Fault detection with Conditional Gaussian Network

International audience

Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors

Non-intrusive monitoring of health state of induction machines within industrial process and harsh environments poses a technical challenge. In the field, winding failures are a major fault accounting for over 45% of total machine failures. In the literature, many condition monitoring techniques based on different failure mechanisms and fault indicators have been developed where the machine current signature analysis (MCSA) is a very popular and effective method at this stage. However, it is extremely difficult to distinguish different types of failures and hard to obtain local information if a non-intrusive method is adopted. Typically, some sensors need to be installed inside the machines for collecting key information, which leads to disruption to the machine operation and additional costs. This paper presents a new non-invasive monitoring method based on GMRs to measure stray flux leaked from the machines. It is focused on the influence of potential winding failures on the stray magnetic flux in induction machines. Finite element analysis and experimental tests on a 1.5-kW machine are presented to validate the proposed method. With time-frequency spectrogram analysis, it is proven to be effective to detect several winding faults by referencing stray flux information. The novelty lies in the implement of GMR sensing and analysis of machine faults.

Spatially defined peptidomimetics for pain relief and for targeted cancer therapy and diagnostics

Cancer is a disease that has plagued scientists for decades, and how to treat cancer and its complications are inevitable topics in current scientific research. Cancer pain is a major factor that reduces the quality of life of patients. Therefore, the development of analgesic agents with minimal adverse side effects, especially with low addiction, has attracted more and more attention. Among them, opioid analgesics are widely used to alleviate cancer pain and improve the quality of life of patients with advanced cancer, such as in the palliative therapy. Although peptide drugs are efficient, selective and safe, they have several unignorable disadvantages such as poor biological stability, rapid excretion, difficulty in penetrate blood brain barrier. In order to solve these problems, peptidomimetics were developed by introducing unnatural/modified amino acids, decorated peptide backbone, conformational restrictions and secondary structure mimics in peptide sequence. Compared with peptides, peptidomimetics have improved biological stability, increased bioavailability, high affinity and selectivity for receptor binding, and decreased adverse side effects. As the second part of this thesis, I explored the opportunity to design peptide-functionalized responsive biomaterials for the detection of cancer cell and the selective delivery of cytotoxic drugs. The conjugation of peptides with biomaterials enhanced the stability of the loaded drugs, improved targeted delivery, decreased side effects, and increased bioavailability. The precise and controllable drug delivery platform has profound application prospects in cancer treatment. Grafting specific peptides sequence on the surface of biomaterials can satisfy different drug delivery demands according to the characteristics of both peptides and biomaterials. For example, the introduction of tumor-targeting peptides can guide biomaterials into tumor lesions, and blood-brain barrier (BBB) shuttle peptides can lead biomaterials to penetrate the BBB, etc.

Aeromonas detection and their toxins from drinking water form reservoirs and drinking fountains

Aeromonads are inhabitants of aquatic ecosystems and are described as being involved in intestinal disturbances and other infections. A total of 200 drinking water samples from domestic and public reservoirs and drinking fountains located in São Paulo (Brazil), were analyzed for the presence of Aeromonas. Samples were concentrated by membrane filtration and enriched in APW. ADA medium was used for Aeromonas isolation and colonies were confirmed by biochemical characterization. Strains isolated were tested for hemolysin and toxin production. Aeromonas was detected in 12 samples (6.0%). Aeromonas strains (96) were isolated and identified as: A. caviae (41.7%), A. hydrophila (15.7%), A.allosacharophila (10.4%), A. schubertii (1.0%) and Aeromonas spp. (31.2%).The results revealed that 70% of A. caviae, 66.7% of A. hydrophila, 80% of A. allosacharophila and 46.6% of Aeromonas spp. were hemolytic. The assay for checking production of toxins showed that 17.5% of A. caviae, 73.3% of A. hydrophila, 60% of A. allosacharophila, 100% of A. schubertii, and 33.3% of Aeromonas spp. were able to produce toxins. The results demonstrated the pathogenic potential of Aeromonas, indicating that the presence of this emerging pathogen in water systems is a public health concern

Innovation concept for measurement gross error detection and identification in power system state estimation

In this study, the innovation approach is used to estimate the measurement total error associated with power system state estimation. This is required because the power system equations are very much correlated with each other and as a consequence part of the measurements errors is masked. For that purpose an index, innovation index (II), which provides the quantity of new information a measurement contains is proposed. A critical measurement is the limit case of a measurement with low II, it has a zero II index and its error is totally masked. In other words, that measurement does not bring any innovation for the gross error test. Using the II of a measurement, the masked gross error by the state estimation is recovered; then the total gross error of that measurement is composed. Instead of the classical normalised measurement residual amplitude, the corresponding normalised composed measurement residual amplitude is used in the gross error detection and identification test, but with m degrees of freedom. The gross error processing turns out to be very simple to implement, requiring only few adaptations to the existing state estimation software. The IEEE-14 bus system is used to validate the proposed gross error detection and identification test.

