A diagnostic method based on clustering qualitative event sequences

A diagnostic algorithm is described in this article that is based on clustering qualitative event sequences called traces. A sufficient number of training traces are used instead of an internal model to specify the faulty models of the system. The diagnosis consists of two phases. In the off-line training phase diagnostic clusters representing nominal and faulty behavior are formed from the set of training traces, while the centroids of these clusters are stored. Arbitrary measured traces in the on-line diagnosis phase are compared with the centroids, to recognize the most probable faulty scenario for the trace. The effects of different mapping functions and different qualitative ranges on the clustering are investigated, and the diagnostic resolution of the method is compared and discussed using a simple process system. A diagnostic case study using the benchmark of Tennessee Eastman process (TEP) is utilized to illustrate the efficiency of the proposed method.     

Effect of Axial Slit on Metallic Tube for Wireless Power Transfer via Magnetic Resonance Coupling

Wireless power transfer using a metallic tube with an axial slit was attempted to demonstrate the wireless power transfer using magnetic resonance coupling to the diagnostics infrastructure. The transmission efficiency with variable distance was measured using the transmission and receiver resonators in the tube. Besides, the transmission and receiver resonators were, respectively, set outside and inside the tube. These experiments are carried out in the computational study using FDTD method.     

An evidential approach for detection of abnormal behaviour in the presence of unreliable sensors

We address the problem of abnormal behaviour recognition of the inhabitant of a smart home in the presence of unreliable sensors. The corner stone of this work is a two-level architecture sensor fusion based on the Transferable Belief Model (TBM). The novelty of our work lies in the way we detect both unreliable sensors and abnormal behaviour within our architecture by using a temporal analysis of conflict resulting from the fusion of sensors. Detection of abnormal behaviour is based on a prediction/observation process and the influence of the faulty sources is discarded by discounting coefficients. Our architecture is tested in a real-life setting using three heterogeneous sensors enabling the detection of impossible transitions between three possible postures: Sitting, Standing and Lying. The impact of having a faulty sensor management is also tested in the real-life experiment for posture detection.     

Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network

In rotary complex machines, collapse of a component may inexplicably occur usually accompanied by a noise or a disturbance emanating from other sources. Rolling bearings constitute a vital part in many rotational machines and the vibration generated by a faulty bearing easily affects the neighboring components. Continuous monitoring, fault diagnosis and predictive maintenance, is a crucial task to reduce the degree of damage and stopping time for a rotating machine. Analysis of fault-related vibration signal is a usual method for accurate diagnosis. Among the resonant demodulation techniques, a well-known resolution often used for fault diagnosis is envelope analysis. But, usually this method may not be adequate enough to indicate satisfactory results. It may require some auxiliary additional techniques. This study suggests some methods to extract features using envelope analysis accompanied by Hilbert Transform and Fast Fourier Transform. The proposed artificial neural network (ANN) based fault estimation algorithm was verified with experimental tests and promising results. Every test was initiated with a reference ANN architecture to avoid inappropriate classification during the evaluation of fitness value. Later, ANN model was modified using a genetic algorithm providing, an optimal skillful fast-reacting network architecture with improved classification results.     

Soot formation in high pressure laminar diffusion flames

The details of the chemical and physical mechanisms of the soot formation process in combustion remain uncertain due to the highly complex nature of hydrocarbon flames, and only a few principles are firmly established mostly for atmospheric conditions. In spite of the fact that most combustion devices used for transportation operate at very high pressures (e.g., aircraft gas turbines up to 40 atm, diesel engines exceeding 100 atm), our understanding of soot formation at these pressures is not at a desirable level, and there is a fundamental lack of experimental data and complementary predictive models. The focus of this review is to assess the experimental results available from laminar co-flow diffusion flames burning at elevated pressures. First, a brief review of soot formation mechanisms in diffusion flames is presented. This is followed by an assessment of soot diagnostics techniques, both intrusive and non-intrusive, most commonly used in soot experiments including the laser induced incandescence. Then the experimental results of soot measurements done at elevated pressures in diffusion flames are reviewed and critically assessed. Soot studies in shock tubes and in premixed flames are not covered. Smoke point fuel mass flow rate is revisited, and shortcomings in recent measurements are pointed. The basic requirements for tractable and comparable measurements as a function of pressure are summarized. Most recent studies at high pressures with aliphatic gaseous fuels show that the soot yield displays a unified behaviour with reduced pressure. The maximum soot yield seems to reach a plateau asymptotically as the pressure exceeds the critical pressure of the fuel. Lack of experimental data on the sensitivity of soot morphology to pressure is emphasized. A short summary of efforts in the literature on the numerical simulation of soot formation in diffusion flames at high pressures is the last section of the paper.     

