This study aimed to develop a multi-objective approach for reducing the positional errors in geometry measurements of track as a linear asset. Accordingly, we evaluated and compared two alignment methods – recursive segment-wise peak alignment (RSPA) and modified correlation optimised warping (MCOW). Furthermore, a novel rule-based approach was introduced to avoid data loss while aligning the datasets of the measurements of linear assets. A case study was conducted to implement and assess the performance of these methods in reducing the positional errors in track geometry measurements. The results revealed that the rule-based method preserves all the single defects present in the datasets. Furthermore, RSPA outperforms MCOW when aligning peaks, whereas MCOW is more efficient when all the data points in the datasets have equal priority.

Railway track, as a critical infrastructure, plays a significant role in freight transportation. However, Railway track degrades with age and usage and can impact negatively track availability and safety. Tamping actions are used to rejuvenate the degradation and recover the functionality of the track to an acceptable level. Tamping actions are performed in a form of preventive and corrective regimes. In performing an effective tamping regime, the recovery of both preventive and corrective tamping should be taken into account. In addition, the occurrence of isolated defects should be considered. By combining the recovery model with the degradation model, the long-term behavior of the track geometry can be predicted, and an accurate estimation of tamping needs can be provided, leading to optimum tamping scheduling. In this study, the effects of tamping recovery are modeled for both preventive and corrective strategies. For this aim, the values of both standard deviation (SD) and isolated defects have been predicted and their values before tamping are used as explanatory variables in a multivariable regression model. Finally, the effect of tamping recovery on the values of both SD and isolated defects is estimated. A case study is performed on a heavy haul line located in Sweden’s rail network to evaluate the performance of the proposed multivariable regression model. Observations showed that the model and its coefficients are significant with P-values close to zero, and the R-squared value suggests that the model explains approximately 70% of the variability in the response variable recovery.

This paper proposes a safety-critical standby component unavailability model that contains aging effects caused by the elapsed time from installation, component degradation due to surveillance tests, and imperfect maintenance actions. An application of the model to a Motor-Operated Valve and a Motor-Driven Pump involved in the HPIS of a VVER/1000-V446 nuclear power plant is demonstrated and compared with other existing models at component and system levels. In addition, the effects of different unavailability models are reflected in the NPP's risk criterion, i.e., core damage frequency, over five maintenance periods. The results show that, compared with other models that do not simultaneously consider the full effects of degradation and maintenance impacts, the proposed model realistically evaluates the unavailabilities of the safety-related components and the involved systems as a plant age function. Therefore, it can effectively reflect the age-dependent CDF impact of a given testing and maintenance policy in a specified time horizon.

An efficient maintenance strategy is a key factor to ensure acceptable safety levels and manage the overall costs of a railway network. So far, research focused mainly on regional and high-speed rail networks, causing a knowledge and regulation gap between standard and narrow-gauge railways networks. These networks, like the local or isolated railways, have peculiar geometric and operational characteristics that play a key role on track geometry quality degradation process. For this reason, the maintenance knowledge gained in the context of standard railways when applied to systems such as narrow-gauge railways could lead to non-optimized use of the limited resources available. Therefore, the aim of this study is to analyse how the track geometry quality degradation behaviour is influenced by the key characteristics of narrow-gauge railways through the analysis of the track geometry data of an Italian local railway, laying the foundation for optimized maintenance strategies for these systems.

This paper proposes a modification to a well-known alignment method, correlation optimized warping (COW), to improve the efficiency of the method and reduce the positional errors in the measurements of linear assets. The modified method relaxes the restrictions of COW in aligning the start and end of datasets and decreases the computational time. Furthermore, the method takes advantage of the interdependencies between simultaneously measured channels to overcome the missing data problem. A case study on railway track geometry measurements was conducted to implement the proposed method and assess its performance in reducing the positioning inaccuracy of the measurements. The findings revealed that the modified method could decrease the positional errors of defects to below 25 cm in 94% of the trials.

The aim of this study has been to predict the track geometry degradation rate using artificial neural network. Tack geometry measurements, asset information, and maintenance history for five line sections from the Swedish railway network were collected, processed, and prepared to develop the ANN model. The information of track was taken into account and different features of track sections were considered as model input variables. In addition, Garson method was applied to explore the relative importance of the variables affecting geometry degradation rate. By analysing the performance of the model, we found out that the ANN has an acceptable capability in explaining the variability of degradation rates in different locations of the track. In addition, it is found that the maintenance history, the degradation level after tamping, and the frequency of trains passing along the track have the strongest contributions among the considered set of features in prediction of degradation rate.

