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

On the use of probabilistic model-checking for the verification of prognostics applications

Prognostics aims to improve asset availability through intelligent maintenance actions. Up-to-date remaining useful life predictions enable the optimization of maintenance planning. Verification of prognostics techniques aims to analyze if the prognostics application meets the design requirements. Online prognostics applications depend on the data-gathering hardware architecture to perform correct prognostics predictions. Accordingly, when verifying prognostics requirements compliance, it is necessary to include the effect of hardware failures on prognostics predictions. In this paper we investigate the use of formal verification techniques for the integrated verification of prognostics applications including hardware and software components. Focusing on the probabilistic model-checking approach, a case study from the power industry shows the validity of the proposed framework

ADEPS: a methodology for designing prognostic applications

Prognostics applications predict the future evolution of an asset under study, by diagnosing the actual health state and modeling the future degradation. Due to rapidly growing interest in prognostics, different prediction techniques have been developed independently without a consistent and systematic design. In this paper we formalize the prognostics design process with a novel methodology entitled ADEPS (Assisted Design for Engineering Prognostic Systems). ADEPS combines prognostics concepts with model-based safety assessment, criticality analysis, knowledge engineering and formal verification approaches. The main activities of ADEPS include synthesis of the safety assessment model from the design model, prioritization of the system failure modes, systematic prognostics model selection and verification of the adequacy of the prognostics results with respect to design requirements. By linking system-level safety assessment models and prognostics results, design and safety models are updated with online information about different failure modes. This step enables system-level health assessment including prognostics predictions of different failure modes. The end-to-end application of the methodology for the design and evaluation of a power transformer demonstrates the benefits of the proposed approach including reduced design time and effort, complete consideration of prognostics algorithms and updated system-level health assessment

Detection of deep-subwavelength dielectric layers at terahertz frequencies using semiconductor plasmonic resonators

Plasmonic bowtie antennas made of doped silicon can operate as plasmonic resonators at terahertz (THz) frequencies and provide large field enhancement close to their gap. We demonstrate both experimentally and theoretically that the field confinement close to the surface of the antenna enables the detection of ultrathin (100 nm) inorganic films, about 3750 times thinner than the free space wavelength. Based on model calculations, we conclude that the detection sensitivity and its variation with the thickness of the deposited layer are related to both the decay of the local THz field profile around the antenna and the local field enhancement in the gap of the bowtie antenna. This large field enhancement has the potential to improve the detection limits of plasmon-based biological and chemical sensors

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

Introducing Axial Chirality into Mesoionic 4,4′-Bis(1,2,3-triazole) Dicarbenes

Mesoionic 4,4′-bis(1,2,3-triazole-5,5′-diylidene) Rh(I) complexes having a C2 chiral 4,4′-axis were accessed from 3-alkyltriazolium salts in virtually complete de. Their structure and configurational integrity were assessed by NMR spectroscopy, X-ray crystallography, and chiral HPLC. Computational analysis of the MICs involved in the reaction suggested the formation of a highly stable and unprecedented cation-carbene intermediate species, which could be evidenced experimentally by cyclic voltammetry analysis

Improved dynamic dependability assessment through integration with prognostics

The use of average data for dependability assessments results in a outdated system-level dependability estimation which can lead to incorrect design decisions. With increasing availability of online data, there is room to improve traditional dependability assessment techniques. Namely, prognostics is an emerging field which provides asset-specific failure information which can be reused to improve the system level failure estimation. This paper presents a framework for prognostics-updated dynamic dependability assessment. The dynamic behaviour comes from runtime updated information, asset inter-dependencies, and time-dependent system behaviour. A case study from the power generation industry is analysed and results confirm the validity of the approach for improved near real-time unavailability estimations

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