A distributed architecture to implement a prognostic function for complex systems

The proactivity in maintenance management is improved by the implementation of CBM (Condition-Based Maintenance) principles and of PHM (Prognostic and Health Management). These implementations use data about the health status of the systems. Among them, prognostic data make it possible to evaluate the future health of the systems. The Remaining Useful Lifetimes (RULs) of the components is frequently required to prognose systems. However, the availability of complex systems for productive tasks is often expressed in terms of RULs of functions and/or subsystems; those RULs have to bring information about the components. Indeed, the maintenance operators must know what components need maintenance actions in order to increase the RULs of the functions or subsystems, and consequently the availability of the complex systems for longer tasks or more productive tasks. This paper aims at defining a generic prognostic function of complex systems aiming at prognosing its functions and at enabling the isolation of components that needs maintenance actions. The proposed function requires knowledge about the system to be prognosed. The corresponding models are detailed. The proposed prognostic function contains graph traversal so its distribution is proposed to speed it up. It is carried out by generic agents

The Signal Data Explorer: A high performance Grid based signal search tool for use in distributed diagnostic applications

We describe a high performance Grid based signal search tool for distributed diagnostic applications developed in conjunction with Rolls-Royce plc for civil aero engine condition monitoring applications. With the introduction of advanced monitoring technology into engineering systems, healthcare, etc., the associated diagnostic processes are increasingly required to handle and consider vast amounts of data. An exemplar of such a diagnosis process was developed during the DAME project, which built a proof of concept demonstrator to assist in the enhanced diagnosis and prognosis of aero-engine conditions. In particular it has shown the utility of an interactive viewing and high performance distributed search tool (the Signal Data Explorer) in the aero-engine diagnostic process. The viewing and search techniques are equally applicable to other domains. The Signal Data Explorer and search services have been demonstrated on the Worldwide Universities Network to search distributed databases of electrocardiograph data

Prognostics: Design, Implementation, and Challenges

Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life (RUL) of a system. It is also a key technology for an integrated vehicle health management (IVHM) system that leads to improved safety and reliability. A vast amount of research has been presented in the literature to develop prognostics models that are able to predict a system’s RUL. These models can be broadly categorised into experience-based models, data-driven models and physics-based models. Therefore, careful consideration needs to be given to selecting which prognostics model to take forward and apply for each real application. Currently, developing reliable prognostics models in real life is challenging for various reasons, such as the design complexity associated with a system, the high uncertainty and its propagation in the degradation, system level prognostics, the evaluation framework and a lack of prognostics standards. This paper is written with the aim to bring forth the challenges and opportunities for developing prognostics models for complex systems and making researchers aware of these challenges and opportunities

A design approach with method and tools to support SMEs in designing and implementing Distributed Renewable Energy (DRE) solutions based on Sustainable Product-Service System (S.PSS).

Nowadays, around 1.2 billion people lack access to electricity. This condition hampers the provision of basic services such as health care and education. In this challenging scenario, Distributed Renewable Energy (DRE), meaning locally-based and small-scale energy systems based on renewable resources (e.g. sunlight and wind), is perceived as a possible solution towards sustainable energy access for All. ‬‬ ‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬ Some authors agree that the diffusion and implementation of DRE solutions can be facilitated if Sustainable Product-Service Systems (S.PSSs) are applied to them, as S.PSSs offer models able to move the focus from product ownership (e.g. solar panel) to the satisfaction of a specific demand (e.g. energy access). In fact, “S.PSSs applied to DRE are able to cut/reduce both the initial investment (e.g. solar panel purchase) and life-cycle costs (e.g. maintenance, repair of solar panel) democratizing the access to energy and energy-related services.” (LeNSes project – EU funded, Edulink II program, 2013-2016). ‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬ ‬‬‬‬ However, designing and implementing S.PSS-applied-to-DRE solutions is still a complex process. ‬‬‬The paper describes the design approach, method and tools used to support local SMEs (as well as NGOs, students, designers, researchers) in the design and implementation of S.PSS-applied-to-DRE solutions, the design process itself and the results achieved. The current generation of local entrepreneurs and designers worldwide need a broad knowledge base and know-how, as well as effective design approaches, methods and tools, in order to play an active role in promoting, designing and implementing S.PSS-applied-to-DRE solutions, and thus foster sustainable energy access for All

Smart Asset Management for Electric Utilities: Big Data and Future

This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises "How to extract information from large chunk of data?" The concept of "rich data and poor information" is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on Engineering Asset Management (WCEAM) 201