Proposing a streaming Big Data analytics (SBDA) platform for condition based maintenance (CBM) and monitoring transportation systems

Statistics demonstrate that public transportation plays a significant role in people’s movement in metropolises. However, transit systems are aging and are facing rising maintenance costs. Technologies such as Condition-Based Maintenance (CBM) could be used in order to monitor performance conditions of transportation and industrial assets in real-time to detect when and what maintenance is required. CBMs could help to identify risk scenarios in real-time, enhance reliability, reduce call out costs, increase productivity, and better asset functioning visibility. Since the high volume of maintenance data is generated from the different source, managing assets conditions with traditional inspection system such as planned maintenance (PM) is impossible. Therefore, providing a comprehensive performance management program is essential. My research is motivated by interesting challenges increasing from the growing size, variety, and complexity of maintenance data in CBM systems. This paper presents a knowledge-based approach of CBM using streaming big data analysis (SBDA) in order to solve real-time big data management, storage and computation challenges and predictive data analytics in CBM systems. This platform could detect changes in asset’s behavior before they stop

The hierarchy structure in directed and undirected signed networks

The concept of social stratification and hierarchy among human dates is back to the origin of human race. Presently, the growing reputation of social networks has given us with an opportunity to analyze these well-studied phenomena over different networks at different scales. Generally, a social network could be defined as a collection of actors and their interactions. In this work, we concern ourselves with a particular type of social networks, known as trust networks. In this type of networks, there is an explicit show of trust (positive interaction) or distrust (negative interaction) among the actors. In a social network, actors tend to connect with each other on the basis of their perceived social hierarchy. The emergence of such a hierarchy within a social community shows the manner in which authority manifests in the community. In the case of signed networks, the concept of social hierarchy can be interpreted as the emergence of a tree-like structure comprising of actors in a top-down fashion in the order of their ranks, describing a specific parent-child relationship, viz. child trusts parent. However, owing to the presence of positive as well as negative interactions in signed networks, deriving such “trust hierarchies” is a non-trivial challenge. We argue that traditional notions (of unsigned networks) are insufficient to derive hierarchies that are latent within signed networks

Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field

Role of higher twist effects in diffractive DIS and determination of diffractive parton distribution functions

The current analysis aims to present the results of a QCD analysis of diffractive parton distribution functions (PDFs) at next-to-leading-order accuracy in perturbative QCD. In this new determination of diffractive PDFs, we use all available and up-to-date diffractive deep-inelastic scattering (DIS) datasets from H1 and ZEUS collaborations at HERA, including the most recent H1/ZEUS combined measurements. In this analysis, we consider the heavy quark contributions to the diffractive DIS in the so-called framework of the fonll general mass variable flavor number scheme. The uncertainties on the diffractive PDFs are calculated using the standard “Hessian error propagation,” which served to provide a more realistic estimate of the uncertainties. This analysis is enriched, for the first time, by including the nonperturbative higher twist (HT) effects in the calculation of diffractive DIS cross sections, which are particularly important at large −x and low Q2 regions. Then, the stability and reliability of the extracted diffractive PDFs are investigated upon the inclusion of HT effects. We discuss the novel aspects of the approach used in this QCD fit, namely, optimized and flexible parametrizations of diffractive PDFs, the inclusion of HT effects, and considering the recent H1/ZEUS combined dataset. Finally, we present the extracted diffractive PDFs with and without the presence of HT effects and discuss the fit quality and the stability upon variations of the kinematic cuts and the fitted datasets. We show that the inclusion of HT effects in diffractive DIS can improve the description of the data, which leads, in general, to a very good agreement between data and theory predictions

Analyzing large- scale smart card data to investigate public transport travel behaviour using big data analytics

In urban public transport, Smart card data have been used more and more in order to collect fare automatically. They allowed passengers to access almost all type of public transportation system modes (bus, train, tram, funiculars, LRT, metro, and ferryboats) with a single card that is valid for the complete journey. Although Smart card major concentration is in revenue collection, they also generate massive amounts of passive data from the technological devices installed to control the operation of them. Generated data could be beneficial to transit planners which could rise the better understanding of passengers’ behavioral patterns for short and long term service planning. However, one of the major challenges is the fact that traditional infrastructures and methods are inefficient when processing or analyzing a large volume of data. Thus, as an alternative, big data technology could be employed to enhance collecting, storing, processing, and analyzing the data. Moreover, the main motivation would be cost-efficiency of this methodology as the cost of processing and analyzing large-scale data is huge. This experience demonstrates that a combination of planning knowledge, big data, and data mining tool allows to produce travel behaviors indicators, public transport policies, operational performance, and fare policies

A hybrid approach based on numerical, statistical and intelligent techniques for optimization of tube drawing process to produced squared section from round tube

In the tube drawing process, there are a bunch of parameters which play key role in process performance. Thus, finding the optimized parameters is a controversial issue. Current study aimed to produce a squared section of round tube by tube sinking process. To simulate the process finite element method (FEM) was used. Then, to find a meaningful kinship between process input and output parameters the developed FE model was associated with the design of experiment based response surface methodology (RSM). The sufficiency of each model was checked by analysis of variances. Further, the SA (simulated annealing) was associated with RSM models to find the optimal solution regarding maximum thickness distributions and minimum force and dimensional error. Hereafter, for performing accurate optimization, the principal component analysis was used to find the appropriate weight factor of each response. The obtained results were in right agreement with those derived from simulation and confirmatory experiment