Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application

Efeito protetor do carvacrol sobre a deterioração da madeira de Pinus taeda em campo de apodrecimento

TCC(graduação) - Universidade Federal de Santa Catarina. Campus Curitibanos. Engenharia Florestal.Considerando que a demanda de madeira tem aumentado anualmente e que madeiras nobres estão sendo substituídas por madeiras de baixa durabilidade natural, tal como o Pinus taeda, há a necessidade de investigar alternativas para a preservação destes materiais. As técnicas utilizadas para tratar madeiras de baixa durabilidade envolvem substâncias químicas contendo arsênio e sais de cromo que são tóxicas ao meio ambiente, animais e seres humanos. Neste sentido, é importante avaliar novas substâncias quanto ao potencial antifúngico contra fungos apodrecedores de madeira. O carvacrol é uma substância aromática volátil que foi encontrada como componente majoritário (76,4%) do óleo essencial da madeira seca de Cupressus lusitânica. Nosso grupo de pesquisa investigou a atividade antifúngica in vitro contra fungos apodrecedores de madeira e encontrou significativa atividade antifúngica em baixas concentrações do óleo (250 ppm). Estes resultados anteriores indicam que há forte evidência de que o carvacrol seja o responsável pela durabilidade natural da madeira de C. lusitanica. Assim, o objetivo deste trabalho foi avaliar o efeito protetor do carvacrol em ensaio de apodrecimento à campo para a madeira de Pinus taeda. Para tanto os corpos de prova de pinus nas dimensões 20x20x300mm (largura, espessura, comprimento) foram impregnados com as soluções de trabalho, a saber: T1 –Madeira de P. taeda sem tratamento; T2 – madeira de P. taeda tratada com carvacrol a 1000 ppm (em solução aquosa com 7% de etanol); T3 – madeira de P. taeda tratada com tribromofenato de sódio (TBP90®) a 1000 ppm (em solução aquosa com 7% de etanol). Também foi incluído no experimento corpos de prova de cupressus sem tratamento (T4). T1 e T4 foram imersos em solução aquosa com 7% de etanol pelo mesmo período de tempo que T2 e T3 (48h). Após imersão dos corpos de prova nas soluções de trabalho, as concentrações da solução inicial (antes da imersão) e da final (após imersão) foram determinadas por espectrofotometria. As curvas de calibração do carvacrol (10–60 ppm, leitura em 272 nm) e do TBP90 (50–175 ppm, leitura em 306 nm) foram obtidas e utilizadas para cálculo das concentrações. A concentração da solução inicial e final para TBP90 foram 936,25 e 688,71 ppm para o carvacrol 828,37 e 662,42 ppm. O trabalho foi conduzido na área experimental florestal da Universidade Federal de Santa Catarina, Campus de Curitibanos, com coletas realizadas a cada 60 dias. Para avaliação da perda de massa foi utilizada a análise estatística Permanova em função dos dados não possuírem normalidade e por permitir a realização de ANOVA fatoriais com dados não normais. Entre os fatores tratamento, tempo e tratamento:tempo, o fator tratamento apresentou 19% de correlação, sendo o único fator com diferença significativa para perda de massa. Os fatores tempo e tempo:tratamento não apresentaram diferença significativa. O tratamento que promoveu menor perda de massa foi o C. lusitanica tratado com solução do trabalho. Apesar das potencialidades do carvacrol como agente preservante de madeira, não houve inibição do ataque fúngico em madeiras de pinus. Além disso, para todos os tratamentos em madeira de pinus foi observado ataque de cupins o que inviabilizou