Optimal Maintenance Schedule Research Articles

Evaluation of Optimization Method for Inspection Scheduling of Power Distribution Facilities Using Maintenance Data Accumulated by Power Utility

Power distribution companies must establish an optimal maintenance schedule that balances the reliability and efficiency of a number of widely scattered facilities. Accordingly, we have developed an optimization method for inspection scheduling by which optimal inspection schedules are planned using 1) a method to predict the inspection priority for each facility and 2) a method to establish an inspection schedule that minimizes the inspection cost. In this study, for step 1, a practical method using both inspection history and facility attributes is proposed. The method consists of two approaches: a relation analysis and a defect prediction. In the relation analysis, relations between facility defect and other maintenance data are analyzed using a multiple correlation analysis method on the basis of chi-square testing. And then, facility defect is predicted using the data items that correlated with the facility defect obtained from the relation analysis. A modified quantification method type II introducing biased posterior probability is proposed for the prediction. Furthermore, proposed method is demonstrated by utilizing maintenance data accumulated by a certain Japanese power utility company. Evaluation results show that inspection cost can be reduced by 36–58% compared with the conventional twice-every-two-years inspection while retaining reliability.

Lease-Oriented Opportunistic Maintenance for Multi-Unit Leased Systems Under Product-Service Paradigm

With many industries increasingly relying on leased equipment and machinery, many original equipment manufacturers (OEMs) are turning to product-service packages where they deliver (typically lease) the physical assets. An integrated service contract will be offered for the asset. A classic example being Rolls Royce power-by-the-hour aircraft engines. Service contracts offered by original equipment manufacturers have predominantly focused on maintenance and upkeep activities for a single asset. Interestingly enough, manufacturing industries are beginning to adopt the product-service paradigm. However, one of the unique aspects in manufacturing settings is that the leased system is often not a single asset but instead a multi-unit system (e.g., an entire production line). In this paper, we develop a lease-oriented maintenance methodology for multi-unit leased systems under product-service paradigm. Unlike traditional maintenance models, we propose a leasing profit optimization (LPO) policy to adaptively compute optimal preventive maintenance (PM) schedules that capture the following dynamics: (1) the structural dependencies of the multi-unit system, (2) opportunistic maintenance of multiple system components, and (3) leasing profit savings (LPSs). We demonstrate the performance of our multi-unit maintenance policy by using a leased automotive manufacturing line and investigate its impact on leasing profits.

송전제약과 등가운전시간을 고려한 장기 예방정비계획 최적화에 관한 연구

송전제약과 등가운전시간을 고려한 장기 예방정비계획 최적화에 관한 연구

Modeling Particle Deposition Effects in Aircraft Engine Compressors

Airborne particles ingested in aircraft engines deposit on compressor blading and end walls. Aerodynamic surfaces degrade on a microscopic and macroscopic scale. Blade row, compressor, and engine performance deteriorate. Optimization of maintenance scheduling to mitigate these effects requires modeling of the deterioration process. This work provides a deterioration model on blade row level and the experimental validation of this model in a newly designed deposition test rig. When reviewing previously published work, a clear focus on deposition effects in industrial gas turbines becomes evident. The present work focuses on quantifying magnitudes and timescales of deposition effects in aircraft engines and the adaptation of the generalized Kern and Seaton deposition model for application in axial compressor blade rows. The test rig's cascade was designed to be representative of aircraft engine compressor blading. The cascade was exposed to an accelerated deposition process. Reproducible deposition patterns were identified. Results showed an asymptotic progression of blade row performance deterioration. A significant increase in total pressure loss and decrease in static pressure rise were measured. Application of the validated model using existing particle concentration and flight cycle data showed that more than 95% of the performance deterioration due to deposition occurs within the first 1000 flight cycles.

Two-Stage Multiobjective Optimization of Maintenance Scheduling for Pavements

AbstractPavements heavily influence the management costs in highway networks. Operating pavements represents a challenging task involving complex decisions on the application of maintenance actions...

Joint optimization of preventive maintenance and production scheduling for parallel machines system

Joint optimization of preventive maintenance and production scheduling for parallel machines system

Modeling an Optimal Track Maintenance Schedule in Consideration of Timing of Grinding and Tamping

<b>Modeling an Optimal Track Maintenance Schedule in Consideration of Timing of Grinding and Tamping</b>

Robust simultaneous estimation of stator and rotor resistances and rotor speed for predictive maintenance of sensorless induction motor drives

In this paper, a novel rotor and stator resistances estimator has been proposed for detecting faults while they are still developing or adequate warning of imminent failure and to give minimum downtime and optimum maintenance schedules to apply in sensorless IM drives. The stator resistance estimation is applied for monitoring stator windings temperature and monitoring the status of stator windings insulation of induction motor. On the other hand, broken bars and end-rings are common faults in three-phase induction motors. A parallel model reference adaptive system (MRAS) has been proposed in order for simultaneous estimation of the stator and rotor resistances and rotor speed. The aim of the proposed MRAS is to save the cost of implementation and the processes time. The proposed parallel MRAS depends on the point of slowly variation of the rotor resistance with respect to the rotor speed.

