A data fusion approach of multiple maintenance data sources for real-world reliability modelling

A central tenet in the theory of reliability modelling is the quantification of the probability of asset failure. In general, reliability depends on asset age and the maintenance policy applied. Usually, failure and maintenance times are the primary inputs to reliability models. However, for many organisations, different aspects of these data are often recorded in different databases (e.g. work order notifications, event logs, condition monitoring data, and process control data). These recorded data cannot be interpreted individually, since they typically do not have all the information necessary to ascertain failure and preventive maintenance times. This paper presents a methodology for the extraction of failure and preventive maintenance times using commonly-available, real-world data sources. A text-mining approach is employed to extract keywords indicative of the source of the maintenance event. Using these keywords, a Naïve Bayes classifier is then applied to attribute each machine stoppage to one of two classes: failure or preventive. The accuracy of the algorithm is assessed and the classified failure time data are then presented. The applicability of the methodology is demonstrated on a maintenance data set from an Australian electricity company.

Proceedings of the 2nd Workshop on (Re)Creating Lively Cities Through Ambient Technology in Arts, Culture and Gastronomic Experiences

Digital and interactive technologies are becoming increasingly embedded in everyday lives of people around the world. Application of technologies such as real-time, context-aware, and interactive technologies; augmented and immersive realities; social media; and location-based services has been particularly evident in urban environments where technological and sociocultural infrastructures enable easier deployment and adoption as compared to non-urban areas. There has been growing consumer demand for new forms of experiences and services enabled through these emerging technologies. We call this ambient media, as the media is embedded in the natural human living environment. This workshop focuses on ambient media services, applications, and technologies that promote people’s engagement in creating and recreating liveliness in urban environments, particularly through arts, culture, and gastronomic experiences. The RelCi workshop series is organized in cooperation with the Queensland University of Technology (QUT), in particular the Urban Informatics Lab and the Tampere University of Technology (TUT), in particular the Entertainment and Media Management (EMMi) Lab. The workshop runs under the umbrella of the International Ambient Media Association (AMEA) (http://www.ambientmediaassociation.org), which is hosting the international open access journal entitled “International Journal on Information Systems and Management in Creative eMedia”, and the international open access series “International Series on Information Systems and Management in Creative eMedia” (see http://www.tut.fi/emmi/Journal). The RelCi workshop took place for the first time in 2012 in conjunction with ICME 2012 in Melbourne, Autralia; and this year’s edition took place in conjunction with INTERACT 2013 in Cape Town, South Africa. Besides, the International Ambient Media Association (AMEA) organizes the Semantic Ambient Media (SAME) workshop series, which took place in 2008 in conjunction with ACM Multimedia 2008 in Vancouver, Canada; in 2009 in conjunction with AmI 2009 in Salzburg, Austria; in 2010 in conjunction with AmI 2010 in Malaga, Spain; in 2011 in conjunction with Communities and Technologies 2011 in Brisbane, Australia; in 2012 in conjunction with Pervasive 2012 in Newcastle, UK; and in 2013 in conjunction with C&T 2013 in Munich, Germany.

Comparison of the sensitivity of different sensor technologies to imbalance severity in low speed wind turbines

Imbalance is not only a direct major cause of downtime in wind turbines, but also accelerates the degradation of neighbouring and downstream components (e.g. main bearing, generator). Along with detection, the imbalance quantification is also essential as some residual imbalance always exist even in a healthy turbine. Three different commonly used sensor technologies (vibration, acoustic emission and electrical measurements) are investigated in this work to verify their sensitivity to different imbalance grades. This study is based on data obtained by experimental tests performed on a small scale wind turbine drive train test-rig for different shaft speeds and imbalance levels. According to the analysis results, electrical measurements seem to be the most suitable for tracking the development of imbalance.