Knowing the maintainability of a component or a system means that repair resource allocations, such as spare part procurement and maintenance training, can be planned and optimized more effectively. Repair data are often collected from multiple and distributed units in different operational conditions, which can introduce heterogeneity into the data. Part of such heterogeneity can be explained and isolated by the observable covariates, whose values and the way that they can affect the item’s maintainability are known. However, some factors which may affect maintainability are typically unknown (unobserved covariates), leading to unobserved heterogeneity. Nevertheless, many researchers have ignored the effect of observed and un-observed covariates, and this may lead to erroneous model selection, as well as wrong conclusions and decisions. Moreover, many authors have simplified their analysis by considering a complex system as a single item. In these studies, the assumption is that all repair data represent an identical repair process for the item. In practice, mechanical systems are composed of multiple parts, with various failure mechanisms, which need different repair processes (repair modes) to return to the operational phase; classical distribution, such as lognormal, which is only a function of time, may not be able to model such complexity. The paper utilizes the mixture frailty model (MFM) in the presence of some specific observed or unobserved covariates to predict maintainability more precisely. MFMs can model the effect of observed and unobserved covariates, as well as identifying different repair processes in the repair dataset. The application of the proposed model is demonstrated by a case study.

Underground pipelines are an essential part of the transportation infrastructure. The structural deterioration of pipelines crossing railways and their subsequent failures are critical for society and industry resulting in direct and indirect costs for all the related stakeholders. Pipeline failures are complex processes, which are affected by many factors, both static (e.g., pipe material, size, age, and soil type) and dynamic (e.g., traffic load, pressure zone changes, and environmental impacts). These failures have serious impacts on public due to safety, disruption of traffic, inconvenience to society, environmental impacts and shortage of resources. Therefore, continuous and accurate condition assessment is critical for the effective management and maintenance of pipeline networks within transportation infrastructure. The aim of this study is to identify failure modes and consequences related to the crossing of pipelines in railway corridors. Expert opinion have been collected through two set of questionnaires which have been distributed to the 291 municipalities in the whole Sweden. The failure analysis revealed that pipe deformation has higher impact followed by pipe rupture at cross-section with railway infrastructure. For underground pipeline under railway infrastructure, aging and external load gets higher ranks among different potential failure causes to the pipeline.

An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications.

Automotive manufacturing industries are required to improve their productivity with higher production rates at the lowest cost, less number of unexpected shutdowns, and reliable operation. In order to achieve the above objectives, the application of reliability, availability, and maintainability methodologies can constitute for resilient operation, identifying the bottlenecks of manufacturing process and optimization of maintenance actions. In this article, we propose a framework for reliability, availability, and maintainability evaluation and maintenance optimization to improve the performance of conveying process of vehicle body in an automotive assembly line. The results of reliability, availability, and maintainability analysis showed that the reliability and maintainability of forklift and loading equipment are the main bottlenecks. To find the optimal maintenance intervals of each unit, a multi-attribute utility theory is applied for multi-criteria decision model considering reliability, availability, and costs. Due to the series configuration of conveying process in automotive assembly line, the optimized time intervals are obtained using opportunistic maintenance strategy. The results could be useful to improve operational performance and sustainability of the production process.

Underground pipelines are an essential part of the transportation infrastructure. The structural deterioration of pipelines crossing railways and their subsequent failures can entail critical consequences for society and industry, resulting in direct and indirect costs for all the stakeholders involved. Therefore, continuous and accurate condition assessment is critical for the effective management and maintenance of pipeline networks within the transportation infrastructure. The aim of this study has been to identify failure modes and consequences related to pipelines crossing railway corridors. Expert opinions have been collected through interviews and two sets of questionnaires have been distributed to the 291 municipalities in Sweden, with 137 responses in total. The failure analysis has revealed that pipe deformation has the highest impact, followed by pipe rupture at locations where pipelines cross railway infrastructure. For underground pipelines under railway infrastructure, ageing and the external load were awarded a higher ranking than other potential causes of pipeline failure.

Authors gratefully acknowledge the funding provided by Sweden’sinnovation agency, Vinnova, through the strategic innovation programmeInfraSweden2030. The funding was granted in a competitiveapplication process that assessed replies to an open call for proposalsconcerning “Condition Assessment and Maintenance of TransportInfrastructure (Grant No. 2016-033113)”.