This paper presents an in-depth case study of a heavy-haul railway line in Sweden to analyze the twist and longitudinal level geometry defects. A linear model was applied to model the evolution of the amplitude of the longitudinal level defects and twist over time. Despite the effect of the defect shapes on the dynamic track loads, the amplitude of the defects still is the only criterion used for the assessment of geometry defect severity. The application of first- and second-order derivatives to capture information about the shape of defects was investigated in the case study. In addition, the RUSBoost algorithm was used to classify track sections into healthy and unhealthy sections using the imbalance class data set. In this algorithm, the standard deviation and the kurtosis of the geometry parameters were used as explanatory variables. Finally, the abnormal track geometry degradation patterns identified in the case study were explored in detail. The results of the analysis can be used directly in maintenance modeling and used for the purpose of maintenance scheduling. 

This study has been dedicated to the optimization of opportunistic tamping scheduling. The aim ofthis study has been to schedule tamping activities in such a way that the total maintenance costs andthe number of unplanned tamping activities are minimized. To achieve this, the track geometry tampingscheduling problem was defined and formulated as a mixed integer linear programming (MILP)model and a genetic algorithm was used to solve the problem. Both the standard deviation of thelongitudinal level and the extreme values of isolated defects were used to characterize the trackgeometry quality and to plan maintenance activities. The performance of the proposed model wastested on data collected from the Main Western Line in Sweden. The results show that different scenariosfor controlling and managing isolated defects will result in optimal scheduling plan. It is alsofound that to achieve more realistic results, the speed of the tamping machine and the unused life ofthe track sections should be considered in the model. Moreover, the results show that prediction ofgeometry condition without considering the destructive effect of tamping will lead to an underestimationof the maintenance needs by 2%.

To predict the occurrence of geometry defects and to achieve a reliable maintenance strategy, accurate positioning of track geometry measurements is of great importance. This paper aims to reduce the positional errors in track geometry measurements by finding an efficient alignment method. Therefore, five alignment methods, i.e. the cross-correlation function, recursive alignment by fast Fourier transform, dynamic time warping, correlation optimized warping, and a combined method, were evaluated and compared concerning their ability to align the measurements precisely, keep the original shape of the measurements, and minimise the use of time and memory. Furthermore, the influence of choosing a proper reference dataset was investigated. A case study based on track geometry data from the Main Western Line in Sweden was conducted to implement and assess the methods. Findings revealed that the combined method could decrease the positional errors of single defects to below 0.25 m in 90% of the trials.

Track is the fundamental part of railway infrastructure and represents a significant part of maintenance effort and cost. For example, in Sweden, the annual maintenance cost for only track geometry is between110 and 130 MSEK. The quality of the track, mostly, is represented by the track geometry properties. Track geometry degrades with age and usage; and loses its functionality over time. Poor quality of track geometry may result in safety problems, speed reduction, traffic disruption, greater maintenance cost, and higher degradation rate of the other railway components (e.g. rails, wheels, switches, and crossings). Railway track maintenance program development is challenging and requires appropriate modeling which reflects the real-life scenario and integrates influencing factors. In addition, there are several uncertainties in data collection, data analysis, modeling, and the prediction that are needed to be considered. Moreover, there is a lack of integrated platform that is able to access geometry data, extract associatedinformation, and retain this knowledge for supporting adaptive maintenance planning and scheduling. The above challenges necessitate the Infrastructure Manager (IM) to employ a maintenance management system that enables higher capacity for evaluation of track performance, learning from asset history, context-driven awareness, planning & scheduling, and transformation of this information to knowledge for decision making. The SIMTRACK project will facilitate simulation-based platform that enables development of a tools,methodologies and techniques for optimization of track geometry maintenance planning, scheduling andopportunistic maintenance. This will provide a basis to predict track geometry degradation, analyse therisk of failures and forecast the maintenance activities as well as renewal investment requirements. The results will enhance safety, maximize capacity utilization, and lead to an efficient and cost effective maintenance program. The project structure track is structured into 6 work packages. WP1 deals with the project management. WP2 presentsthe industrial scenarios, specifications and requirements that provide inputs to WP3 and WP4. WP3 andWP4 are defined as predictive modelling and analytics of track geometry condition and trackmaintenance optimization and decision support system respectively. WP5 is dedicated to evaluation ofabsolute track geometry condition. Finally, WP6 deals with dissemination and exploitation, is devotedfor formulating comprehensive plans for results assimilation by the partners and set the ground for theexploitation. Figure 1 shows the work packages and their relationships.