o uso de algumas amostras nos cálculos estatísticos. Estes resultados são preliminares e indicam que uma concentração maior de carvacrol deve ser utilizada para a impregnação na madeira de pinus ou outro método de impregnação.Considering that the demand for wood has increased annually and that noble woods are being replaced by woods of low natural durability, such as Pinus taeda, there is a need to investigate alternatives for the preservation of these materials. The techniques used to treat low–durability wood involve chemical substances containing arsenic and chromium salts that are toxic to the environment, animals and humans. In this sense, it is important to evaluate new substances regarding their antifungal potential against wood–rotting fungi. Carvacrol is a volatile aromatic substance that was found as a major component (76.4%) of the essential oil of the dry wood of Cupressus lusitanica. Our research group investigated in vitro antifungal activity against wood rotting fungi and found significant antifungal activity at low oil concentrations (250 ppm). These previous results indicate that there is strong evidence that carvacrol is responsible for the natural durability of C. lusitanica wood. Thus, the objective of this work was to evaluate the protective effect of carvacrol in a field rot test for Pinus taeda wood. For this purpose, the pine specimens in the dimensions 20x20x300mm (width, thickness, length) were impregnated with the working solutions, namely: T1 – P. taeda wood without treatment; T2 – P. taeda wood treated with carvacrol at 1000 ppm (in aqueous solution with 7% ethanol); T3 – P. taeda wood treated with sodium tribromophenate (TBP90®) at 1000 ppm (in aqueous solution with 7% ethanol). Untreated cupressus specimens (T4) were also included in the experiment. T1 and T4 were immersed in an aqueous solution with 7% ethanol for the same period of time as T2 and T3 (48h). After immersion of the specimens in the working solutions, the concentrations of the initial solution (before immersion) and of the final solution (after immersion) were determined by spectrophotometry. The calibration curves for carvacrol (10–60 ppm, reading at 272 nm) and TBP90 (50–175 ppm, reading at 306 nm) were obtained and used to calculate the concentrations. The initial and final solution concentrations for TBP90 were 936.25 and 688.71 ppm for carvacrol 828.37 and 662.42 ppm. The work was carried out in the experimental forest area of the Federal University of Santa Catarina, Campus de Curitibanos, with collections carried out every 60 days. Permanova statistical analysis was used to assess mass loss because the data were not normal and for allowing factorial ANOVA to be performed with non–normal data. Among the treatment, time and treatment:time factors, the treatment factor presented a 19% correlation, being the only factor with a significant difference for mass loss. The time and time:treatment factors showed no significant difference. The treatment that promoted less mass loss was C. lusitanica treated with working solution. Despite the potential of carvacrol as a wood preservative agent, there was no inhibition of fungal attack on pine woods. In addition, for all pine wood treatments, termite attacks were observed, which made the use of some samples in the statistical calculations unfeasible. These results are preliminary and indicate that a higher concentration of carvacrol should be used for impregnation in pine wood or another impregnation method

