Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for continuous operations in machines which can also improve productivity and enhance system safety. In condition-based maintenance (CBM), effective diagnostics and prognostics are important aspects of CBM which provide sufficient time for maintenance engineers to schedule a repair and acquire replacement components before the components finally fail. All machine components have certain characteristics of failure patterns and are subjected to degradation processes in real environments. This paper describes a technique for accurate assessment of the remnant life of machines based on prior expert knowledge embedded in closed loop prognostics systems. The technique uses Support Vector Machines (SVM) for classification of faults and evaluation of health for six stages of bearing degradation. To validate the feasibility of the proposed model, several fault historical data from High Pressure Liquefied Natural Gas (LNG) pumps were analysed to obtain their failure patterns. The results obtained were very encouraging and the prediction closely matched the real life particularly at the end of term of the bearings

Estimating development effort in free/open source software projects by mining software repositories: A case study of OpenStack

Because of the distributed and collaborative nature of free/open source software (FOSS) projects, the development effort invested in a project is usually unknown, even after the software has been released. However, this information is becoming of major interest, especially-but not only-because of the growth in the number of companies for which FOSS has become relevant for their business strategy. In this paper we present a novel approach to estimate effort by considering data from source code management repositories. We apply our model to the OpenStack project, a FOSS project with more than 1,000 authors, in which several tens of companies cooperate. Based on data from its repositories and together with the input from a survey answered by more than 100 developers, we show that the model offers a simple, but sound way of obtaining software development estimations with bounded margins of error.Gregorio Robles, Carlos Cervig on and Jes us M. Gonz alez-Barahona, project SobreSale (TIN2011-28110). and The work of Daniel Izquierdo has been funded in part by the Torres Quevedo program (PTQ-12-05577

Review of current practices in recording road traffic incident data: with specific reference to spatial analysis and road policing policy

Road safety involves three major components: the road system, the human factor and the vehicle element. These three elements are inter-linked through geo-referenced traffic events and provide the basis for road safety analyses and attempts to reduce the number of road traffic incidents and improve road safety. Although numbers of deaths and serious injuries are back to approximately the 1950s levels when there were many fewer vehicles on the road, there are still over 100 fatalities or serious injuries every day, and this is a considerable waste of human capital. It is widely acknowledged that the location perspective is the most suitable methodology by which to analyse different traffic events, where by in this paper, I will concentrating on the relationship between road traffic incidents and traffic policing. Other methods include studying road and vehicle engineering and these will be discussed later. It is worth noting here that there is some division within the literature concerning the definitions of ‘accident’ and ‘incident’. In this paper I will use ‘incident’ because it is important to acknowledge a vast majority of ‘road accidents’ are in fact crimes. However I will use the term ‘accident’ where it is referred to in the literature or relevant reports. It is important to mention here that a road traffic accident can be defined as ‘the product of an unwelcome interaction between two or more moving objects, or a fixed and moving object’ (Whitelegg 1986). Road safety and road incident reduction relates to many other fields of activity including education, driver training, publicity campaigns, police enforcement, road traffic policing, the court system, the National Health Service and Vehicle engineering. Although the subject of using GIS to analyse road traffic incidents has not received much academic attention, it lies in the field of crime mapping which is becoming increasingly important. It is clear that studies have been attempted to analyse road traffic incidents using GIS are increasingly sophisticated in terms of hypotheses and statistical technique (for example see Austin, Tight and Kirby 1997). However it is also clear that there is considerable blurring of boundaries and the analysis of road accidents sits uncomfortably in crime mapping. This is due to four main reasons: - Road traffic incidents are associated with road engineering, which is concerned with generic solutions while road traffic analysis is about sensitivity to particular contexts. - Not all road traffic incidents are crimes - It is not just the police who have an interest in reducing road traffic incidents, other partners include local authorities, hospitals and vehicle manufacturers - The management of road traffic incidents is not just confined to the police GIS has been used for over thirty years however it has only been recently been used in the field of transportation. The field of transportation has come to embrace Geographical Information Systems as a keytechnology to support its research and operational need. The acronym GIS-T is often employed to refer to the application and adaptation of GIS to research, planning and management in transportation. GIS-T covers a broad arena of disciplines of which road traffic incident detection is just one theme. Others include in vehicle navigation systems. Initially it was only used to ask simple accident enquiries such as depicting the relative incidence of accidents in wet weather or when there is no street lighting, or to flag high absolute or relative incidences of accidents (see Anderson 2002). Recently however there has been increased acknowledgement that there is a requirement to go beyond these simple questions and to extend the analyses. It has been widely claimed by academics and the police alike that knowing where road accidents occur must lead to better road policing, in order to ensure that road policing becomes better integrated with other policing activities. This paper will be used to explore issues surrounding the analysis of road traffic accidents and how GIS analysts, police and policy makers can achieve a better understanding of road traffic incidents and how to reduce them. For the purpose of this study I will be trying to achieve a broader overview of the aspects concerning road accident analysis with a strong emphasis on data quality and accuracy with concern to GIS analysis. Data quality and accuracy are seen as playing a pivotal role in the road traffic management agenda because they assist the police and Local Authorities as to the specific location whereby management can be undertaken. Part one will consider the introduction to road incidents and their relationship with geography and spatial analysis and how this were initially applied to locating ‘hotspots’ and the more recent theory of ‘accident migration’. Part two will address current data issues of the UK collection procedure. This section will pay particular reference to geo-referencing and the implication of data quality on the procedure of analysing road incidents using GIS. Part three addresses issues surrounding the spatial analysis of road traffic incidents, including some techniques such as spatial autocorrelation, time-space geography and the modifiable area unit problem. Finally part four looks at the role of effective road traffic policing and how this can be achieved due to better understanding of the theory and issues arising from analysing road traffic incidents. It will also look at the diffusion and use of GIS within the police and local authorities

