Preventive Maintenance Operations Research Articles

Dynamic Clustering of Wind Turbines Using SCADA Signal Analysis

This work explores the ability to dynamically group the Wind Turbine (WT) of a Wind Farm (WF) based on the behavior of some of their Supervisory Control And Data Acquisition (SCADA) signals to detect the turbines that exhibit abnormal behavior. This study is centered on a small WF of five WTs and uses the observation that the same signals from different WTs in the same WF coherently evolve temporally in a time domain, describing very similar waveforms. In this contribution, averaged signals from the SCADA system are used and omit maximums, minimums and standard deviations, focusing mainly on velocities and other slowly varying signals. For the temporal analysis, sliding windows of different temporal durations are explored. The signals are encoded using the Discrete Cosine Transform, which reduces the problem’s dimensions. A hierarchical tree is built in each time window. Clusters are formed by pruning the tree using a threshold interpretable in terms of distance. It is unnecessary to work with an a priori known number of clusters. A protocol for enumerating the clusters based on the tree’s shape is then established, making it easier to follow the evolution of the clusters over time. The capability to automatically identify WTs whose signals differ from the group’s behavior can alert and program preventive maintenance operations on such WTs before a major breakdown occurs.

An efficient collaborative multi-swap iterated greedy algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance

Under the context of globalization, distributed multi-factory production model is becoming the mainstream of the manufacturing industry because it provides enterprises with flexible and efficient production solutions. In real-world environments, the wear and tear of machines is unavoidable, so it is necessary to maintain equipment for sustainable processing and production. This paper explores the distributed permutation flowshop scheduling problem with preventive maintenance operations (DPFSP/PM) and introduces a collaborative multi-swap iterated greedy (CMSIG) algorithm to minimize the makespan. In the initialization phase, an improved heuristic algorithm is proposed, which re-swaps inserted jobs based on NEH2 to generate initial solutions. In the destruction phase, an adaptive destroy method is proposed. The factory is selected based on a tournament strategy and a critical factory strategy, and then the jobs are greedily reinserted in the construction phase. In the local search phase, four swap operators operate together to enhance their ability for exploring the solution space. Finally, extensive experiments show that the proposed CMSIG algorithm has significant advantages in solving DPFSP/PM compared with other algorithms.

Maintenance policy of degradation components based on the two-phase Wiener process

This paper proposes a condition-based maintenance policy for the two-phase Wiener degradation process components. The main contribution of this article is to provide the time distribution of degradation failures for the two-phase Wiener process degradation component, as well as the modeling and solving methods for two-phase maintenance. The two-phase maintenance policy includes two-phase inspection and preventive replacement maintenance operations. The established optimization maintenance policy model aims to minimize long-term operation costs. The specific cost calculation equation and the solution method of the maintenance model are given. The feasibility of the maintenance policy model is verified using the two-phase degradation data of the Liquid Coupling Devices. The Particle swarm optimization algorithm can stably solve the described problem, and the results show that the two-phase maintenance policy can be more economical and improve components availability. After that, we also analyzed the impact of the cost parameters on the maintenance policy through sensitivity analysis.

On the multi-period combined maintenance and routing optimisation problem

This paper focuses on the combined maintenance and routing optimisation problem in a multi-period environment that consists of scheduling maintenance operations for a set of geographically distributed machines, subject to non-deterministic failures with a set of technicians that perform preventive maintenance and corrective operations. This study uses a two-step approach based on column generation. The first step consists of a maintenance model that determines the optimal time until the next preventive maintenance operation, and each machine’s maintenance frequency while minimising the total expected maintenance costs. The second step involves a routing model that assigns and schedules maintenance operations for each technician over the planning horizon while minimising the routing and maintenance costs. We formulate the second step problem as a periodic vehicle routing problem and to solve it, we propose a column generation approach. The master problem is a set partitioning formulation that indicates which machines must be visited in the planning horizon. This decomposition has two auxiliary problems: the first one is an elementary shortest-path problem to find a feasible path for each period and the second one finds a set of visiting periods for each machine. Our approach balances the maintenance cost, routing cost, and failure probabilities.

