Self-learning of fault diagnosis identification

A good and early fault detection and isolation system along with efficient alarm management and fine sensor validation systems are very important in today¿s complex process plants, specially in terms of safety enhancement and costs reduction. This paper presents a methodology for fault characterization. This is a self-learning approach developed in two phases. An initial, learning phase, where the simulation of process units, without and with different faults, will let the system (in an automated way) to detect the key variables that characterize the faults. This will be used in a second (on line) phase, where these key variables will be monitored in order to diagnose possible faults. Using this scheme the faults will be diagnosed and isolated in an early stage where the fault still has not turned into a failure.

Nonrigid Motion Compensation of Free Breathing Acquired Myocardial Perfusion Data

In this work, we present a novel method to compensate the movement in images acquired during free breathing using first-pass gadolinium enhanced, myocardial perfusion magnetic resonance imaging (MRI). First, we use independent component analysis (ICA) to identify the optimal number of independent components (ICs) that separate the breathing motion from the intensity change induced by the contrast agent. Then, synthetic images are created by recombining the ICs, but other then in previously published work (Milles et al. 2008), we omit the component related to motion, and therefore, the resulting reference image series is free of motion. Motion compensation is then achieved by using a multi-pass non-rigid image registration scheme. We tested our method on 15 distinct image series (5 patients) consisting of 58 images each and we validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration. The average correlation to the manually obtained curves before registration 0:89 0:11 was increased to 0:98 0:02

Distributed Fault Diagnosis Using Bayesian Reasoning in MAGNETO

Many of the emerging telecom services make use of Outer Edge Networks, in particular Home Area Networks. The configuration and maintenance of such services may not be under full control of the telecom operator which still needs to guarantee the service quality experienced by the consumer. Diagnosing service faults in these scenarios becomes especially difficult since there may be not full visibility between different domains. This paper describes the fault diagnosis solution developed in the MAGNETO project, based on the application of Bayesian Inference to deal with the uncertainty. It also takes advantage of a distributed framework to deploy diagnosis components in the different domains and network elements involved, spanning both the telecom operator and the Outer Edge networks. In addition, MAGNETO features self-learning capabilities to automatically improve diagnosis knowledge over time and a partition mechanism that allows breaking down the overall diagnosis knowledge into smaller subsets. The MAGNETO solution has been prototyped and adapted to a particular outer edge scenario, and has been further validated on a real testbed. Evaluation of the results shows the potential of our approach to deal with fault management of outer edge networks.

Study of the evolution of the seismic cycle of stress and strain associated to the El Salvador Fault Zone

• Central America: – Regional studies in Central America (Seismic Hazard). – El Salvador Fault Zone (ESFZ). – Aguacaliente‐Navarro Fault Zone (ANFZ), Central Valley of Costa Rica. – Haiti (seismic hazard) • Spain: – Regional‐Nacional studies of seismic hazards (applications to building codes, eurocode, emergency plans, etc.) – Betic range zone, south of Spain. – Ibero‐Maghrebi region (collision zone)

Fault Tolerance Analysis and Self-Healing Strategy of Autonomous, Evolvable Hardware Systems

This paper presents an analysis of the fault tolerance achieved by an autonomous, fully embedded evolvable hardware system, which uses a combination of partial dynamic reconfiguration and an evolutionary algorithm (EA). It demonstrates that the system may self-recover from both transient and cumulative permanent faults. This self-adaptive system, based on a 2D array of 16 (4×4) Processing Elements (PEs), is tested with an image filtering application. Results show that it may properly recover from faults in up to 3 PEs, that is, more than 18% cumulative permanent faults. Two fault models are used for testing purposes, at PE and CLB levels. Two self-healing strategies are also introduced, depending on whether fault diagnosis is available or not. They are based on scrubbing, fitness evaluation, dynamic partial reconfiguration and in-system evolutionary adaptation. Since most of these adaptability features are already available on the system for its normal operation, resource cost for self-healing is very low (only some code additions in the internal microprocessor core)

Ferromanganese nodules and micro-hardgrounds associated with the Cadiz Contourite Channel (NE Atlantic): Palaeoenvironmental records of fluid venting and bottom currents

