Fault detection in operating helicopter drive train components based on support vector data description

The objective of the paper is to develop a vibration-based automated procedure dealing with early detection of mechanical degradation of helicopter drive train components using Health and Usage Monitoring Systems (HUMS) data. An anomaly-detection method devoted to the quantification of the degree of deviation of the mechanical state of a component from its nominal condition is developed. This method is based on an Anomaly Score (AS) formed by a combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CI), thus the operational variability is implicitly included in the model through the CI correlation. The problem of fault detection is then recast as a one-class classification problem in the space spanned by a set of CI, with the aim of a global differentiation between normal and anomalous observations, respectively related to healthy and supposedly faulty components. In this paper, a procedure based on an efficient one-class classification method that does not require any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows an efficient data description without the need of a significant amount of statistical data. Several analyses have been carried out in order to validate the proposed procedure, using flight vibration data collected from a H135, formerly known as EC135, servicing helicopter, for which micro-pitting damage on a gear was detected by HUMS and assessed through visual inspection. The capability of the proposed approach of providing better trade-off between false alarm rates and missed detection rates with respect to individual CI and to the AS obtained assuming jointly-Gaussian-distributed CI has been also analysed

H∞ and L2–L∞ filtering for two-dimensional linear parameter-varying systems

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Wiley-BlackwellIn this paper, the H∞ and l2–l∞ filtering problem is investigated for two-dimensional (2-D) discrete-time linear parameter-varying (LPV) systems. Based on the well-known Fornasini–Marchesini local state-space (FMLSS) model, the mathematical model of 2-D systems under consideration is established by incorporating the parameter-varying phenomenon. The purpose of the problem addressed is to design full-order H∞ and l2–l∞ filters such that the filtering error dynamics is asymptotic stable and the prescribed noise attenuation levels in H∞ and l2–l∞ senses can be achieved, respectively. Sufficient conditions are derived for existence of such filters in terms of parameterized linear matrix inequalities (PLMIs), and the corresponding filter synthesis problem is then transformed into a convex optimization problem that can be efficiently solved by using standard software packages. A simulation example is exploited to demonstrate the usefulness and effectiveness of the proposed design method

Kalman Filter Bibliography: Agriculture, Biology, and Medicine

27 pages, 1 article*Kalman Filter Bibliography: Agriculture, Biology, and Medicine* (Federer, Walter T.; Murty, B. R.) 27 page

Discrete Time Systems

Discrete-Time Systems comprehend an important and broad research field. The consolidation of digital-based computational means in the present, pushes a technological tool into the field with a tremendous impact in areas like Control, Signal Processing, Communications, System Modelling and related Applications. This book attempts to give a scope in the wide area of Discrete-Time Systems. Their contents are grouped conveniently in sections according to significant areas, namely Filtering, Fixed and Adaptive Control Systems, Stability Problems and Miscellaneous Applications. We think that the contribution of the book enlarges the field of the Discrete-Time Systems with signification in the present state-of-the-art. Despite the vertiginous advance in the field, we also believe that the topics described here allow us also to look through some main tendencies in the next years in the research area

Deep Reinforcement Learning for Distribution Network Operation and Electricity Market

The conventional distribution network and electricity market operation have become challenging under complicated network operating conditions, due to emerging distributed electricity generations, coupled energy networks, and new market behaviours. These challenges include increasing dynamics and stochastics, and vast problem dimensions such as control points, measurements, and multiple objectives, etc. Previously the optimization models were often formulated as conventional programming problems and then solved mathematically, which could now become highly time-consuming or sometimes infeasible. On the other hand, with the recent advancement of artificial intelligence technologies, deep reinforcement learning (DRL) algorithms have demonstrated their excellent performances in various control and optimization fields. This indicates a potential alternative to address these challenges. In this thesis, DRL-based solutions for distribution network operation and electricity market have been investigated and proposed. Firstly, a DRL-based methodology is proposed for Volt/Var Control (VVC) optimization in a large distribution network, to effectively control bus voltages and reduce network power losses. Further, this thesis proposes a multi-agent (MA)DRL-based methodology under a complex regional coordinated VVC framework, and it can address spatial and temporal uncertainties. The DRL algorithm is also improved to adapt to the applications. Then, an integrated energy and heating systems (IEHS) optimization problem is solved by a MADRL-based methodology, where conventionally this could only be solved by simplifications or iterations. Beyond the applications in distribution network operation, a new electricity market service pricing method based on a DRL algorithm is also proposed. This DRL-based method has demonstrated good performance in this virtual storage rental service pricing problem, whereas this bi-level problem could hardly be solved directly due to a non-convex and non-continuous lower-level problem. These proposed methods have demonstrated advantageous performances under comprehensive case studies, and numerical simulation results have validated the effectiveness and high efficiency under different sophisticated operation conditions, solution robustness against temporal and spatial uncertainties, and optimality under large problem dimensions