Optimisation of design parameters for collimators and pin-holes of bolometer cameras

The total radiation emission profile of fusion experiments is usually determined using the bolometer diagnostic. In order to evaluate the spatially resolved profile, many line integrated measurements are inverted using tomographic reconstruction techniques. Their success depends on a well known and optimised definition of the viewing cones of every line-of-sight. To this aim a set of equations has been derived and put in hierarchical order to define the design parameters for bolometer cameras in fusion experiments. In particular, previous considerations, which focussed on the beam width overlap and light yield optimisation, are extended to explicitly take geometrical boundary conditions imposed by the experimental device into account, with an emphasis on small gap sizes through which viewing cones have to pass through. The equations are derived for both camera types, collimator and pin-hole versions. The results obtained can be used to design bolometer cameras for any fusion device, but in particular also for ITER. An example of such an application is given and implications for the realisation of the optimal design are discussed.     

Design and performance analysis of ITER ex-vessel magnetic diagnostics

Accurate magnetic diagnostics are essential to perform reliable operation of any tokamak. The ITER magnetic diagnostics include a wide variety of sensors located on the inner and outer surfaces of the vacuum vessel, in the divertor cassettes and in the casing of the toroidal field coils. As the measurement accuracy of the inner set of magnetic sensors might be compromised by various radiation effects and high heat loads, the complementary ex-vessel set is essential to provide backup information. This paper is an overview of the ex-vessel magnetic diagnostic which consists mainly of pick-up coils, steady state sensors, Rogowski coils in the toroidal field coil casing and fibre optic current sensors. The work presented aims at designing these sensors to meet the performance requirements in spite of the constraints due to the tokamak environment. The manufacturing constraints and the positioning requirements for all the ex-vessel magnetic sensors are described. The use and expected accuracy of the entire ex-vessel magnetic diagnostic is assessed in terms of magnetic equilibrium reconstruction and plasma current measurement precision.     

Accumulation and Toxicity of Superparamagnetic Iron Oxide Nanoparticles in Cells and Experimental Animals

The uptake and distribution of negatively charged superparamagnetic iron oxide (Fe₃O₄) nanoparticles (SPIONs) in mouse embryonic fibroblasts NIH3T3, and magnetic resonance imaging (MRI) signal influenced by SPIONs injected into experimental animals, were visualized and investigated. Cellular uptake and distribution of the SPIONs in NIH3T3 after staining with Prussian Blue were investigated by a bright-field microscope equipped with digital color camera. SPIONs were localized in vesicles, mostly placed near the nucleus. Toxicity of SPION nanoparticles tested with cell viability assay (XTT) was estimated. The viability of NIH3T3 cells remains approximately 95% within 3–24 h of incubation, and only a slight decrease of viability was observed after 48 h of incubation. MRI studies on Wistar rats using a clinical 1.5 T MRI scanner were showing that SPIONs give a negative contrast in the MRI. The dynamic MRI measurements of the SPION clearance from the injection site shows that SPIONs slowly disappear from injection sites and only a low concentration of nanoparticles was completely eliminated within three weeks. No functionalized SPIONs accumulate in cells by endocytic mechanism, none accumulate in the nucleus, and none are toxic at a desirable concentration. Therefore, they could be used as a dual imaging agent: as contrast agents for MRI and for traditional optical biopsy by using Prussian Blue staining.