptive degradation‐based maintenance model taking into account both imperfect adjustments and AGAN replacements

AbstractThis paper presents an adaptive maintenance model for equipment that can be adjusted (minor preventive maintenance, imperfect state) or replaced (major preventive maintenance, as good as new) at specific scheduled times based on degradation measurements. An initial reliability law that uses a degradation‐based model is built from the collection of hitting times of a failure threshold. Inspections are performed to update the reliability, the remaining useful life, and the optimum time for preventive maintenance. The case of both as good as new replacements and imperfect adjustments is considered. The proposed maintenance model is based on the optimization of the long‐term expected cost per unit of time. The model is then tested on a numerical case study to assess its effectiveness. This results in an improvement for the occurrences of maintenance tasks that minimizes the mean cost per unit of time as well as an optimized number of adjustments that can be considered before replacing an item. The practical application is a decision aid support to answer the 2 following questions: Should we intervene now or wait for the next inspection? For each intervention, should we adjust or replace the item of equipment? The originality is the presence of 2 criteria that help the maintainer to decide to postpone or not the preventive replacement time depending on the measured degradation and to decide whether the item should be adjusted or replaced.

A recursive framework for time-dependent characteristics of tested and maintained standby units with arbitrary distributions for failures and repairs

The time-dependent unavailability and the failure and repair intensities of periodically tested aging standby system components are solved with recursive equations under three categories of testing and repair policies. In these policies, tests or repairs or both can be minimal or perfect renewals. Arbitrary distributions are allowed to times to failure as well as to repair and renewal durations. Major preventive maintenance is done periodically or at random times, e.g. when a true demand occurs. In the third option process renewal is done if a true demand occurs or when a certain mission time has expired since the previous maintenance, whichever occurs first. A practical feature is that even if a repair can renew the unit, it does not generally renew the alternating process. The formalism updates and extends earlier results by using a special backward-renewal equation method, by allowing scheduled tests not limited to equal intervals and accepting arbitrary distributions and multiple failure types and causes, including failures caused by tests, human errors and true demands. Explicit solutions are produced to integral equations associated with an age-renewal maintenance policy.

Vacuum science considerations for rapid reactor recovery with extremely low oxygen in low temperature low pressure chemical vapor deposition of Si1−xGex and Si1−x−yGexCy films

The films for this study were grown in the AMAT Centura 5200 low pressure chemical vapor deposition (LPCVD) reactor. Silicon germanium (SixGe1−x) and silicon germanium carbon (Si1−x−yGexCy) have widely been accepted as the materials of choice for a variety of uses in semiconductor devices. Most manufacturers utilize LPCVD for the low temperature growth of these films. Due to the reaction kinetics, oxygen incorporation in SiGe films may become substantial, especially following a major preventive maintenance (PM). Depending on the technology, elevated oxygen may be unacceptable due to process and device performance considerations. These considerations range from oxygen reaction with dopant source gases, thus minimizing dopant incorporation and activation, to minority carrier lifetime reduction in the base of a SiGe heterojunction bipolar transistor due to oxygen related defects. To achieve low oxygen processes the engineer must have a clear picture of the mechanisms and the fundamental vacuum science as it relates to LPCVD processing. Outgassing and the all too familiar chamber leak rate are discussed as they relate to sorption and sorbtion effects and recommendations are made to facilitate very rapid PM recoveries with sustained in-film oxygen levels as good as attained with ultrahigh vacuum CVD. Secondary ion mass spectrometry profiles are presented, which demonstrate that a sustained sub-1017at.∕cc oxygen level in low temperature LPCVD SiGe processing is possible when these types of gas loads are addressed.

An Optimization Methodology for Condition Based Minimal and Major Preventive Maintenance

This paper proposes a condition based preventive maintenance (CBPM) policy for Markov deteriorating systems. The proposed model considers deterioration and random failures with minimal and major preventive maintenance (PM). Minimal repairs are carried out after every random failure, and the device is getting replaced after the occurrence of the deterioration failure. The system undergoes random inspections to assess the condition; mean time between inspections is exponentially distributed. Based upon the observed condition of the device, do nothing, minimal PM, and major PM may take place. Minimal PM makes the system one deterioration stage younger, while Major PM makes the system τ deterioration stages (τ > 1) younger. The proposed models consider increasing intensity for the random failures. An exact recursive algorithm computes the steady-state probabilities of the system. Optimal solutions of the model are derived based on two criteria, viz., (a) availability maximization, and (b) total cost minimization.

MAINTENANCE OPTIMIZATION OF EQUIPMENT BY LINEAR PROGRAMMING

Optimal levels of preventive maintenance performed on any system ensures cost-effective and reliable operation of the system. In this paper a component with deterioration and random failure is modeled using Markov processes while incorporating the concept of minor and major preventive maintenance. The optimal mean times to preventive maintenance (both minor and major) of the component is determined by maximizing its availability with respect to mean time to preventive maintenance. Mathematical optimization programs Maple 7 and Lingo 7 are used to find the optimal solution, which is illustrated using a numerical example. Further, an optimal maintenance policy is obtained using Markov Decision Processes (MDPs). Linear Programming (LP) is utilized to implement the MDP problem.

Reliability data analysis and modelling of offshore oil platform plant

This paper reports on the reliability data analysis and modelling of offshore oil platform plant. We first analysed the effectiveness of preventive maintenance to the plant concerned, and then identified the key sub‐units and major preventive maintenance activities to be modelled. A modelling technique called the delay time is then used to determine the optimal periodic inspection intervals. The analysis showed that preventive maintenance is generally effective, but a slight decrease of the periodic inspection intervals is favourable.