A large part of the railway track geometry maintenance burden concerns local maintenance activates conducted to rectify isolated defects. Isolated defects are short irregularities in the track geometry that can dramatically increase the dynamic forces between the wheel and rail, which in turn will accelerate the growth or occurrence of internal rail defects. The dynamic force between the wheel and rail is dependent on the shape of the isolated geometry defects. However, the severity of isolated defects is mainly defined only by their amplitude. Therefore, in addition to amplitude, other characteristics of geometry defects must be considered to analyze severity of defects and to prioritize local maintenance actions. This study aims to use the first- and second-order derivatives of the longitudinal level defects to plan local maintenance activities. The derivatives of geometry defects provide useful information about the shape of defects. The information is used to categorize the isolated defects based on their severities and prioritize maintenance actions. In this regard, K-means clustering technique is applied. The results of this study will support the decision-making process regarding the planning of local maintenance activities. The foot-by-foot track geometry data collected from Main Western Line in Sweden is used to implement and test the model.

In order to evaluate the railway track geometry condition and plan maintenance activities, track inspection cars run over the track at specific times to monitor it and record geometry measurements. Applying an adequate inspection interval is vital to ensure the availability, safety and quality of the railway track, at the lowest possible cost. The aim of this study has been to investigate the effect of different inspection intervals on the track geometry condition. To achieve this, an integrated statistical model was developed to predict the track geometry condition given different inspection intervals. In order to model the evolution of the track geometry condition, a piecewise exponential model was used which considers break points at the maintenance times. Ordinal logistic regression was applied to model the probability of the occurrence of severe isolated defects. The Monte Carlo technique was used to simulate the track geometry behaviour given different inspection intervals. The results of the proposed model support the decision-making process regarding the selection of the most adequate inspection interval. The applicability of the model was tested in a case study on the Main Western Line in Sweden.

The aim of this study has been to develop a data-driven analytical methodology for prediction of isolated track geometry defects, based on the measurement data obtained from a field study. Within the study, a defect-based model has been proposed to identify the degradation pattern of isolated longitudinal level defects. The proposed model considered the occurrence of shock events in the degradation path. Furthermore, the effectiveness of tamping intervention in rectifying the longitudinal level defects was analysed. The results show that the linear model is an appropriate choice for modelling the degradation pattern of longitudinal level defects. In addition, a section-based model has been developed using binary logistic regression to predict the probability of occurrence of isolated defects associated with track sections. The model considered the standard deviation and kurtosis of longitudinal level as explanatory variables. It has been found that the kurtosis of the longitudinal level is a statistically significant predictor of the occurrence of isolated longitudinal level defects in a given track section. The validation results show that the proposed binary logistic regression model can be used to predict the occurrence of isolated defects in a track section.

A great part of the cost of railway maintenance is related to the track geometry. Reliable and complete track geometry data form the foundation of an efficient and effective condition-based maintenance strategy. Generally, track geometry measurement data suffer from positional errors. Because the aim of track geometry degradation modeling has been to analyze the evolution of location-specific defects over time, accurate information on the position of defects is of crucial importance. Therefore, collected track geometry measurements in different inspection runs need to be pre-processed before they are used to model geometry degradation or implement a condition-based maintenance strategy. In this study, three relative position-based approaches are applied to align the positional measurement data obtained from different inspection runs. The two main types of data positional errors are shifted and stretched waveforms. In this regard, three time series alignment algorithms, i.e., crosscorrelation function, dynamic time warping, and dynamic time alignment kernel, are applied to align both shifted and stretched waveforms. Foot-by-foot track geometry data obtained from the main Western line in Sweden are used to implement and test the models. On the basis of the results, dynamic time warping outperforms the other two techniques to align shifted and stretched datasets.

The objective of this study has been to develop an approach to the allocation of an effective maintenancelimit for track geometry maintenance that leads to a minimisation of the total annual maintenancecost. A cost model was developed by considering the cost associated with inspection, preventivemaintenance, normal corrective maintenance and emergency corrective maintenance. The standarddeviation and extreme values of isolated defects of the longitudinal level were used as quality indicatorsfor preventive and corrective maintenance activities. The Monte Carlo technique was used tosimulate the track geometry behaviour under different maintenance limit scenarios and the effectivelimit was determined which minimises the total maintenance cost. The applicability of the model wastested in a case study on the Main Western Line in Sweden. Finally, a sensitivity analysis was carriedout on the inspection intervals, the emergency corrective maintenance cost and the maintenanceresponse time. The results show that there is an optimal region for selecting an effective limit.However, by considering the safety aspects in track geometry maintenance planning, it is suggestedthat the lower bound of the optimal region should be selected.