Robust simultaneous estimation of stator and rotor resistances and rotor speed for predictive maintenance of sensorless induction motor drives

In this paper, a novel rotor and stator resistances estimator has been proposed for detecting faults while they are still developing or adequate warning of imminent failure and to give minimum downtime and optimum maintenance schedules to apply in sensorless IM drives. The stator resistance estimation is applied for monitoring stator windings temperature and monitoring the status of stator windings insulation of induction motor. On the other hand, broken bars and end-rings are common faults in three-phase induction motors. A parallel model reference adaptive system (MRAS) has been proposed in order for simultaneous estimation of the stator and rotor resistances and rotor speed. The aim of the proposed MRAS is to save the cost of implementation and the processes time. The proposed parallel MRAS depends on the point of slowly variation of the rotor resistance with respect to the rotor speed.

Operation Process Rebuilding (OPR)-Oriented Maintenance Policy for Changeable System Structures

Considering the operation process rebuilding (OPR) of manufacturing/operation systems, we propose a dynamic interactive bilevel maintenance methodology to satisfy rapid market changes. Predictive maintenance (PdM) intervals at the machine level are dynamically scheduled by a multiobjective model for each diverse machine. A system-level opportunistic maintenance (OM) policy is proposed to facilitate PdM optimizations according to OPR activities. This novel OPR-OM policy utilizes a variable maintenance time window to construct optimal maintenance schedules that are suitable for changeable system structures. The results obtained by applying this methodology at Shanghai Port indicate that the proposed methodology can help a port transportation system to achieve rapid responses to OPR activities, which can significantly improve system efficiency and economy.

Sensor-Driven Condition-Based Generator Maintenance Scheduling—Part I: Maintenance Problem

Traditionally, generator maintenance scheduling has been implemented using highly conservative maintenance policies based on manufacturing specifications and engineering expertise on the type of generators. However, recent advances in sensor technology, signal processing, and embedded online diagnosis provide more unit-specific information on the degradation characteristics of the generators. In this two-paper study, we propose a new generation maintenance framework that integrates the sensor-driven predictive maintenance technologies with optimal maintenance scheduling models. In Part I, we propose a new mixed-integer optimization model for generation maintenance scheduling, which effectively incorporate the dynamic information of generators' health and maintenance cost provided by the Bayesian prognostic models. In Part II, we propose a framework that extends the maintenance model presented herein, and consider the effects of maintenance on network operation by coordinating generator maintenance schedules with the unit commitment and dispatch decisions. We introduce new reformulations and efficient algorithms for solving large-scale instances of the proposed maintenance scheduling model. Extensive computational studies using real-world degradation data demonstrates the effectiveness of the new framework.

Inertial Measurement Unit for On-Machine Diagnostics of Machine Tool Linear Axes

Machine tools degrade during operations, yet knowledge of degradation is elusive; accurately detecting degradation of machines’ components such as linear axes is typically a manual and time-consuming process. Thus, manufacturers need automated, efficient, and robust methods to diagnose the condition of their machine tool linear axes with minimal disruptions to production. Towards this end, a method was developed to use data from an inertial measurement unit (IMU) for identification of changes in the translational and angular errors due to axis degradation. The IMU-based method uses data from accelerometers and rate gyroscopes to identify changes in linear and angular errors due to axis degradation. A linear axis testbed, established for the purpose of verification and validation, revealed that the IMU-based method was capable of measuring geometric errors with acceptable test uncertainty ratios. Specifically, comparison of the IMU-based and laser-based results demonstrate that the IMU-based method is capable of detecting micrometer-level and microradian-level degradation of linear axes. Consequently, an IMU was created for application of the IMU-based method on a machine tool as a proof of concept for detection of linear axis error motions. If the data collection and analysis are integrated within a machine controller, the process may be streamlined for the optimization of maintenance activities and scheduling, supporting more intelligent decision-making by manufacturing personnel and the development of self-diagnosing smart machine tools.

An impulse control approach for optimal maintenance scheduling of a gas turbine

AbstractToday gas turbine outages are determined by one global life counter and deterministic life times. In the future the gas turbine will be equipped with multiple life counters for outage determination. Therefore we present an impulse control approach which takes the probabilistic nature of the damage mechanism into account to calculate an optimized maintenance schedule. (© 2016 Wiley‐VCH Verlag GmbH &amp; Co. KGaA, Weinheim)

A Computational Framework for Infrastructure Asset Maintenance Scheduling

This paper presents a computational framework for the optimization of maintenance activities for infrastructure assets, with particular emphasis being placed on road network assets. This framework incorporates degradation and maintenance models for infrastructure assets along with multi-objective optimization for searching optimal maintenance schedules. Given a schedule of maintenance actions, the future performance is estimated by means of a Monte Carlo simulation that enables to account for inherent uncertainties. The design variables of optimization are the types of maintenance actions and their timing over the planning horizon. The objectives are to minimize both the asset degradation and maintenance cost. This includes satisfaction of constraints representing performance demands. The proposed framework is general and can be applied to different types of infrastructure assets. The numerical results, obtained for a road bridge managed by a highway operating agency, demonstrate the validity and usefulness of the proposed framework.

