On cost-effective reuse of components in the design of complex reconfigurable systems

Design strategies that benefit from the reuse of system components can reduce costs while maintaining or increasing dependability—we use the term dependability to tie together reliability and availability. D3H2 (aDaptive Dependable Design for systems with Homogeneous and Heterogeneous redundancies) is a methodology that supports the design of complex systems with a focus on reconfiguration and component reuse. D3H2 systematizes the identification of heterogeneous redundancies and optimizes the design of fault detection and reconfiguration mechanisms, by enabling the analysis of design alternatives with respect to dependability and cost. In this paper, we extend D3H2 for application to repairable systems. The method is extended with analysis capabilities allowing dependability assessment of complex reconfigurable systems. Analysed scenarios include time-dependencies between failure events and the corresponding reconfiguration actions. We demonstrate how D3H2 can support decisions about fault detection and reconfiguration that seek to improve dependability while reducing costs via application to a realistic railway case study

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

Special Issue on "Strong Coupling of Molecules to Cavities"

This is the author accepted manuscript. The final version is available from American Chemical Society via the DOI in this record

Robust Subnanometric Plasmon Ruler by Rescaling of the Nonlocal Optical Response

We present the optical response of two interacting metallic nanowires calculated for separation distances down to angstrom range. State-of-the-art local and nonlocal approaches are compared with full quantum time-dependent density functional theory calculations that give an exact account of nonlocal and tunneling effects. We find that the quantum results are equivalent to those from classical approaches when the nanoparticle separation is defined as the separation between centroids of the screening charges. This establishes a universal plasmon ruler for subnanometric distances. Such a ruler not only impacts the basis of many applications of plasmonics, but also provides a robust rule for subnanometric metrology

Anomalous spectral shift of near- and far-field plasmonic resonances in nanogaps

This is an open access article published under a Creative Commons Attribution (CC-BY) License.-- et al.The near-field and far-field spectral response of plasmonic systems are often assumed to be identical, due to the lack of methods that can directly compare and correlate both responses under similar environmental conditions. We develop a widely tunable optical technique to probe the near-field resonances within individual plasmonic nanostructures that can be directly compared to the corresponding far-field response. In tightly coupled nanoparticle-on-mirror constructs with nanometer-sized gaps we find >40 meV blue-shifts of the near-field compared to the dark-field scattering peak, which agrees with full electromagnetic simulations. Using a transformation optics approach, we show such shifts arise from the different spectral interference between different gap modes in the near- and far-field. The control and tuning of near-field and far-field responses demonstrated here is of paramount importance in the design of optical nanostructures for field-enhanced spectroscopy, as well as to control near-field activity monitored through the far-field of nano-optical devices.We acknowledge financial support from EPSRC grants EP/G060649/1, EP/L027151/1, EP/G037221/1, EPSRC NanoDTC, and ERC grant LINASS 320503. J.A. acknowledges support from project FIS2013-41184-P from Spanish MINECO and project NANOGUNE’14 from the Department of Industry of the Basque Country. F.B. acknowledges support from the Winton Programme for the Physics of Sustainability. R.C. acknowledges financial support from St. John’s College, Cambridge, for a Dr. Manmohan Singh Scholarship. P.A. acknowledges funding from the Helmholtz Association for the Young Investigator group VH-NG-928 within the Initiative and Networking Fund.Peer Reviewe

Polarization control of metal-enhanced fluorescence in hybrid assemblies of photosynthetic complexes and gold nanorods

Fluorescence imaging of hybrid nanostructures composed of a bacterial light-harvesting complex LH2 and Au nanorods with controlled coupling strength is employed to study the spectral dependence of the plasmon-induced fluorescence enhancement. Perfect matching of the plasmon resonances in the nanorods with the absorption bands of the LH2 complexes facilitates a direct comparison of the enhancement factors for longitudinal and transverse plasmon frequencies of the nanorods. We find that the fluorescence enhancement due to excitation of longitudinal resonance can be up to five-fold stronger than for the transverse one. We attribute this result, which is important for designing plasmonic functional systems, to a very different distribution of the enhancement of the electric field due to the excitation of the two characteristic plasmon modes in nanorods

A cost-benefit approach for the evaluation of prognostics-updated maintenance strategies in complex dynamic systems

The implementation of maintenance strategies which integrate online condition data has the potential to increase availability and reduce maintenance costs. Prognostics techniques enable the implementation of these strategies through up-to-date remaining useful life estimations. However, a cost-benefit assessment is necessary to verify the scale of potential benefits of condition-based maintenance strategies and prognostics for a given application. The majority of prognostics applications focus on the evaluation of a specific failure mode of an asset. However, industrial systems are comprised of different assets with multiple failure modes, which in turn, work in cooperation to perform a system level function. Besides, these systems include time-dependent events and conditional triggering events which cause further effects on the system. In this context not only are the system-level prognostics predictions challenging, but also the cost-benefit analysis of condition-based maintenance policies. In this work we combine asset prognostics predictions with temporal logic so as to obtain an up-to-date system level health estimation. We use asset level and system level prognostics estimations to evaluate the cost-effectiveness of alternative maintenance policies. The application of the proposed approach enables the adoption of conscious trade-off decisions between alternative maintenance strategies for complex systems. The benefits of the proposed approach are discussed with a case study from the power industry

Prognostics and health management oriented data analytics suite for transformer health monitoring

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety, and economic effects. With the advance of machine learning techniques, it is possible to enhance traditional transformer health monitoring techniques with data-driven and expert-based prognostics and health management (PHM) applications. Accordingly, this paper reviews the experience of the authors in the implementation of machine learning methods for transformer condition monitoring

Prognostics and health management oriented data analytics suite for transformer health monitoring

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety, and economic effects. With the advance of machine learning techniques, it is possible to enhance traditional transformer health monitoring techniques with data-driven and expert-based prognostics and health management (PHM) applications. Accordingly, this paper reviews the experience of the authors in the implementation of machine learning methods for transformer condition monitoring