Hybrid fault diagnosis scheme implementation for power distribution systems automation

Power distribution automation and control are import-ant tools in the current restructured electricity markets. Unfortunately, due to its stochastic nature, distribution systems faults are hardly avoidable. This paper proposes a novel fault diagnosis scheme for power distribution systems, composed by three different processes: fault detection and classification, fault location, and fault section determination. The fault detection and classification technique is wavelet based. The fault-location technique is impedance based and uses local voltage and current fundamental phasors. The fault section determination method is artificial neural network based and uses the local current and voltage signals to estimate the faulted section. The proposed hybrid scheme was validated through Alternate Transient Program/Electromagentic Transients Program simulations and was implemented as embedded software. It is currently used as a fault diagnosis tool in a Southern Brazilian power distribution company.

Jointly multi-user detection and channel estimation with genetic algorithm

This work aims at proposing the use of the evolutionary computation methodology in order to jointly solve the multiuser channel estimation (MuChE) and detection problems at its maximum-likelihood, both related to the direct sequence code division multiple access (DS/CDMA). The effectiveness of the proposed heuristic approach is proven by comparing performance and complexity merit figures with that obtained by traditional methods found in literature. Simulation results considering genetic algorithm (GA) applied to multipath, DS/CDMA and MuChE and multi-user detection (MuD) show that the proposed genetic algorithm multi-user channel estimation (GAMuChE) yields a normalized mean square error estimation (nMSE) inferior to 11%, under slowly varying multipath fading channels, large range of Doppler frequencies and medium system load, it exhibits lower complexity when compared to both maximum likelihood multi-user channel estimation (MLMuChE) and gradient descent method (GrdDsc). A near-optimum multi-user detector (MuD) based on the genetic algorithm (GAMuD), also proposed in this work, provides a significant reduction in the computational complexity when compared to the optimum multi-user detector (OMuD). In addition, the complexity of the GAMuChE and GAMuD algorithms were (jointly) analyzed in terms of number of operations necessary to reach the convergence, and compared to other jointly MuChE and MuD strategies. The joint GAMuChE-GAMuD scheme can be regarded as a promising alternative for implementing third-generation (3G) and fourth-generation (4G) wireless systems in the near future. Copyright (C) 2010 John Wiley & Sons, Ltd.

Molecular detection and differentiation of Brazilian and African isolates of the entomopathogen Neozygites tanajoae (Entomophthorales: Neozygitaceae) with PCR using specific primers

Neozygites tanajoae is an entomopathogenic fungus which has been used for biocontrol of the cassava green mite (Mononychellus tanajoa, CGM) in Africa. Establishment and dispersal of Brazilian isolates which have been introduced into some African countries in recent years to improve CGM control was followed with specific PCR assays. Two primer pairs, NEOSSU_F/NEOSSU_R and 8DDC_F/8DDC_R, were used to differentiate isolates collected from several locations in Brazil and from three countries in Africa, Benin, Ghana and Tanzania. The first primer pair enabled the species-specific detection of Neozygites tanajoae, while the second differentiated the Brazilian isolates from those of other geographical origin. PCR assays were designed for detection of fungal DNA in the matrix of dead infested mites since N. tanajoae is difficult to isolate and culture on selective artificial media. Our results show that all isolates (Brazilian and African) that sporulated on mummified mites were amplified with the first primer pair confirming their Neozygites tanajoae identity. The second pair amplified DNA from all the Brazilian isolates, but did not amplify any DNA samples from the African isolates. None of the two primers showed amplification neither from any of the non-sporulating mite extracts nor from the dead uninfected mites used as negative controls. We confirmed that the two primer pairs tested are suitable for the detection and differential identification of N. tanajoae isolates from Brazil and Africa and that they are useful to monitor the establishment and spread of the Brazilian isolates of N. tanajoae introduced into Benin or into other African countries for improvement of CGM biocontrol.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

