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.

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.

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.

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

Life-cycle cost (LCC) analysis is an economic technique used to assess the totalcosts associated with the lifetime of a system in order to support decision making in long termstrategic planning. For complex systems, such as railway and road infrastructures, the cost ofmaintenance plays an important role in the LCC analysis. Costs associated with maintenanceinterventions can be more reliably estimated by integrating the probabilistic nature of thefailures associated to these interventions in the LCC models. Reliability, Maintainability,Availability and Safety (RAMS) parameters describe the maintenance needs of an asset in aquantitative way by using probabilistic information extracted from registered maintenanceactivities. Therefore, the integration of RAMS in the LCC analysis allows obtaining reliablepredictions of system maintenance costs and the dependencies of these costs with specific costdrivers through sensitivity analyses. This paper presents an innovative approach for acombined RAMS & LCC methodology for railway and road transport infrastructures beingdeveloped under the on-going H2020 project INFRALERT. Such RAMS & LCC analysisprovides relevant probabilistic information to be used for condition and risk-based planning ofmaintenance activities as well as for decision support in long term strategic investmentplanning.

In this paper, optimal reduction and redundancy methods for reliability system improvement have been proposed. Multilevel redundancy allocation problem (MLRAP) based on hot and cold redundancies have been considered. For various reasons, for instance, space limitation, high cost and so on, redundancy method cannot always be applied to improve system reliability. Hence the concept of equivalence has been presented to choice a more suitable method. In these conditions, optimal reduction method has been applied instead of optimal redundancy method. So that we decide what appropriate degree to decrease the failure rate is. To solve this problem, the value of optimal reliability equivalence factor is obtained by equating two obtained reliability functions based on optimal redundancy and reduction methods. Result shows that equivalence value maximizes the efficiency of the system performance in reduction method instead of redundancy method. Finally, the numerical examples illustrate the results obtained theoretically.

Fault detection in the software of control systems is a difficult task. In largely interconnected systems, not only the individual performance of one channel, but also its interaction with other components, must be considered. In this conceptual paper, we outline a new maintenance concept for the detection of software faults in control systems. The concept includes two approaches, morning gymnastics test and envelope analysis. The morning gymnastics test generates data for a baseline of the current operational abilities in contrast to the specified abilities and should be applied when feasible in continuous production systems. The test integrates historical and new sets of data to track degradation trends. Envelope analysis can be performed to detect operational anomalies and is based on subsequent deep analysis to distinguish software and hardware faults from each other. By using the envelope analysis it is possible to identify failures and disturbances affecting the control system. Thus, the proposed maintenance concept may facilitate detection and identification of potential failures in critical automated system.