Track geometry degradation is a function of several interacting factors (e.g. traffic, environmental conditions, track structure, and maintenance history) and each interaction may follow several interrelating rules. Artificial Neural Network (ANN) showed a high capability in prediction of track geometry degradation, by analysing a set of features that explain the behaviour of geometry evolution. The aim of this study is to develop an approach using ANN to predict the degradation of track geometry along a track line. In the ANN model, maintenance history, curvature, annual traffic, presence of level cross, speed, frequency of trains passing along the track and material type and age are inputs to the model and track geometry degradation rate is the output of the model. A set of data from railway network in Sweden are used to train and validate the model. The results indicate that ANN is able to predict the degradation of track geometry.

Railway track geometry subjects to degradation with age and usage and highly affects track functionality. An accurate prediction of track geometry degradation is essential to enhance the performance of maintenance planning and scheduling. In this study, artificial neural network is used to predict track geometry degradation rate. The data of five lines from Swedish railway network are used to develop the model. The standard deviation of the longitudinal level is used as quality indicator for track geometry degradation. For all track sections, a set of features which may contribute in track geometry degradation are collected. Principal component analysis method is used to reduce the dimension of the features to facilitate the training of the ANN model. Then, the new principal features considered as inputs to ANN model. The results indicate that the proposed method can be used to predict track geometry degradation along the track line.

Accurate prediction of track geometry degradation is essential for an efficient track geometry maintenance planning and scheduling. Track geometry prediction is a complex task as many quantitative and qualitative parameters affect track geometry degradation. Artificial neural networks (ANNs) have shown a great capability in prediction of such complex systems, although they are not complicated. In this paper, a three-layered feedforward network is employed to predict track geometry degradation for each track section with-in a track line for a period of two years. A set of influencing factors along with track geometry degradation history are used as inputs to the model. The weight and bias values in the ANN model are optimised using Levenberg and Marquardt optimization algorithm.  Data from the Swedish railway network are used to train and verify the proposed model. The relative importance of input parameters on track geometry degradation is determined by Garson`s algorithm. The results indicate that ANN is able to predict the future state of the track in next two years even in the existence of tamping activity within the degradation history.

Within aviation enterprises, the process of dismantling an aircraft at the end of its life is referred to as parting-out. Obviously, the asset value of the units and materials parted out from the retired airframes can be considerable. The benchmarked best practice within the aviation industry is to dismantle the retired aircraft and use the parted-out spares to support the remaining fleet or to offer them on the surplus market. Part-out-based spares provisioning (PBSP) has been a major focus of attention for aviation companies. The PBSP approach is a complex task that requires a multidisciplinary and integrated decision-making process. In order to control the stock level and fulfil the decision criteria within PBSP, it is necessary to make decisions on the termination, at specific times, of both the parting-out process and the maintenance and repair actions performed on the units.

This paper considers repairable units and introduces a computational model to identify the applicable alternatives for repair termination times that will minimize the number of remaining spares at the end of the retirement period, while fulfilling the availability requirement for spares during the PBSP period, at the lowest possible cost.  The feasible alternatives are compared with regard to their respective costs, and the most cost-effective solution is selected. The cost model uses estimates of future maintenance requirements, the turn-around times, the cost of the various maintenance tasks, the future spares consumption, and the estimated salvage of spares from retired aircraft. The output of the model is a set of applicable alternatives which satisfy the availability requirements for spares for the active fleet. The method is illustrated using a case study performed on the Saab-105 training aircraft. 

The results show that the proposed PBSP approach and computational model provide added value from a sustainability point of view, since the use of existing resources is maximized during the retirement process, through the process of reclaiming units and the applicable maintenance termination alternatives. The implementation of the proposed computational model in a PBSP programme provides a detailed and situation-based overview of the stock level dynamics, and contributes to the spares provisioning process by providing solutions to issues such as obsolescence, last-time buys and cannibalization.

Tamping is one of the major activities applied by railway maintenance managers to recover track geometry condition. Modelling the tamping effectiveness along with track geometry degradation is essential for long term prediction of track geometry behaviour. The aim of this study is to analyse theeffect of tamping on the different track geometry measures, i.e. longitudinal level, alignment and cant; on the basis of inspection car records from a part of Main Western Line in Sweden. To model recovery after tamping a probabilistic approach is applied. The track geometry condition before tamping was considered as the dominant factor for modelling the model parameters. Correlation analysis has performed to measure the linear relation between the recoveries of the different geometry measures. The results show a moderate correlation between the recovery of the longitudinal level and that of the cant, and a weak correlation between the recovery of the longitudinal level and that of the alignment. Linear regression and Wiener process are also applied to model track geometry degradation and to obtain degradation rates. Theeffect of tamping on degradation rate is analysed. It is observed that the degradation rate was increased after tamping intervention.  