NEURAL CORRELATES OF SCENT MARKING BEHAVIOR IN C57BL/6J MICE: DETECTION AND RECOGNITION OF A SOCIAL STIMULUS

Mice show urinary scent marking behavior as a form of social communication. Marking to a conspecific stimulus mouse or odor varies with stimulus familiarity, indicating discrimination of novel and familiar animals. This study investigated Fos immunoreactivity in inbred C57BL/6J (C57) males following scent marking behavior in response to detection of a social stimulus, or discrimination between a familiar and an unfamiliar conspecific. In Experiment 1 C57 mice were exposed for four daily trials to an empty chamber; on a test day they were exposed to the same chamber or to a male CD-1 mouse in that chamber. Increased scent marking to the CD-1 mouse was associated with increased Fos-immunoreactive cells in the basolateral amygdala, medial amygdala, and dorsal and ventral premammillary nuclei. In Experiment 2 C57 mice were habituated to a CD-1 male for 4 consecutive days and, on the 5th day, exposed to the same CD-1 male, or to a novel CD-1 male. Mice exposed to a novel CD-1 displayed a significant increase in scent marking compared to their last exposure to the familiar stimulus, indicating discrimination of the novelty of this social stimulus. Marking to the novel stimulus was associated with enhanced activation of several telencephalic, as well as hypothalamic and midbrain, structures in which activation had not been seen in the detection paradigm (Experiment 1). These included medial prefrontal and piriform cortices, and lateral septum; the paraventricular nuclei, ventromedial nuclei, and lateral area of the hypothalamus, and the ventrolateral column of the periaqueductal gray. These data suggest that a circumscribed group of structures largely concerned with olfaction is involved in detection of a conspecific olfactory stimulus, whereas discrimination of a novel vs. a familiar conspecific stimulus engages a wider range of forebrain structures encompassing higher-order processes and potentially providing an interface between cognitions and emotions. (C) 2009 IBRO. Published by Elsevier Ltd. All rights reserved.

Genetic polymorphism in an inflammasome component, cervical mycoplasma detection and female infertility in women undergoing in vitro fertilization

The inflammasome is an inducible cytoplasmic structure that is responsible for production and release of biologically active interleukin-1 (IL-1). A polymorphism in the inflammasome component NALP3 has been associated with decreased IL-1 levels and increased occurrence of vaginal Candida infection. We hypothesized that this polymorphism-induced variation would influence susceptibility to infertility. DNA was obtained from 243 women who were undergoing in vitro fertilization (IVF) and tested for a length polymorphism in intron 2 of the gene coding for NALP3 (gene symbol CIAS1). At the conclusion of testing the findings were analyzed in relation to clinical parameters and IVF outcome. The frequency of the 12 unit repeat allele, associated with maximal inflammasome activity, was 62.3% in cases of female infertility vs. 75.6% in cases where only the male partner had a detectable fertility problem (p = 0.0095). Conversely, the frequency of the 7 unit repeat allele was 28.9% in those with a female fertility problem, 17.0% in women with infertile males and 18.4% in idiopathic infertility (p = 0.0124). Among the women who were cervical culture-positive for mycoplasma the frequency of the 7 unit repeat was 53.7% as opposed to 19.5% in those negative for this infection (p < 0.0001). We conclude that the CIAS1 7 unit repeat polymorphism increases the likelihood of mycoplasma infection-associated female infertility. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Cancer detection and mammogram volume of radiologists in a population-based screening programme

This study investigates the relationship between the number of screening mammograms read by radiologists and the screening breast cancer detection rate. Cancer detection rates for incident screens (all women aged >= 40 years) were compared by increasing categories of reader volume using Poisson regression. Data from New South Wales (NSW) for a 2 year period (2000-2001) were obtained from the BreastScreen NSW programme. Cancer detection rates increased with the number of mammograms read in the programme, reaching a plateau of approximately 40 per 10,000 after 1375 mammograms per year. No significant differences in cancer detection were evident above 875 mammograms (compared to below 875 mammograms) per year (RR = 0.79, 95% CI 0.63-0.99). (c) 2005 Elsevier Ltd. All rights reserved.

Application of Real-Time PCR Stool Assay for Helicobacter pylori Detection and Clarithromycin Susceptibility Testing in Brazilian Children

Background: Helicobacter pylori ClariRes assay is a novel commercially available real-time PCR assay allowing H. pylori detection and clarithromycin susceptibility testing in either gastric biopsy or stool specimens. Objective: The aim of this study was to validate the novel biprobe real-time assay in stool specimens from 217 dyspeptic children. Methods: DNA from gastric biopsies and stool specimens were obtained and submitted to the biprobe real time assay for H. pylori detection and clarithromycin susceptibility testing. Results: The sensitivity, specificity, and test accuracy were 69, 100 and 93.9% for the detection of H. pylori infection and 83.3, 100 and 95.6%, for detection of clarithromycin resistance. Conclusion: This assay proved to be appropriate for H. pylori clarithromycin susceptibility testing, particularly in children populations where a high prevalence of clarithromycin-resistant strains is suspected.