Learning Semantics with Deep Belief Network for Cross-Language Information Retrieval

This paper introduces a cross-language information retrieval (CLIR) framework that combines the state-of-the-art keyword-based approach with a latent semantic-based retrieval model. To capture and analyze the hidden semantics in cross-lingual settings, we construct latent semantic models that map text in different languages into a shared semantic space. Our proposed framework consists of deep belief networks (DBN) for each language and we employ canonical correlation analysis (CCA) to construct a shared semantic space. We evaluated the proposed CLIR approach on a standard ad hoc CLIR dataset, and we show that the cross-lingual semantic analysis with DBN and CCA improves the state-of-the-art keyword-based CLIR performance

ECIR 2016 Workshop on Modeling, Learning and Mining for Cross/Multilinguality (MultiLingMine 16)

[EN] The First International Workshop on Modeling, Learning and Mining for Cross/Multilinguality (MultiLingMine) was held in conjunction with the 2016 European Conference on Information Retrieval (ECIR), in Padua, Italy. This report presents an overview of the motivations and objectives underlying the establishment of this workshop. It also provides a summary of the contributing papers and of the main research topics and trends discussed among the participants.This work is partially funded by research project PON 2007-2013 “BA2Know - Business Analytics to Know”, funded by Italian Ministry of Instruction, University, and Research, and by the research project TIN2015-71147-C2-1-P of the Spanish Ministry of Economy and Competitiveness.Ienco, D.; Roche, M.; Romeo, S.; Rosso, P.; Tagarelli, A. (2016). ECIR 2016 Workshop on Modeling, Learning and Mining for Cross/Multilinguality (MultiLingMine 16). ACM SIGIR Forum. 50(2):89-95. https://doi.org/10.1145/3053408.3053424S8995502S. Banerjee, S. Kumar Naskar, P. Rosso, and S. Bandyopadhyay. The first cross-script code-mixed question answering corpus. In ECIR 2016 Workshop on Modeling, Learning and Mining for Cross/Multilinguality (MultiLingMine), Padua, Italy, March 20, 2016, pages 56--65, 2016.D. Brodić, A. Amelio, and Z. N. Milivojević. A new image analysis framework for latin and italian language discrimination. In ECIR 2016 Workshop on Modeling, Learning and Mining for Cross/Multilinguality (MultiLingMine), Padua, Italy, March 20, 2016, pages 46--55, 2016.I. Cunha, E. SanJuan, J.M. Torres-Moreno, I. Castellon, and M. Lloberes. Extending automatic discourse segmentation for texts in spanish to catalan. In ECIR 2016 Workshop on Modeling, Learning and Mining for Cross/Multilinguality (MultiLingMine), Padua, Italy, March 20, 2016, pages 36--45, 2016.H. N. Esfahani, J. Dadashkarimi, and A. Shakery. Profile-based translation in multilingual expertise retrieval. In ECIR 2016 Workshop on Modeling, Learning and Mining for Cross/Multilinguality (MultiLingMine), Padua, Italy, March 20, 2016, pages 26--35, 2016.A. Ferrando, S. Beux, V. Mascardi, and P. Rosso. Identification of disease symptoms in multilingual sentences: an ontology-driven approach. In ECIR 2016 Workshop on Modeling, Learning and Mining for Cross/Multilinguality (MultiLingMine), Padua, Italy, March 20, 2016, pages 6--15, 2016.M. Franco-Salvador, F. L. Cruz, J. A. Troyano, and P. Rosso. Cross-domain polarity classification using a knowledge-enhanced meta-classifier. Knowl.-Based Syst., 86:46--56, 2015.M. Franco-Salvador, P. Rosso, and R. Navigli. A knowledge-based representation for cross-language document retrieval and categorization. In Proc. of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL), April 26-30, 2014, Gothenburg, Sweden, pages 414--423, 2014.J. Kim, J. Nam, and I. Gurevych. Learning semantics with deep belief network for cross-language information retrieval. In Proc. of the 24th International Conference on Computational Linguistics (COLING), December 8-15, 2012, Mumbai, India, pages 579--588, 2012.Y.-M. Kim, M.-R. Amini, C. Goutte, and P. Gallinari. Multi-view clustering of multilingual documents. In Proc. of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), July 19-23, 2010, Geneva, Switzerland, pages 821--822, 2010.M. Llorens-Salvador and S. J. Delany. Deep level lexical features for cross-lingual authorship attribution. In ECIR 2016 Workshop on Modeling, Learning and Mining for Cross/Multilinguality (MultiLingMine), Padua, Italy, March 20, 2016, pages 16--25, 2016.M.-F. Moens and I. Vulic. Multilingual probabilistic topic modeling and its applications in web mining and search. In Proc. of the 17th ACM International Conference on Web Search and Data Mining (WSDM), February 24-28, 2014, New York, NY, USA, pages 681--682, 2014.R. Navigli and S. P. Ponzetto. Babelnet: Building a very large multilingual semantic network. In Proc. of the 48th Annual Meeting of the Association for Computational Linguistics, July 11-16, 2010, Uppsala, Sweden, pages 216--, 2010.S. Romeo, D. Ienco, and A. Tagarelli. Knowledge-based representation for transductive multilingual document classification. In Proc. of the 37th European Conference on IR Research (ECIR), March 29 - April 2, 2015, Vienna, Austria, pages 92--103, 2015.S. Romeo, A. Tagarelli, and D. Ienco. Semantic-based multilingual document clustering via tensor modeling. In Proc. of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), October 25-29, 2014, Doha, Qatar, pages 600--609, 2014.B. Steichen, N. Ferro, D. Lewis, and E. H. Chi. 1st international workshop on multilingual web access (MWA 2015). SIGIR Forum, 49(2):137--140, 2015.I. Vulic and M.-F. Moens. A unified framework for monolingual and cross-lingual relevance modeling based on probabilistic topic models. In Proc. of the 35th European Conference on Information Retrieval Research (ECIR), March 24-27, 2013, Moscow, Russia, pages 98--109, 2013.T. Zhang, K. Liu, and J. Zhao. Cross lingual entity linking with bilingual topic model. In Proc. of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), August 3-9, 2013, Beijing, China, 2013