An Efficient Heuristic Algorithm for Flow Shop Scheduling Problem with Periodic Machine Specific Halting and Preventive Maintenance Operations

An Efficient Heuristic Algorithm for Flow Shop Scheduling Problem with Periodic Machine Specific Halting and Preventive Maintenance Operations

Justification of the methods of material and technical maintenance and repair of agricultural machines

This work substantiates the rational structure of the repair and maintenance base of agricultural machinery, methods for determining the needs for material and technical means, taking into account the appropriate proportions between the links of the repair network. The maintenance and restoration of the operability of tractors and other agricultural machines in sufficient limits is ensured, as is known, with the help of a system of maintenance and repair, which provides for the implementation of preventive maintenance operations and the conduct of routine and major repairs of machines in a planned manner. The established system of maintenance and repair, the creation of a material and technical basis for its implementation, as well as a continuous process of improving the qualifications of machine operators and repair personnel, contribute to the effective use of equipment in agriculture. At the same time, there are significant disadvantages in the use of tractors and agricultural machines. The analysis of the results of numerous studies of VIMa, ChIMESXa and other organizations made it possible to identify and systematize the loss of time due to machine downtime. In particular, machine downtime for regulated maintenance is 10-15% of the shift time, for technical malfunctions - from 5-8% for plowing, up to 30% for harvesting crops. The volume of work on the repair of cars in the country as a whole is more than 2 times higher than the standard. The efficiency of maintenance and repair of machines largely depends on the level of the material and technical base of these works - the availability of modern equipment and tooling, industrial buildings, warehouses, and other structures, inventories, means of transport and communications.

A modified harmony search for the T-single machine scheduling problem with variable and flexible maintenance

A common assumption in the scheduling literature provides that machines are always available, while often they have to be stopped because of preventive maintenance operations. Inspired by a real-life manufacturing context, this paper deals with the single machine scheduling problem with a flexible/variable maintenance to be executed within a specific time window. Differently from other studies in the literature, a set of non-resumable jobs with sequence dependent setup-times and non-zero release dates have to be scheduled along with a maintenance activity whose duration is a non-decreasing function of its starting time, with the objective of minimizing the total tardiness (T-problem). A mathematical model of the problem is proposed and, due to the high computational complexity, several heuristics have been implemented and tested with the aim of using some of them to make metaheuristic algorithms more effective and computationally efficient. Hence, a modified harmony search, powered by several reinforcement strategies and even equipped with a self-adaptive calibration mechanism, has been developed and compared with a set of additional algorithms proposed by the literature. Numerical results reveal that the modified harmony search outperforms the competing algorithms and no performance loss arises by adopting the self-adaptive mechanism. Finally, a proper sensitivity analysis allows assessing the impact of several influencing factors on the total tardiness minimization.

Optimal Periods of Conducting Preventive Maintenance to Reduce Expected Downtime and Its Impact on Improving Reliability.

The present research mainly aims to use a mathematical formula to determine the optimal intervals for conducting preventive maintenance operations for machines to reduce the expected failure time when the malfunction data follow the Weibull distribution. The reliability function, failure rate, and the average time between machine failures were derived after performing preventive maintenance operations and before conducting preventive maintenance operations to state the amelioration that happens to machines. These rely on real data of performing preventive maintenance operations and the downtime required to repair machine or device faults that occur between preventive maintenance periods and the downtime necessary to perform preventive maintenance operations on the machine or device. Thus, the study concluded that preventive maintenance operations are working to increase the reliability of the machine and improve it, as well as to increase the average period of time for the machine to operate between faults.

Static Integration Based on Stochastic P-Time Petri Nets of Maintenance Scheduling for Railway Transport Equipment

In transport systems, all equipment requires maintenance, which directly affects the machine’s availability and consequently the planned transport schedule. The purpose of this paper is to carry out a method for integrating recovery jobs in railway systems. The proposed method allows the insertion of preventive and corrective maintenance operations when the transport equipment is available in order to minimize periods of inactivity, avoid catastrophic scenarios, and maintain stability and safety of the studied networks. A computing algorithm, allowing insertion of the planned recovery tasks in periods of metro availability, without changing the initial scheduling solution, is established. Finally, we illustrate the implementation of the proposed approach on Tunisian Sahel railway transport networks.