Accurate prediction and efficient simulation of the evolution of track geometry condition is a prerequisite for planning effective railway track maintenance. In this regard, the degradation and tamping effect should be equipped with proper and efficient probabilistic models. The possible correlation induced by the spatial structure also needs to be taken into account when modelling the track geometry degradation. To address these issues, a two-level piecewise linear model is proposed to model the degradation path. At the first level, the degradation characteristic of each track section is modelled by a piecewise linear model with known break points at the tamping times. At the second level, Autoregressive Moving Average models are used to capture the spatial dependences between the parameters of the regression lines indexed by their locations. To illustrate the model, a comprehensive case study is presented using data from the Main Western Line in Sweden

Reliability analysis of frequency converters based on failures and outages reports constitute an important basis for asset performance and management. Two- and four-state reliability models that recognize the operating characteristics of base load units and peaking units are presented and compared in this study. In this study, a four-state model is modified to a three-state model by combining the ‘needed’ and ‘not-needed’ forced-out states. Moreover, the transitions in the three-state model for power frequency converter have been designed according to real operational data. An outage-reporting database modelled considering IEEE STD 762 is presented and compared with the existing failure-reporting database of the case considered here. Furthermore, a method to extract information missing in the failure-reporting database by electrical readings is proposed to meet the requirements of the outage-reporting database. The study found that the results of indexes based on the IEEE four-state model are not reasonable for the frequency converter given their differences with the gas-turbine results under operational conditions. The forced outage rates and availability factors of twelve actual traction frequency converters of Swedish railways network are presented to validate the modified model.

Increased demand for railway transportation is creating a need for higher train speeds and axle loads. These, in turn,increase the likelihood of track degradation and failures. Modelling the degradation behaviour of track geometry anddevelopment of applicable and effective maintenance strategies has become a challenging concern for railway infrastructuremanagers. During the last three decades, a number of track geometry degradation and maintenance modellingapproaches have been developed to predict and improve the railway track geometry condition. In this paper, existingtrack geometry measures are identified and discussed. Available models for track geometry degradation are reviewedand classified. Tamping recovery models are also reviewed and discussed to identify the issues and challenges of differentavailable methodologies and models. Existing track geometry maintenance models are reviewed and critical observationson each contribution are provided. The most important track maintenance scheduling models are identified and discussed.Finally, the paper provides directions for further research.

Design and production of highly reliable and safer automotive systems with longer life has been a challenge. The pressure is outcome of high competitive market and recent safety issues of reputable car manufacturers. In this paper, an integrated methodology is proposed based on design for reliability of automotive systems and considering its reliability/safety critical sub-systems. In the proposed approach, the FMEA results are used in the process of failure mode/mechanism identification. The basic failure data, mostly obtained from generic databases, are adjusted by multiplicative corrective factors to account for the design and environment impacts on system failure characteristics. The system is modeled by reliability block diagram method, simulated by Monte Carlo technique. The results of FMEA and reliability evaluation are used for system improvement by reducing the components’ failure rates and potential change of system configuration. The components’ reliability is improved by increasing the quality of components by utilization of high quality materials and modern manufacturing techniques. Modification of system configuration, e.g., adding redundancy, is an alternative for system reliability improvement in some cases. The results show that the friction lining component is the most critical elements in terms of reliability importance. After completion of this phase, an assessment is done for system reliability by comparing the system reliability targets. As a case study, dry friction clutch is studied for assessment of the proposed method. In this study, the life test requirement is researched for each component using a reliability testing techniques. Finally, the uncertainties are computed associated with the failure data and final reliability estimations and the results were presented with a confidence interval.

Scheduled maintenance and inspection development is one of the main requirements for emergency equipment and safety devices. These types of devices have hidden functions which are used intermittently or infrequently, so their failure will not be evident to the operating crew. The analytical model presented in this paper deals with the periodically tested units with overhauls (preventive maintenance) after certain number of inspections and a renewal after a series of overhauls. The cost based optimization method presented in this paper identifies the optimum interval and frequency of Failure Finding Inspection (FFI) and restoration. In the proposed model, repair due to failures found by inspection makes the unit As Bad As Old, and restoration/overhaul action rejuvenates the unit to any condition between As Good As New and As Bad As Old. As Good As New effectiveness also is considered for renewal action. It considers inspection and repair times, and takes into account the costs associated with inspection, repair, restoration, and also the cost of accidents due to the occurrence of multiple failure. The results show that when the unit is not under aging process, the optimal alternative for each inspection interval is the one with highest possible number of inspection without restoration. Finally, it is observed that when the cost of accident is quite high it is needed to perform inspections at smaller intervals to control the risk of accident.

