Linking component importance to optimisation of preventive maintenance policy

In reliability engineering, time on performing preventive maintenance (PM) on a component in a system may affect system availability if system operation needs stopping for PM. To avoid such an availability reduction, one may adopt the following method: if a component fails, PM is carried out on a number of the other components while the failed component is being repaired. This ensures PM does not take system’s operating time. However, this raises a question: Which components should be selected for PM? This paper introduces an importance measure, called Component Maintenance Priority (CMP), which is used to select components for PM. The paper then compares the CMP with other importance measures and studies the properties of the CMP. Numerical examples are given to show the validity of the CMP

Tone from the Top in Risk Management: A Complementarity Perspective on How Control Systems Influence Risk Awareness

Prompted by the weaknesses of standardized risk management approaches in the aftermath of the 2008 financial crisis, scholars, regulators, and practitioners alike emphasize the importance of creating a risk-aware culture in organizations. Recent insights highlight the special role of tone from the top as crucial driver of risk awareness. In this study, we take a systems-perspective on control system design to investigate the role of tone from the top in creating risk awareness. In particular, we argue that both interactive and diagnostic use of budgets and performance measures interact with tone from the top in managing risk awareness. Our results show that interactive control strengthens the effect of tone from the top on risk awareness, while tone from the top and diagnostic control are, on average, not interrelated with regard to creating risk awareness. To shed light on the boundary conditions of the proposed interdependencies, we further investigate whether the predicted interdependencies are sensitive to the level of perceived environmental uncertainty. We find that the effect of tone from the top and interactive control becomes significantly stronger in a situation of high perceived environmental uncertainty. Most interestingly, tone from the top and diagnostic control are complements with regard to risk awareness in settings of low perceived environmental uncertainty and substitutes at high levels of perceived environmental uncertainty.Series: Department of Strategy and Innovation Working Paper Serie

Cross-layer optimization of unequal protected layered video over hierarchical modulation

Abstract-unequal protection mechanisms have been proposed at several layers in order to improve the reliability of multimedia contents, especially for video data. The paper aims at implementing a multi-layer unequal protection scheme, which is based on a Physical-Transport-Application cross-layer design. Hierarchical modulation, in the physical layer, has been demonstrated to increase the overall user capacity of a wireless communications. On the other hand, unequal erasure protection codes at the transport layer turned out to be an efficient method to protect video data generated by the application layer by exploiting their intrinsic properties. In this paper, the two techniques are jointly optimized in order to enable recovering lost data in case the protection is performed separately. We show that the cross-layer design proposed herein outperforms the performance of hierarchical modulation and unequal erasure codes taken independently

Mechanism Deduction from Noisy Chemical Reaction Networks

We introduce KiNetX, a fully automated meta-algorithm for the kinetic analysis of complex chemical reaction networks derived from semi-accurate but efficient electronic structure calculations. It is designed to (i) accelerate the automated exploration of such networks, and (ii) cope with model-inherent errors in electronic structure calculations on elementary reaction steps. We developed and implemented KiNetX to possess three features. First, KiNetX evaluates the kinetic relevance of every species in a (yet incomplete) reaction network to confine the search for new elementary reaction steps only to those species that are considered possibly relevant. Second, KiNetX identifies and eliminates all kinetically irrelevant species and elementary reactions to reduce a complex network graph to a comprehensible mechanism. Third, KiNetX estimates the sensitivity of species concentrations toward changes in individual rate constants (derived from relative free energies), which allows us to systematically select the most efficient electronic structure model for each elementary reaction given a predefined accuracy. The novelty of KiNetX consists in the rigorous propagation of correlated free-energy uncertainty through all steps of our kinetic analyis. To examine the performance of KiNetX, we developed AutoNetGen. It semirandomly generates chemistry-mimicking reaction networks by encoding chemical logic into their underlying graph structure. AutoNetGen allows us to consider a vast number of distinct chemistry-like scenarios and, hence, to discuss assess the importance of rigorous uncertainty propagation in a statistical context. Our results reveal that KiNetX reliably supports the deduction of product ratios, dominant reaction pathways, and possibly other network properties from semi-accurate electronic structure data.Comment: 36 pages, 4 figures, 2 table

Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification

Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e.g., cables and related insulation, transformers, breakers and so on). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid itself are collected such as meteorological information. Designing a suitable recognition (discrimination) model of faults in a real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. The second point is that, for synthesizing a recognition model, in practice only the conditions of observed faults are usually meaningful. Therefore, a suitable recognition model should be synthesized by making use of the observed fault conditions only. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of multiple dissimilarity measures customization and one-class classification techniques. We provide here an in-depth study related to the available data and to the models synthesized by the proposed one-class classifier. We offer also a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based reliability decision rule