An effective multi-start iterated greedy algorithm to minimize makespan for the distributed permutation flowshop scheduling problem with preventive maintenance

In recent years, distributed scheduling problems have been well studied for their close connection with multi-factory production networks. However, the maintenance operations that are commonly carried out on a system to restore it to a specific state are seldom taken into consideration. In this paper, we study a distributed permutation flowshop scheduling problem with preventive maintenance operation (PM/DPFSP). A multi-start iterated greedy (MSIG) algorithm is proposed to minimize the makespan. An improved heuristic is presented for the initialization and re-initialization by adding a dropout operation to NEH2 to generate solutions with a high level of quality and disperstiveness. A destruction phase with the tournament selection and a construction phase with an enhanced strategy are introduced to avoid local optima. A local search based on three effective operators is integrated into the MSIG to reinforce local neighborhood solution exploitation. In addition, a restart strategy is adpoted if a solution has not been improved in a certain number of consecutive iterations. We conducted extensive experiments to test the performance of the presented MSIG. The computational results indicate that the presented MSIG has many promising advantages in solving the PM/DPFSP under consideration.

DETERMINATION OF THE OPTIMAL PERIOD FOR CARRYING OUT PREVENTIVE MAINTENANCE TO MINIMIZE EXPECTED TOTAL COST

The essential objective of this research is to decide the optimal periods for conducting the preventive maintenance operations (PM) for machines so as to minimize the expected total costs through relying on data concerning the cost of repair or replacement activities (C_s) and the cost of preventive maintenance (C_r). Moreover, the reliability function was calculated for the machines before and after the maintenance operations conducting, considering the data has a Weibull distribution. Also, the application was carried out on real data representing the operating times of the machines between faults for some machines of the National Company for Sponge and Plastic Industry. Thus, the present research concluded that the preventive maintenance works to minimization the total expected costs of maintenance operations, raise the reliability of the machine and its improving, and increase the Mean period of time for machine operation between faults (MTBF) in the case that the shape parameter is increased. However, if the shape parameter is fixed, the preventive maintenance process is useless or with no avail.

A Critical Review of Sensors for the Continuous Monitoring of Smart and Sustainable Railway Infrastructures

Real-time and continuous monitoring through smart sensors is considered to be the evolution of traditional track testing, enabling the earlier detection of the main failure modes that degrade railway tracks. Through carrying out preventive maintenance operations, infrastructure resources may be optimized, leading to smarter and more sustainable infrastructure. For this reason, under the larger goal of creating a synergy with various types of sensors for railway tracks, this article presents a critical review on the different, currently available sensors for smart and continuous monitoring. Specifically, the most appropriate monitoring technologies for each of the main railway track failure modes have been assessed and identified, thus deriving the advantages and capacities of each solution. Furthermore, this review presents some of the main experiences carried out to date in literature by using sensor technologies, such as strain gauges, piezoelectric sensors, fiber-optics, geophones and accelerometers. These technologies have proven to offer appropriate characteristics and accuracy for the continuous monitoring of a railway track’s structural state, being capable of measuring different parameters, such as deflections, deformations, stresses or accelerations that would permit the technical tracking of various forms of degradation.

Scheduling proportionate flow shops with preventive machine maintenance

We examine machine scheduling problems that have been inspired by the production environment of semiconductor manufacturing. In semiconductor manufacturing a job processing machine is often enforced to go through a preventive maintenance operation with an objective of minimizing the wafer quality risk. In this research such periodic machine maintenance is considered for single machine and flow shop scheduling models. For the flow shop model we focus on an important special case with proportionate processing times. We first present that the maximum lateness and the total completion time in a single machine can be minimized by a monotone scheduling rule. For the maximum lateness it is shown that the scheduling rule of a single machine special case is also optimal for a proportionate flow shop. However, for the total completion time we observe that the scheduling algorithm of a single machine is not necessarily optimal for its extension to a proportionate flow shop. We prove that the total completion time in a proportionate flow shop is solvable by an O(n2) algorithm, and even the total weighted completion time is minimized with the same computational complexity. Lastly, we discuss how these results are further generalized when setup operations are additionally considered.