Performance-based    Logistics    (PBL)    contracts    require  metrics  and  methodologies  set  in  a  systemic  way  to  provide  readiness  for  the  warfighter  at  reasonable  costs.  There  must be a clever and verifiable connection of metrics in order to access  and  analyze  data and  to  deliver  sound  and  consistent  inputs  for  the  entire  support  system  to  accomplish  its  goals.    A  system   of   incentives   and   penalties   usually   takes   place   and   suggests  functions  to  be  maximized  and/or  minimized  in  a  multi-criterion  and  highly  integrated  environment.  This  study  deals  with  the  optimization  of  the  entire  setup  of  Performance-Based Logistics  contracts  and  is  limited  to  a  preliminary  study  in  a  simple  scenario  that  has  showed  a  promises  results.    A  mixed  method, using both the Ɛ-Constraint  and    Goal  Programming  is  proposed to model the case. Results indicate that metrics used for reliability,    maintainability,    availability    and    supportability    (RAMS)  and  costs  can  be  optimized  simultaneously  for  clever  contracts.

It is reported that switches and crossings (S&C) are one of the subsystems that cause the most delays on Swedish Railways while accounting for at least 13% of maintenance costs [6]. It is the main reason why we chose to base our study on this subsystem.

Intelligent data processing allows understanding the real reliability characteristics of the assets to be maintained. The first objective of this research is to determine the S&C reliability characteristics based on field data collection. Because field failure data are typically strongly censored, an especial statistics software package was developed to process field failure data, as commercial packages have not been found satisfactory in that respect. The resulting software, named RDAT® (Reliability Data Analysis Tool) has been relied upon for this study: it is especially adapted to statistical failure data analysis.

In the next step the availability of studied switches and crossings is estimated based on the reliability characteristics founded in the first step.

In this research, a methodology is presented to optimize the life performance of mechanical systems using multiple objective evolutionary algorithm. Conflicting objectives are encountered (e.g. low costs and longer service life) here along with significant number of constraints (e.g. technological limitations, weight and volume). To solve the design optimization problems, the objective function is weighted with a combination of the targets. The evolutionary algorithm (i.e. GA) is applied for minimization of the system failure rates, reliability allocation optimization. The failure rates are optimally estimated for the system?s critical components. Reliability allocation technique is utilized to determine the optimum reliability of the constituent components with higher failure rates. The reliability targets are determined for the components through the minimized failure rates. The methodology is demonstrated through a case study, on a centrifugal pump. The pump design requires consideration of several targets and requirements with different weight factors for satisfaction (e.g., availability, capability, efficiency and weight) along the pump life. Analytical Hierarchy Process (AHP) is utilized to determine the weights of the objectives. As a result of this research, the equipment design is improved, costly over-designs options are prevented and development tests are optimized.

Corrosion is a major contributor in gas pipeline degradation and failure. Due to significant sources of uncertainties in corrosion progression modelling, nondeterministicapproaches are more promising in recent researches. Several researches were conducted about probabilistic assessment of corrosion and itseffects in practical models. However, in these studies, either internal or external corrosion individually were separately evaluated, without taking intoaccount their correlated effects. In this study, a reliability analysis method is proposed for pressurized bare gas pipeline buried in soil by taking into accountboth corrosion effects from inside and the outside of the pipe. Environment effects are also studied on pipe’s corrosion by considering different soil types. Itwas observed that soil grain size has significant impact in corrosion occurrence. Consequently, sandy clay loam has the highest reliability index while clayand clay loam have the lowest values among these soil types.

In practice, the analyst is often dealing with multiple repairable units, installed in different positions or functioning under different operating conditions, and maintained by different disciplines. This paper presents a decision framework to identify an appropriate reliability model for massive multiple repairable units. It splits non-homogeneous failure data into homogeneous groups and classifies them based on their failure trends using statistical tests. The framework discusses different scenarios for analysing multiple repairable units, according to trend, intensity, and dependency of the units’ failure data. The proposed framework has been verified in a fleet of aircraft and in two simulated data sets. The results show a reliability model of multiple repairable units may contain a mixture of different stochastic models. Considering single reliability models for such populations may cause erroneous calculation of the time to failure of a particular unit, which can, in turn, lead to faulty conclusions and decisions. When dealing with massive and non-homogeneous multiple repairable units, the application of the proposed framework can facilitate the selection of an appropriate reliability model.