Ferromanganese nodule fields and hardgrounds have recently been discovered in the Cadiz Contourite Channel in the Gulf of Cadiz (850–1000 m). This channel is part of a large contourite depositional system generated by the Mediterranean Outflow Water. Ferromanganese deposits linked to contourites are interesting tools for palaeoenviromental studies and show an increasing economic interest as potential mineral resources for base and strategic metals. We present a complete characterisation of these deposits based on submarine photographs and geophysical, petrographic, mineralogical and geochemical data. The genesis and growth of ferromanganese deposits, strongly enriched in Fe vs. Mn (av. 39% vs. 6%) in this contourite depositional system result from the combination of hydrogenetic and diagenetic processes. The interaction of the Mediterranean Outflow Water with the continental margin has led to the formation of Late Pleistocene–Holocene ferromanganese mineral deposits, in parallel to the evolution of the contourite depositional system triggered by climatic and tectonic events. The diagenetic growth was fuelled by the anaerobic oxidation of thermogenic hydrocarbons (δ13CPDB=−20 to −37‰) and organic matter within the channel floor sediments, promoting the formation of Fe–Mn carbonate nodules. High 87Sr/86Sr isotopic values (up to 0.70993±0.00025) observed in the inner parts of nodules are related to the influence of radiogenic fluids fuelled by deep-seated fluid venting across the fault systems in the diapirs below the Cadiz Contourite Channel. Erosive action of the Mediterranean Outflow Water undercurrent could have exhumed the Fe–Mn carbonate nodules, especially in the glacial periods, when the lower core of the undercurrent was more active in the study area. The growth rate determined by 230Thexcess/232Th was 113±11 mm/Ma, supporting the hypothesis that the growth of the nodules records palaeoenvironmental changes during the last 70 ka. Ca-rich layers in the nodules could point to the interaction between the Mediterranean Outflow Water and the North Atlantic Deep Water during the Heinrich events. Siderite–rhodochrosite nodules exposed to the oxidising seabottom waters were replaced by Fe–Mn oxyhydroxides. Slow hydrogenetic growth of goethite from the seawaters is observed in the outermost parts of the exhumed nodules and hardgrounds, which show imprints of the Mediterranean Outflow Water with low 87Sr/86Sr isotopic values (down to 0.70693±0.00081). We propose a new genetic and evolutionary model for ferromanganese oxide nodules derived from ferromanganese carbonate nodules formed on continental margins above the carbonate compensation depth and dominated by hydrocarbon seepage structures and strong erosive action of bottom currents. We also compare and discuss the generation of ferromanganese deposits in the Cadiz Contourite Channel with that in other locations and suggest that our model can be applied to ferromanganiferous deposits in other contouritic systems affected by fluid venting.

Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Images acquired during free breathing using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) exhibit a quasiperiodic motion pattern that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. In this work, we present a method to compensate this movement by combining independent component analysis (ICA) and image registration: First, we use ICA and a time?frequency analysis to identify the motion and separate it from the intensity change induced by the contrast agent. Then, synthetic reference images are created by recombining all the independent components but the one related to the motion. Therefore, the resulting image series does not exhibit motion and its images have intensities similar to those of their original counterparts. Motion compensation is then achieved by using a multi-pass image registration procedure. We tested our method on 39 image series acquired from 13 patients, covering the basal, mid and apical areas of the left heart ventricle and consisting of 58 perfusion images each. We validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration of 13 patient data sets (39 distinct slices). We compared linear, non-linear, and combined ICA based registration approaches and previously published motion compensation schemes. Considering run-time and accuracy, a two-step ICA based motion compensation scheme that first optimizes a translation and then for non-linear transformation performed best and achieves registration of the whole series in 32 ± 12 s on a recent workstation. The proposed scheme improves the Pearsons correlation coefficient between manually and automatically obtained time?intensity curves from .84 ± .19 before registration to .96 ± .06 after registration

Magneto-optic current sensor with Faraday mirror for linear birefringence compensation

Fiber optic sensors have some advantages in subjects related with electrical current and magnetic field measurement. In spite of the optical fiber utilization advantages we have to take into account undesirable effects, which are present in real non-ideal optical fibers. In telecommunication and sensor application fields the presence of inherent and induced birefringence is crucial. The presence of birefringence may cause an undesirable change in the polarization state. In order to compensate the linear birefringence a promising method has been chosen. This method employs orthogonal polarization conjugation in the back propagation direction of the light wave in the fiber. A study and a simulation of an experimental setup are realized with the advantage of a significant sensitivity improvement.