Integrating preventive maintenance activities to the no-wait flow shop scheduling problem with dependent-sequence setup times and makespan minimization

In this paper, preventive maintenance (PM) operations are incorporated as a constraint to the m-machine no-wait flow shop problem with dependent-sequence setup times and makespan minimization scheduling problem. PM are based on the concept of flexible and diverse maintenance levels (ML). Flexibility means that PM activities can be carried out in other periods besides the pre-determined. In order to ensure the diversity of each ML is determined according to different parameters of a PM policy based on Weibull distribution. A MILP is proposed to describe the problem. A procedure to assign PM activity in the sequence of jobs are proposed. As solution methods for the problem, the main constructive heuristics for the m-machine no-wait flow shop with makespan minimization are implemented in the original and adapted versions to solve small and large problem sizes. Computational results show that LWW and FCH are the best heuristics regarding the quality of solution in large problem sizes and generate the better results in small sizes, compared with the MILP solutions. In comparison with other procedures to scheduling PM operations to the sequence of jobs, the proposed procedure generates better results in all large problem sizes.

An extensive evaluation of ensemble techniques for software change prediction

AbstractPredicting the areas of the source code having a higher likelihood to change in the future represents an important activity to allow developers to plan preventive maintenance operations. For this reason, several change prediction models have been proposed. Moreover, research community demonstrated how different classifiers impact on the performance of devised models as well as classifiers tend to perform similarly even though they are able to correctly predict the change proneness of different code elements, possibly indicating the presence of some complementarity among them. In this paper, we deeper investigated whether the use of ensemble approaches, ie, machine learning techniques able to combine multiple classifiers, can improve the performances of change prediction models. Specifically, we built three change prediction models based on different predictors, ie, product‐, process‐ metrics‐, and developer‐related factors, comparing the performances of four ensemble techniques (ie, Boosting, Random Forest, Bagging, and Voting) with those of standard machine learning classifiers (ie, Logistic Regression, Naive Bayes, Simple Logistic, and Multilayer Perceptron). The study was conducted on 33 releases of 10 open‐source systems, and the results showed how ensemble methods and in particular Random Forest provide a significant improvement of more than 10% in terms of F measure. Indeed, the statistical analyses conducted confirm the superiority of this ensemble technique. Moreover, the model built using developer‐related factors performed better than the other models that exploit product and process metrics and achieves an overall median of F measure around 77%.

Prediction of tram track gauge deviation using artificial neural network and support vector regression

ABSTRACTAlong with increase in tram traffic, gradual increase in gauge deviation is inevitable. Excessive track gauge is considered as an effective parameter in aggravating the quality of ride as well as the traffic safety of tram services. To ensure a smooth ride, reduce the risk of the train derailment and lower cost of maintenance, preventive maintenance operations are necessary which demand accurate track degradation prediction modelling. In this study, the Melbourne tram network is considered as a case study. Two types of rail tracks including straight segments and curved segments are studied. In order to develop models for gauge degradation prediction, two machine learning models including artificial neural network (ANN) and support vector machine (SVM) are applied. Two indexes including the coefficient of determination (R2) and mean squared error (MSE) are used to evaluate the performance of the proposed models. According to the results, both ANN and SVM models provide acceptable and somewhat similar outcomes. However, the performance of ANN models in predicting gauge deviation of straight segments is slightly better than SVM models. On the contrary, the performance of SVM models in predicting gauge deviation of curved segments is slightly better than ANN models.