Railway transportation is exposed to a higher demand that necessitates the use of trains with higher speed and heavier axle loads. These increase the track geometry degradation rate, which needs a more effective control on geometry degradation. Keeping the track geometry in acceptable levels requires proper inspection and maintenance planning that inevitably entails in-depth knowledge of track geometry degradation. In addition, it is needed to identify the most effective approach for degradation modelling. To do so, it is vital to synthesis published results into a summary of what is known and validated and what is not as a major step. To this end, this paper reviews track degradation models, discusses various degradation measures, and proposes directions for future researches. It is found that combining the mechanistic and statistical approaches can leads to a more accurate prediction of track geometry degradation behaviour.

Maintaining a track line in a good condition is a continuous challenge since it has to deal with various track line heterogeneities that contribute to accelerate its degradation. As a result, railway tracks should be inspected regularly to detect geometry faults and to plan maintenance actions in consequence. A maintenance plan that minimizes track maintenance cost is highly desirable by infrastructure managers. This paper presents an adaptive maintenance scheduling based on track condition prediction. The degradation indicator is the standard deviation of the longitudinal (SDL) level that is sampled on every 200m-long track section. Standards define some thresholds on this indicator that correspond to different levels of severity and related penalty costs. From collected data, a degradation model that uses a random coefficient Wiener degradation-based process is built. A probabilistic model to simulate the recovery effect after the maintenance action (tamping) is also used. Based on this degradation and recovery models, a cost model is built to find the optimal time for tamping on a single track section. After that we use a Monte Carlo approach to assess the performance of the cost model for the whole track line, considering both calendar based and adaptive opportunistic tamping actions. The results show that the adaptive opportunistic maintenance strategy has a lower cost per unit of time than the systematic preventive maintenance.

Track geometry bear huge static and dynamic forces that accelerate degradation process. As a result, railway track should be inspected regularly to detect geometry faults and to plan maintenance actions in advanced. An inspection plan that minimizes track maintenance cost is highly desirable by infrastructure managers. This paper proposes constructing an integrated model to identify the optimum track geometry inspection interval. To this end, it develops a long term prediction model combining degradation, shock event, and tamping recovery models. It applies the Wiener process to model track geometry degradation, simulates shock event times using an exponential distribution, and uses a probabilistic model to model recovery after tamping. With the proposed integrated model and simulation, it is possible to identify the optimum track geometry inspection frequencies that minimize total track maintenance costs.

Several factors such as reliability, availability, and cost may consider in the maintenance modeling. In order to develop an optimal inspection program, it is necessary to consider the simultaneous effect of above factor in the model structure. In addition, for finding the optimal maintenance interval it is necessary to make trade-offs between several factors, which may conflicting each other as well. The study comprises of mathematical formulating an optimal interval problem based on Multi-Attribute Utility Theory (MAUT). The aim of the proposed research is to develop a methodology with supporting tools for determination of optimal inspection in a maintenance planning to assure and preserve a desired level of performance measure such as reliability, availability, risk, etc. For verification and validation purposes, the proposed methodology (analysis approach) and tools (models) will be applied in a real case which given by the literature.

The modelling of maintenance data starts with the black-box approach (where the model selection is based solely on the maintenance data) and then through the grey-box approach for proper analysis (where one can gain insights to build better models). These models allow for more effective maintenance of the object. This paper deals with the grey-box approach to modelling. It discusses the process of modelling and illustrates this through a real case study

The aim of this paper is to develop a reliability-based cost model for periodically inspected units subject to hidden functions, with imperfect restoration (overhaul) action after a certain number of inspections. In the model, effectiveness of the consecutive restoration actions is considered, and possible alternative maintenance strategies are identified and compared. The method is based on the Total Cost and identifies the optimum interval and frequency of inspections, as well as restoration that minimize the total life cycle cost. In the proposed model, repair due to failures found by inspection is considered as minimal repair, and restoration/overhaul action is considered as normal repair. The result also shows that for a specific value of restoration effectiveness (θ0), when θ> θ0, the behavior of alternatives tends to as-bad-as-old and for θ< θ0 tends to an as-good-as-new. It is also observed that when the cost of accident is high it is needed to perform inspections at smaller intervals.

