13,325 research outputs found
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
A Survey on IT-Techniques for a Dynamic Emergency Management in Large Infrastructures
This deliverable is a survey on the IT techniques that are relevant to the three use cases of the project EMILI. It describes the state-of-the-art in four complementary IT areas: Data cleansing, supervisory control and data acquisition, wireless sensor networks and complex event processing. Even though the deliverableâs authors have tried to avoid a too technical language and have tried to explain every concept referred to, the deliverable might seem rather technical to readers so far little familiar with the techniques it describes
Analysis of A Nonsmooth Optimization Approach to Robust Estimation
In this paper, we consider the problem of identifying a linear map from
measurements which are subject to intermittent and arbitarily large errors.
This is a fundamental problem in many estimation-related applications such as
fault detection, state estimation in lossy networks, hybrid system
identification, robust estimation, etc. The problem is hard because it exhibits
some intrinsic combinatorial features. Therefore, obtaining an effective
solution necessitates relaxations that are both solvable at a reasonable cost
and effective in the sense that they can return the true parameter vector. The
current paper discusses a nonsmooth convex optimization approach and provides a
new analysis of its behavior. In particular, it is shown that under appropriate
conditions on the data, an exact estimate can be recovered from data corrupted
by a large (even infinite) number of gross errors.Comment: 17 pages, 9 figure
Experimental analysis of computer system dependability
This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance
Performance of the LHCb vertex locator
The Vertex Locator (VELO) is a silicon microstrip detector that surrounds the proton-proton interaction region in the LHCb experiment. The performance of the detector during the first years of its physics operation is reviewed. The system is operated in vacuum, uses a bi-phase CO2 cooling system, and the sensors are moved to 7 mm from the LHC beam for physics data taking. The performance and stability of these characteristic features of the detector are described, and details of the material budget are given. The calibration of the timing and the data processing algorithms that are implemented in FPGAs are described. The system performance is fully characterised. The sensors have a signal to noise ratio of approximately 20 and a best hit resolution of 4 ÎŒm is achieved at the optimal track angle. The typical detector occupancy for minimum bias events in standard operating conditions in 2011 is around 0.5%, and the detector has less than 1% of faulty strips. The proximity of the detector to the beam means that the inner regions of the n+-on-n sensors have undergone space-charge sign inversion due to radiation damage. The VELO performance parameters that drive the experiment's physics sensitivity are also given. The track finding efficiency of the VELO is typically above 98% and the modules have been aligned to a precision of 1 ÎŒm for translations in the plane transverse to the beam. A primary vertex resolution of 13 ÎŒm in the transverse plane and 71 ÎŒm along the beam axis is achieved for vertices with 25 tracks. An impact parameter resolution of less than 35 ÎŒm is achieved for particles with transverse momentum greater than 1 GeV/c
Increasing resilience of ATM networks using traffic monitoring and automated anomaly analysis
Systematic network monitoring can be the cornerstone for
the dependable operation of safety-critical distributed
systems. In this paper, we present our vision for informed
anomaly detection through network monitoring and
resilience measurements to increase the operators'
visibility of ATM communication networks. We raise the
question of how to determine the optimal level of
automation in this safety-critical context, and we present a
novel passive network monitoring system that can reveal
network utilisation trends and traffic patterns in diverse
timescales. Using network measurements, we derive
resilience metrics and visualisations to enhance the
operators' knowledge of the network and traffic behaviour,
and allow for network planning and provisioning based on
informed what-if analysis
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
dynamic modelling of the swash plate of a hydraulic axial piston pump for condition monitoring applications
Abstract In the last years Prognostic and Health Management (PHM) has become one of the challenging topic in the engineering field. In particular, model-based approach for diagnostic relies on the development of a mathematical model of the system representing its flawless status. Once the model has been developed and carefully calibrated on experimental data referred to flawless pump condition the comparison between the model output and the real system output leads to the residual analysis, which gives a diagnosis of the component health. This paper presents the mathematical model of a hydraulic axial piston pump developed in order to replicate the dynamic behavior of the swash plate for PHM applications. The model has been developed on the basis of simplified hypotheses, a friction model between swash plate and bearings has been introduced. A detailed experimental activity was carried out to calibrate and validate the model with step tests and sweep tests. The comparison between numerical and experimental results shows a satisfying agreement and highlights the model capability to reproduce the swash plate dynamics. Future works will include tests with the pump in faulty conditions to evaluate the pump health state through the residual analysis of the swash plate position
Validation Techniques for Sensor Data in Mobile Health Applications
Mobile applications have become amust in every userâs smart device, andmany of these applications make use of the device sensorsâ
to achieve its goal. Nevertheless, it remains fairly unknown to the user to which extent the data the applications use can be relied
upon and, therefore, to which extent the output of a given application is trustworthy or not. To help developers and researchers and
to provide a common ground of data validation algorithms and techniques, this paper presents a review of the most commonly
used data validation algorithms, along with its usage scenarios, and proposes a classification for these algorithms. This paper also
discusses the process of achieving statistical significance and trust for the desired output.Portuguese Foundation for Science and Technology UID/EEA/50008/2013COST Action Architectures, Algorithms and Protocols for Enhanced Living Environments (AAPELE) IC130
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