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

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

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

A classical description of subnanometer resolution by atomic features in metallic structures

Recent experiments have evidenced sub-nanometer resolution in plasmonic-enhanced probe spectroscopy. Such a high resolution cannot be simply explained using the commonly considered radii of metallic nanoparticles on plasmonic probes. In this contribution the effects of defects as small as a single atom found on spherical plasmonic particles acting as probing tips are investigated in connection with the spatial resolution provided. The presence of abundant edge and corner sites with atomic scale dimensions in crystalline metallic nanoparticles is evident from transmission electron microscopy (TEM) images. Electrodynamic calculations based on the Finite Element Method (FEM) are implemented to reveal the impact of the presence of such atomic features in probing tips on the lateral spatial resolution and field localization. Our analysis is developed for three different configurations, and under resonant and non-resonant illumination conditions, respectively. Based on this analysis, the limits of field enhancement, lateral resolution and field confinement in plasmon-enhanced spectroscopy and microscopy are inferred, reaching values below 1 nanometer for reasonable atomic sizes

Improved power transformer condition monitoring under uncertainty through soft computing and probabilistic health index

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The health index (HI) formulation is a pragmatic approach to combine multiple information sources and generate a consistent health state indicator for asset management planning. Generally, existing transformer HI methods are based on expert knowledge or data-driven models of specific transformer subsystems. However, the effect of uncertainty is not considered when integrating expert knowledge and data-driven models for the system-levelHI estimation. With the increased dynamic and non-deterministic engineering problems, the sources of uncertainty are increasing across power and energy applications, e.g. electric vehicles with new dynamic loads or nuclear power plants with de-energized periods, and transformer health assessment under uncertainty is becoming critical for accurate condition monitoring. In this context, this paper presents a novel soft computing driven probabilistic HI framework for transformer health monitoring. The approach encapsulates data analytics and expert knowledge along with different sources of uncertainty and infers a transformer HI value with confidence intervals for decision-making under uncertainty. Using real data from a nuclear power plant, the proposed framework is compared with traditional HI implementations and results confirm the validity of the approach for transformer health assessment

Sub-wavelength surface IR imaging of soft-condensed matter

Outlined here is a technique for sub-wavelength infrared surface imaging performed using a phase matched optical parametric oscillator laser and an atomic force microscope as the detection mechanism. The technique uses a novel surface excitation illumination approach to perform simultaneously chemical mapping and AFM topography imaging with an image resolution of 200 nm. This method was demonstrated by imaging polystyrene micro-structures

Constitutional mismatch repair deficiency (CMMRD) presenting with high-grade glioma, multiple developmental venous anomalies and malformations of cortical development-a multidisciplinary/multicentre approach and neuroimaging clues to clinching the diagnosis

Constitutional mismatch repair deficiency syndrome (CMMRD) is a rare cancer-predisposition syndrome associated with a high risk of developing a spectrum of malignancies in childhood and adolescence, including brain tumours. In this report, we present the case of an 8-year-old boy with acute headache, vomiting and an episode of unconsciousness in whom brain imaging revealed a high-grade glioma (HGG). The possibility of an underlying diagnosis of CMMRD was suspected radiologically on the basis of additional neuroimaging findings, specifically the presence of multiple supratentorial and infratentorial developmental venous anomalies (DVAs) and malformations of cortical development (MCD), namely, heterotopic grey matter. The tumour was debulked and confirmed to be a HGG on histopathology. The suspected diagnosis of CMMRD was confirmed on immunohistochemistry and genetic testing which revealed mutations in PMS2 and MSH6. The combination of a HGG, multiple DVAs and MCD in a paediatric or young adult patient should prompt the neuroradiologist to suggest an underlying diagnosis of CMMRD. A diagnosis of CMMRD has an important treatment and surveillance implications not only for the child but also the family in terms of genetic counselling

Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing

Accurate diagnosis of power transformers is critical for the reliable and cost-effective operation of the power grid. Presently there are a range of methods and analytical models for transformer fault diagnosis based on dissolved gas analysis. However, these methods give conflicting results and they are not able to generate uncertainty information associated with the diagnostics outcome. In this situation it is not always clear which model is the most accurate. This paper presents a novel multiclass probabilistic diagnosis framework for dissolved gas analysis based on Bayesian networks and hypothesis testing. Bayesian network models embed expert knowledge, learn patterns from data and infer the uncertainty associated with the diagnostics outcome, and hypothesis testing aids in the data selection process. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset and is shown to have a maximum diagnosis accuracy of 88.9%

Improving the accuracy of transformer DGA diagnosis in the presence of conflicting evidence

Transformers are critical assets for the reliable and cost-effective operation of the power grid. Transformers may fail if condition monitoring does not identify degraded conditions in time. Dissolved Gas Analysis (DGA) focuses on the examination of the dissolved gasses in the transformer oil and there exist different methods for transformer fault diagnosis based on different analyses of the gassing levels. However, these methods can give conflicting results, and it is not always clear which model is most accurate in a given situation. This paper presents a novel evidence combination framework for DGA based on Bayesian networks. Bayesian network models embed expert knowledge along with learned data patterns and evidence combination which aids in the consistency of analysis. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset with a maximum diagnosis accuracy of 88.3%