Mathematical approach assisted differential evolution for generator maintenance scheduling

Maintenance scheduling of power generating units is very essential for the economical and reliable operation of a power system. The objective of Generator Maintenance Scheduling (GMS) problem is to find the exact time interval for preventive maintenance of power generating units in order to minimize the operating cost, maximize the system reliability and to extend the life time of the generating units. In this paper, the problem of scheduling of generating units for maintenance is formulated as a mixed integer optimization problem by considering minimizing the operating cost. Since generator maintenance scheduling is a mixed integer problem, differential evolution algorithm is suitably modified to handle the integer variables. The control variables in differential evolution algorithm are integers which denote the starting period of each generating unit for carrying out maintenance work. The lambda iteration method is used to determine the optimal generation schedule of committed generating units. This paper presents a mathematical approach assisted differential evolution (MADE) to solve maintenance scheduling problem in a power system. The performance of the proposed algorithm is validated by considering two test systems. The result obtained by the proposed MADE method is compared with mathematical approach assisted particle swarm optimization. The test results reveal the capability of the proposed MADE algorithm in finding optimal maintenance schedule for the GMS problem.

Optimization of Generator Maintenance Scheduling with Consideration on the Equivalent Operation Hours

In order for the optimal solution of generators&#x2019; annual maintenance scheduling to be applicable to the actual power system it is crucial to incorporate the constraints related to the equivalent operation hours (EOHs) in the optimization model. However, most of the existing researches on the optimal maintenance scheduling are based on the assumption that the maintenances are to be performed periodically regardless of the operation hours. It is mainly because the computation time to calculate EOHs increases exponentially as the number of generators becomes larger. In this paper an efficient algorithm based on demand grouping method is proposed to calculate the approximate EOHs in an acceptable computation time. The method to calculate the approximate EOHs is incorporated into the optimization model for the maintenance scheduling with consideration on the EOHs of generators. The proposed method is successfully applied to the actual Korean power system and shows significant improvement when compared to the result of the maintenance scheduling algorithm without consideration on EOHs.

A Review of Optimal maintenance scheduling of generators using PSO technique

A Review of Optimal maintenance scheduling of generators using PSO technique

Maintenance of petroleum process plant systems as a source of major accidents?

The prevention of hydrocarbon leaks is of great importance as they are the most critical precursor events that may lead to major accidents on petroleum facilities. Maintenance of process components on offshore and onshore petroleum facilities is therefore crucial in order to avoid major accidents, such as Piper Alpha and Texas City. Maintenance of Pressure Safety Valves (PSVs) is a significant activity because they are usually in quite high number and are recertified regularly. The accident chain that led to Piper Alpha started with maintenance of a PSV. Studies of leak circumstances have shown that, on Norwegian offshore installations, there is approximately one hydrocarbon leak per year resulting from recertification of PSVs, due to errors made during isolation and blinding or reinstatement. The preventive maintenance of PSVs thus becomes a source of a leak (which indicates risk) as well as a safety barrier element to reduce risk. The paper discusses corrective as well as preventive maintenance of static (not rotating) process equipment in relation to experience with hydrocarbon leaks and makes a case for optimization of preventive maintenance scheduling for static process equipment.

Rotor Resistance Estimation for Predictive Maintenance of Sensorless Induction Motor Drives

Broken bars and end-rings are common faults in three phase induction motors. These faults lead to unsymmetrical magnetic field, generate noise and vibration, increase temperature and the motor may be destroyed. In this paper, a rotor resistance estimator has been proposed for detecting faults while they are still developing or adequate warning of imminent failure and to give minimum downtime and optimum maintenance schedules. The rotor resistance has been estimated in order to apply in sensorless IM drives. A parallel model reference adaptive system (MRAS) has been proposed in order to simultaneous estimation of the rotor resistance and rotor speed. The aim of the proposed MRAS is to save the cost of implementation and the processes time. The proposed parallel MRAS depends on the point of slowly variation of the rotor resistance with respect to the rotor speed. The performance of the proposed scheme has been verified through computer simulations. The simulation results confirm that the proposed algorithm is effective. Also, an accurate estimation of rotor resistance and speed is achieved under different operation conditions s

Determination of maintenance schedule and process mean of production system

In the literature it is very common to treat the problem of determining the optimal mean of a process (process target) and the optimal preventive maintenance schedule as separate problems. The purpose of this paper is to integrate those decisions and develop a model that finds the optimal selection of process mean and frequency and interval length of preventive maintenance. In developing the model, we assume that the amount of raw material an item receives follows normal distribution. Moreover, a constant-interval preventive maintenance policy is used in this paper. A closed-form solution is obtained when the time to failure of equipment follows a general distribution. Also, sensitivity analysis is conducted on key parameters of the model.