Classification of Human Ventricular Arrhythmia in High Dimensional Representation Spaces

We studied classification of human ECGs labelled as normal sinus rhythm, ventricular fibrillation and ventricular tachycardia by means of support vector machines in different representation spaces, using different observation lengths. ECG waveform segments of duration 0.5-4 s, their Fourier magnitude spectra, and lower dimensional projections of Fourier magnitude spectra were used for classification. All considered representations were of much higher dimension than in published studies. Classification accuracy improved with segment duration up to 2 s, with 4 s providing little improvement. We found that it is possible to discriminate between ventricular tachycardia and ventricular fibrillation by the present approach with much shorter runs of ECG (2 s, minimum 86% sensitivity per class) than previously imagined. Ensembles of classifiers acting on 1 s segments taken over 5 s observation windows gave best results, with sensitivities of detection for all classes exceeding 93%.Comment: 9 pages, 2 tables, 5 figure

Embedded Model Control calls for disturbance modeling and rejection

Robust control design is mainly devoted to guaranteeing the closed-loop stability of a model-based control law in the presence of parametric uncertainties. The control law is usually a static feedback law which is derived from a (nonlinear) model using different methodologies. From this standpoint, stability can only be guaranteed by introducing some ignorance coefficients and restricting the feedback control effort with respect to the model-based design. Embedded Model Control shows that, the model-based control law must and can be kept intact in the case of uncertainty, if, under certain conditions, the controllable dynamics is complemented by suitable disturbance dynamics capable of real-time encoding the different uncertainties affecting the ‘embedded model', i.e. the model which is both the design source and the core of the control unit. To be real-time updated the disturbance state is driven by an unpredictable input vector, the noise, which can only be estimated from the model error. The uncertainty-based (or plant-based) design concerns the noise estimator, so as to prevent the model error from conveying uncertainty components (parametric, cross-coupling, neglected dynamics) which are command-dependent and thus prone to destabilizing the controlled plant, into the embedded model. Separation of the components in the low and high frequency domain by the noise estimator itself allows stability recovery and guarantee, and the rejection of low frequency uncertainty components. Two simple case studies endowed with simulated and experimental runs will help to understand the key assets of the methodolog