A Traction Power Supply System (TPSS) supplies electricity for train propulsion on electrified railways. In many countries, electrified railways have single-phase low-frequency AC power systems fed by frequency converters (FCs). Outages of power will significantly weaken the TPSS, cause operational problems and ultimately lead to traffic disruption in the form of speed reduction, train delays, or cancellations. The aim of this study is to propose an approach to analyse the reliability of power conversion capacity to discover the reasons for ‘loss of load”. The paper assumes train delays are caused by converter outages, shortages of reserve power capacity for unexpected train loads, or a combination of the two. It considers the availability performance of the converter station and evaluates the reliability of the conversion power capacity. It proposes a power-traffic factor (PTF) to aggregate and quantify train power demands. The PTF can be related to the availability of traction power capacity and will identify areas with risks of power shortages. The study finds the shortage in power leading to train delays in some regions of the system is due to a shortage of reserve power capacity

This paper proposes a model selection framework for analysing the failure data of multiple repairable units when they are working in different operational and environmental conditions. The paper provides an approach for splitting the non-homogeneous failure data set into homogeneous groups, based on their failure patterns and statistical trend tests. In addition, when the population includes units with an inadequate amount of failure data, the analysts tend to exclude those units from the analysis. A procedure is presented for modelling the reliability of a multiple repairable units under the influence of such a group to prevent parameter estimation error. We illustrate the implementation of the proposed model by applying it on 12 frequency converters in the Swedish railway system. The results of the case study show that the reliability model of multiple repairable units within a large fleet may consist of a mixture of different stochastic models, i.e. the HPP/RP, TRP, NHPP and BPP. Therefore, relying only on a single model to represent the behaviour of the whole fleet may not be valid and may lead to wrong parameter estimation.

Turnouts are of the most critical components of railway track which are prone to high static and dynamic forces leading to more intense degradation. They require more inspection than other parts of a railway track as they are potential safety hazards. As a result, turnout degradation processes are crucial to be understood by infrastructure manager to plan for their maintenance and renewal in advance. Two approaches have been introduced in the literature to achieve a thorough understanding of degradation processes in turnouts. The first one acts to develop degradation models based on influential parameters and historical data and then to predict degradation processes in the future; while the second one tries to improve inspection through using new concepts and technologies leading turnout condition data to be better captured over time. The purpose of this paper is to review all available resources regarding these two approaches and provide a guide for further research into turnout studies

The purpose of this paper is to develop a practical economic replacement decision model to identify the economic lifetime of a mining drilling machine. A data driven optimisation model was developed for operating and maintenance costs, purchase price and machine resale value. Equivalent present value of these costs by using discount rate was considered. The proposed model shows that the absolute optimal replacement time (ORT) of a drilling machine used in one underground mine in Sweden is 115 months. Sensitivity and regression analysis show that the maintenance cost has the largest impact on the ORT of this machine. The proposed decision making model is applicable and useful and can be implemented within the mining industry.

Most businesses view maintenance as tasks carried out by technicians and the data collected is mostly cost related. There is a growing trend towards outsourcing of maintenance and the data collection issues are not addressed properly in most maintenance service contracts. Effective maintenance management requires proper data management - data collection and analysis for decision-making. This requires a proper framework and when maintenance is outsourced it raises several issues and challenges. The paper develops a framework for data management when maintenance is outsourced and looks at a real case study that highlights the need for proper data management.

This paper investigates reliability analysis of a repairable unit at fleet level. In fleet, multiple similar systems are running in different operating environments. In fact, due to the highly censored data as well as high variations in failure rate within the fleet, merging the datasets becomes a challenge. The non-parametric analysis is used to capture the failure trend of repairable units at fleet level. Consequently, fleet data have been aggregated and the actual number of failure has been compared with the expected total number of failures. In addition, parametric models are used to model the reliability trend obtained through a non-parametric approach to identify the reliability parameters at fleet level. Real data are used to demonstrate the applicability and validity of the proposed method. Results shows the accuracy of log-linear process is reasonably acceptable.

Purpose - This paper presents a practical model to determine the economic replacement time (ERT) of production machines. The objective is to minimise the total cost of capital equipment, where total cost includes acquisition, operating, maintenance costs and costs related to the machine’s downtime. The costs related to the machine’s downtime are represented by the costs of using a redundant machine. Design/methodology/approach - Four years of cost data are collected. Data is analysed, practical optimisation model is developed and regression analysis is done to estimate the drilling rigs ERT. The artificial neural network (ANN) technique is used to identify the effect of factors influencing the ERT of the drilling rigs.Findings - The results show that the redundant rig cost has the largest impact on ERT, followed by acquisition, maintenance and operating costs. The study also finds that increasing redundant costs per hour have a negative effect on ERT, while decreases in other costs have a positive effect. Regression analysis shows a linear relationship between the cost factors and ERT. Practical implications - The proposed approach can be used by the decision maker in determining the economic replacement time of production machines which used in mining industry.Originality/value - The research proposed in this paper provides and develops an optimisation model for economic replacement time of mining machines. This research also identifies and explains the factors that have the largest impact on the production machine’s ERT. This model for estimating the ERT has never been studied on mining drilling rigs.Keywords Decision support model, Life cycle cost, Optimisation, Replacement timePaper type Research paper