Authors gratefully acknowledge the technical support and collaboration(In-kind support) of Arrsleff R€orteknik at Sweden, Luleå RailwayResearch Center (JVTC), Stormwater&Sewers and the SwedishTransport Administration (Trafikverket). In addition, the authors arethankful to the anonymous referees for their constructive commentsand Dr Matthias Asplund and Dr Masoud Naseri for their support andsuggestions.

Life-cycle cost (LCC) analysis is an assessment technique used to evaluate costs incurred during the life-cycle of a system to help in long term decision making. In railway and road transport infrastructures, costs are subject to numerous uncertainties associated to the operation and maintenance phase. By integrating in the LCC the stochastic nature of failure using Reliability, Maintainability, Availability and Safety (RAMS) analyses, maintenance costs can be more reliably estimated. This paper presents an innovative approach for a combined RAMS&LCC methodology for linear transport infrastructures which has been developed under the H2020 project INFRALERT. Results of the application of such methodology in two real use cases are shown, one for rail and another one for road. The use cases show how the approach is implemented in practice.

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.

Increasing demand for quality and reliability of the asset is progressively seen as a motivation for improved maintenance procedure and management. Always the role of qualitative maintenance data is neglected in the maintenance recovery level identification. Human factor parameter in the maintenance and qualitative technical data, for instance, maintenance experience, maintenance knowledge, training, quality before maintenance, number of previous maintenance, maintenance documentation and environmental condition can be collected and evaluated to increase the accuracy of maintenance recovery estimation. This information always expressed linguistically and considering their effect in the recovery model is challenging. The aim of this study is to propose a symbolic model to capture the effect of above qualitative factor on maintenance recovery level. Fuzzy inference systems are applied to qualitative expert knowledge to extract the percentage effect which can be incorporated in the recovery level model. The tamping railway case study is considered to validate the model. The results show that the maintenance experience and environmental condition are playing main role in maintenance quality. The application of above method can be extended to asset condition assessment in combination with data driven and physical model

The rolling stock health condition is important for both passenger and freight trains in terms of safety, availability, punctuality and efficiency. Various inspection and maintenance methodologies are per-formed on rolling stock equipment to fulfill the above performance measures. This paper suggests a new approach, namely, intelligent functional test (IFTest) to estimate the remaining useful life (RUL) of the equipment, sub-systems and systems of rolling stock dynamically by data driven methods. IFTest generates a baseline of the current operational abilities in contrast to the required abilities. The test integrates the historical and new set of data to track the trend of degradation of equipment. With this approach, the operation and maintenance personnel have ample time to make decisions for the maintenance and failure consequences. In addition, it is supposed that by using such data we are achieving a more accurate result for the estimation of reliability and RUL of critical rolling stock equipment.

The basic function of electricity distribution system is to supply electrical energy as economically as possible to customer in different geographic with an accept-able level of reliability and quality [1]. The quality of electricity means the supplied current and voltage waveforms are essentially pure sinusoidal waveforms. By incorporating reliability and maintainability con-siderations in the system design and in the planning of system expansion, operation and maintenance the both quality and safety can be improved [2]. To obtain useful results from system reliability assessments, accurate values of component reliability parameters need to be estimated. If the outage database are collected properly, they can reflecting the conditions in which a component works. Hence, collecting, extraction and exploitation useful information from historical data getting more and more importance. A statistical significance of an outage database depends on the quality and reliability as well as number of records data in the database. Such database would describe the real conditions of network equipment more accurately. This study will exploit unplanned outages in electrical components of low voltage (LV) distribution systems in North Khorasan Electricity Distribution Company (NKEDC). The database containing 64035 outages and 150569 Kwh undistributed energy and total time of 2637430 minutes of outages in LV components from July 2010 to February 2016. Here, the main aim of the analysis is to identify the “Significant Few” failures as well as the current maintainability performance of each identified failure models in the power distribution. Such analysis will give a picture of current situation of the system. To start the analysis two meeting have been conducted with the expertise of company to identified the main failure model in the system. Thereafter, the collect data have been explored. The collected data are very detailed regarding the repair time. However, the recorded failure model and preliminary failure cause are not well collected in database. Moreover, we noticed that cause of the failure are not recorded using appropriate coding system that make extracting the data very difficult. Hence, at the first step appropriate coding system have been developed. In this stage, the required recommendation for improving the data collection have been provide and discussed with the company. Based on failure mechanism, of equipment and expert opinions 23 failure mode have been identified and then maintainability for these identified failure mode have been calculated. The trend test and the serial correlation test should all collected repair data are iid distributed and lognormal distribution is suitable tools to model the most identified failure modes