A mathematical framework for new fault detection schemes in nonlinear stochastic continuous-time dynamical systems

n this work, a mathematical unifying framework for designing new fault detection schemes in nonlinear stochastic continuous-time dynamical systems is developed. These schemes are based on a stochastic process, called the residual, which reflects the system behavior and whose changes are to be detected. A quickest detection scheme for the residual is proposed, which is based on the computed likelihood ratios for time-varying statistical changes in the Ornstein–Uhlenbeck process. Several expressions are provided, depending on a priori knowledge of the fault, which can be employed in a proposed CUSUM-type approximated scheme. This general setting gathers different existing fault detection schemes within a unifying framework, and allows for the definition of new ones. A comparative simulation example illustrates the behavior of the proposed schemes.

A multi-agent system with distributed bayesian reasoning for network fault diagnosis

In this paper, an innovative approach to perform distributed Bayesian inference using a multi-agent architecture is presented. The final goal is dealing with uncertainty in network diagnosis, but the solution can be of applied in other fields. The validation testbed has been a P2P streaming video service. An assessment of the work is presented, in order to show its advantages when it is compared with traditional manual processes and other previous systems.

Damage assessment on building structures subjected to the recent near-fault earthquake in Lorca (Spain)

The city of Lorca (Spain) was hit on May 11th 2011 by two consecutive earthquakes with 4.6 and 5.2 Mw respectively, causing casualties and important damage in buildings. Lorca is located in the south-east region of Spain and settled on the trace of the Murcia-Totana-Lorca fault. Although the magnitudes of these ground motions were not severe, the damage observed was considerable over a great amount of buildings. More than 300 of them have been demolished and many others are being retrofitted. This paper reports a field study on the damage caused by these earthquakes. The observed damage is related with the structural typology. Further, prototypes of the damaged buildings are idealized with nonlinear numerical models and their seismic behavior and proneness to damage concentration is further investigated through dynamic response analyses.

Influence of Atmospheric Conditions on mmWave CATR Facilities: Formulation and Compensation Scheme

The performance of a CATR relies on the planarity of the synthesized test wave, which is generated within a bounded volume for which specifications are drawn. Millimetre-wave facilities deal with the classical limitations of this frequency band, among which two become critical in our analysis: time-extensive acquisition campaigns and impact of environmental variables. Both features become more evident when increasing the frequency of operation. The variation in atmospheric variables, such as humidity, temperature and pressure has an influence over the performance of all the elements of the facility. The instrumentation behavior is influenced both by the warming up process, and the ambience conditions that surround the equipment. On the changes of the atmosphere itself, they affect the electromagnetic wave propagation, given the physical link between the conditions of the atmosphere and its electric properties as an electromagnetic waves propagation medium

Fault detection and isolation on a noisy nonlinear circuit

In this paper fault detection and isolation (FDI) schemes are applied in the context of the surveillance of emerging faults in an electrical circuit. The FDI problem is studied on a noisy nonlinear circuit, where both abrupt and incipient faults in the voltage source are considered. A rigorous analysis of fault detectability precedes the application of the fault detection (FD) scheme; then, the fault isolation (FI) phase is accomplished with two alternative FI approaches, proposed as new extensions of that FD approach. Numerical simulations illustrate the applicability of the mentioned schemes.

Fault isolation schemes for a class of continuous-time stochastic dynamical systems

In this paper a new method for fault isolation in a class of continuous-time stochastic dynamical systems is proposed. The method is framed in the context of model-based analytical redundancy, consisting in the generation of a residual signal by means of a diagnostic observer, for its posterior analysis. Once a fault has been detected, and assuming some basic a priori knowledge about the set of possible failures in the plant, the isolation task is then formulated as a type of on-line statistical classification problem. The proposed isolation scheme employs in parallel different hypotheses tests on a statistic of the residual signal, one test for each possible fault. This isolation method is characterized by deriving for the unidimensional case, a sufficient isolability condition as well as an upperbound of the probability of missed isolation. Simulation examples illustrate the applicability of the proposed scheme.

Evaluation of the applicability of FRA for inter-turn fault detection in stator windings

In this paper, the applicability of the FRA technique is discussed as a method for detecting inter-turn faults in stator windings. Firstly, this method is tested in an individual medium-voltage stator coil with satisfactory results. Secondly, the tests are extended to a medium-voltage induction motor stator winding, in which inter-turn faults are performed in every coil end of one phase. Results of the frequency response in case of inter-turn faults are evaluated in both cases for different fault resistance values. The experimental setup is also described for each experiment. The results of the application of this technique to the detection of inter-turn faults justify further research in optimizing this technique for preventive maintenance.