On Practical machine Learning and Data Analysis

This thesis discusses and addresses some of the difficulties associated with practical machine learning and data analysis. Introducing data driven methods in e.g industrial and business applications can lead to large gains in productivity and efficiency, but the cost and complexity are often overwhelming. Creating machine learning applications in practise often involves a large amount of manual labour, which often needs to be performed by an experienced analyst without significant experience with the application area. We will here discuss some of the hurdles faced in a typical analysis project and suggest measures and methods to simplify the process. One of the most important issues when applying machine learning methods to complex data, such as e.g. industrial applications, is that the processes generating the data are modelled in an appropriate way. Relevant aspects have to be formalised and represented in a way that allow us to perform our calculations in an efficient manner. We present a statistical modelling framework, Hierarchical Graph Mixtures, based on a combination of graphical models and mixture models. It allows us to create consistent, expressive statistical models that simplify the modelling of complex systems. Using a Bayesian approach, we allow for encoding of prior knowledge and make the models applicable in situations when relatively little data are available. Detecting structures in data, such as clusters and dependency structure, is very important both for understanding an application area and for specifying the structure of e.g. a hierarchical graph mixture. We will discuss how this structure can be extracted for sequential data. By using the inherent dependency structure of sequential data we construct an information theoretical measure of correlation that does not suffer from the problems most common correlation measures have with this type of data. In many diagnosis situations it is desirable to perform a classification in an iterative and interactive manner. The matter is often complicated by very limited amounts of knowledge and examples when a new system to be diagnosed is initially brought into use. We describe how to create an incremental classification system based on a statistical model that is trained from empirical data, and show how the limited available background information can still be used initially for a functioning diagnosis system. To minimise the effort with which results are achieved within data analysis projects, we need to address not only the models used, but also the methodology and applications that can help simplify the process. We present a methodology for data preparation and a software library intended for rapid analysis, prototyping, and deployment. Finally, we will study a few example applications, presenting tasks within classification, prediction and anomaly detection. The examples include demand prediction for supply chain management, approximating complex simulators for increased speed in parameter optimisation, and fraud detection and classification within a media-on-demand system

HAZARDS OF EXPROPRIATION:TENURE INSECURITY AND INVESTMENT IN RURAL CHINA

This paper uses household data from Northeast China to examine the link between investment and land tenure insecurity induced by China's system of village-level land reallocation. We quantify expropriation risk using a hazard analysis of individual plot tenures and incorporate the predicted hazards of expropriation into an empirical analysis of plot-level investment. Our focus is on organic fertilizer use, which has long lasting benefits for soil quality. Although we find that higher expropriation risk significantly reduces application of organic fertilizer, a welfare analysis shows that guaranteeing land tenure in this part of China would yield only minimal efficiency gains.Land Economics/Use,