Integration of Genetic Algorithm and Support Vector Machine to Predict Rail Track Degradation

Gradual deviation in track gauge of tram systems resulted from tram traffic is unavoidable. Tram gauge deviation is considered as an important parameter in poor ride quality and the risk of train derailment. In order to decrease the potential problems associated with excessive gauge deviation, implementation of preventive maintenance activities is inevitable. Preventive maintenance operation is a key factor in development of sustainable rail transport infrastructure. Track degradation prediction modelling is the basic prerequisite for developing efficient preventive maintenance strategies of a tram system. In this study, the data sets of Melbourne tram network is used and straight rail tracks sections are examined. Two model types including plain Support Vector Machine (SVM) and SVM optimised by Genetic Algorithm (GA- SVM) have been applied to the case study data. Two assessment indexes including Mean Squared Error (MSE) and the coefficient of determination (R2) are employed to evaluate the performance of the proposed models. Based on the results, GA-SVM model produces more accurate outcomes than plain SVM model.

Joint Optimization of Preventive Maintenance, Spare Parts Inventory and Transportation Options for Systems of Geographically Distributed Assets

Maintenance scheduling for geographically dispersed assets intricately and closely depends on the availability of maintenance resources. The need to have the right spare parts at the right place and at the right time inevitably calls for joint optimization of maintenance schedules and logistics of maintenance resources. The joint decision-making problem becomes particularly challenging if one considers multiple options for preventive maintenance operations and multiple delivery methods for the necessary spare parts. In this paper, we propose an integrated decision-making policy that jointly considers scheduling of preventive maintenance for geographically dispersed multi-part assets, managing inventories for spare parts being stocked in maintenance facilities, and choosing the proper delivery options for the spare part inventory flows. A discrete-event, simulation-based meta-heuristic was used to optimize the expected operating costs, which reward the availability of assets and penalizes the consumption of maintenance/logistic resources. The benefits of joint decision-making and the incorporation of multiple options for maintenance and logistic operations into the decision-making framework are illustrated through a series of simulations. Additionally, sensitivity studies were conducted through a design-of-experiment (DOE)-based analysis of simulation results. In summary, considerations of concurrent optimization of maintenance schedules and spare part logistic operations in an environment in which multiple maintenance and transpiration options are available are a major contribution of this paper. This large optimization problem was solved through a novel simulation-based meta-heuristic optimization, and the benefits of such a joint optimization are studied via a unique and novel DOE-based sensitivity analysis.

Short‐term crude oil scheduling with preventive maintenance operations: a fuzzy stochastic programming approach

AbstractThis paper presents a fuzzy stochastic mathematical model for a short‐term crude oil scheduling problem with preventive maintenance for charging tanks. The objective function of the proposed model minimizes the supply chain total cost. Lost sale is also reflected in the objective function acting as a criterion for the supply chain responsiveness. This study is among the first attempts to consider preventive maintenance operations, unavailability of the charging tanks, and minimizing lost sale for crude oil scheduling problem. The model encounters two types of uncertainty, namely fuzzy and stochastic uncertainty caused by demand, cost, and time parameters. Fuzzy programming and chance constraint method are employed to formulate the nondeterministic model. The model's applicability is illustrated with a case study. Results indicate that in cases where demand is sizable compared to storage volumes and maximum flow rate is sufficiently large, a preventive maintenance system can be scheduled regularly. Furthermore, the performed sensitivity analyses reveal that the problem is more sensitive to demand uncertainty than to cost and time uncertainty.

Quantitative analysis of the impact of maintenance management on the energy consumption of a hospital in Extremadura (Spain)

The aim of this paper is to analyse the impact of maintenance management on the energy consumption of a hospital in Extremadura (Spain) and to look for existing relationships between the time spent on maintenance operations and the energy consumption of the building.The results show that an average annual increase of 6% in time spent on preventive maintenance operations, over a period of 5 years, resulted in a 20% decrease in the demand for corrective maintenance and an average annual saving of 500 MWh in energy consumption. This in turn prevented the release of 186 tons of CO2 and other greenhouse gases into the atmosphere. This represents an annual saving of 75,000 €, with no increase in either human resources used nor in maintenance costs. Air conditioning installations, Low Voltage systems and Plumbing installations consume more than 80% of the resources applied to building maintenance.