Anterior dental loading and root morphology in Neanderthals

Distinguer les incisives et canines des Néanderthaliens de celles des hommes modernes peut représenter un défi dans le cas de dents isolées trouvées dans des collections de musée, ou provenant de contextes stratigraphiques perturbés. De plus, la morphologie de la couronne ne peut être utilisée dans le cas de dents fortement usées. Une étude préliminaire basée sur des échantillons limités et des mesures linéaires (Bailey, 2005) propose que la longueur des racines dentaires à elle seule permet de discriminer taxonomiquement les Néanderthaliens des hommes modernes du Paléolithique supérieur et actuels. Cette thèse teste cette hypothèse pour un échantillon de Néanderthaliens et d'hommes modernes, plus large géographiquement et chronologiquement, en utilisant la micro-tomographie. En plus de l'intérêt taxonomique d'explorer la taille et la forme des racines, nous discutons les implications fonctionnelles de la morphologie racinaire des dents antérieures dans le contexte de l'hypothèse des " dents-utilisées-comme-des-outils ", et des activités para-masticatrices. La première partie a été publiée comme suit : Le Cabec, A., Kupczik, K., Gunz, P., Braga, J., and Hublin, J.J. (2012). Long Anterior Mandibular Tooth Roots in Neanderthals Are Not the Result of their Large Jaws. Journal of Human Evolution, pp. 63, 667-681. DOI: 10.1016/j. jhevol.2012.07.003. Cette partie valide la longueur des racines dentaires en tant qu'outil taxonomique pour distinguer les Néanderthaliens tardifs des hommes modernes du Paléolithique Supérieur et récents. En dépit de l'absence de corrélation entre la taille des racines et la taille de la symphyse mentonnière, les Néanderthaliens ont de grandes racines, pour la taille de leurs mâchoires. Il est alors proposé que les courtes racines des hommes modernes récents résulteraient d'une allométrie négative. La seconde partie a été publiée comme suit : Le Cabec, A., Gunz, P., Kupczik, K., Braga, J. and Hublin, J.J. (2013). Anterior Tooth Root Morphology and Size in Neanderthals: Taxonomic and Functional Implications. Journal of Human Evolution, 64, pp. 169-193. DOI: 10.1016/j. jhevol.2012.08.011. La morphologie racinaire est étudiée à travers un large échantillon d'hominidés fossiles et actuels, couvrant une large période chronologique et une vaste zone géographique. Les plus grandes longueurs racinaires observées chez les Néanderthaliens peuvent avoir résulté de la rétention d'une condition ancestrale. L'attribution taxonomique débattue de certains spécimens est discutée à la lumière de la morphologie racinaire des dents antérieures et montre que la longueur racinaire seule ne devrait pas être considérée comme suffisante pour une diagnose taxonomique. La fréquente présence d'hypercémentose et sa distribution non-homogène autour de l'apex racinaire pour les dents antérieures des Néanderthaliens pourrait refléter le régime de charge exercé sur les dents antérieures, probablement utilisées comme une troisième main.Distinguishing Neanderthal and modern human incisors and canines can be challenging in the case of isolated teeth found in museum collections, or from unclear stratigraphic contexts. In addition, the crown morphology cannot be used in the case of heavily worn teeth. A preliminary study based on limited samples and linear measurements (Bailey, 2005) proposed that root length alone can taxonomically discriminate Neanderthals from Upper Paleolithic and extant modern humans. This thesis investigates whether this remains true for a broader chronological and geographical sample of Neanderthals and modern humans, using micro-computed tomography. In addition to the taxonomic interest of investigating root size and shape, we discuss the functional implications of the anterior root morphology in the context of the 'teeth-as-tools' hypothesis and of para-masticatory activities. The first part was published as: Le Cabec, A., Kupczik, K., Gunz, P., Braga, J., and Hublin, J.J. (2012). Long Anterior Mandibular Tooth Roots in Neanderthals Are Not the Result of their Large Jaws. Journal of Human Evolution, 63, pp. 667-681. DOI: 10.1016/j.jhevol.2012.07.003. This part validates root length as a taxonomical tool to distinguish late Neanderthals from Upper Paleolithic and recent modern humans. Despite the absence of correlation between root size and symphyseal size, Neanderthals have large roots for the size of their jaws. It is hypothesized that the short roots of extant modern humans result from a negative allometry. The second part was published as: Le Cabec, A., Gunz, P., Kupczik, K., Braga, J. and Hublin, J.J. (2013). Anterior Tooth Root Morphology and Size in Neanderthals: Taxonomic and Functional Implications. Journal of Human Evolution, 64, pp. 169-193. DOI: 10.1016/j. jhevol.2012.08.011. Root morphology is explored across a chronologically and geographically large sample of fossil and extant hominids. Longer roots in Neanderthals may have resulted from the retention of an ancestral condition. The debated taxonomic attribution of some specimens is discussed in light of anterior tooth root morphology and shows that root length alone should not be sufficient for taxonomic diagnosis. The frequent presence of hypercementosis and its non-homogeneous distribution around the root apex in Neanderthal anterior teeth could reflect the loading regime exerted on the front teeth, likely used as a third hand