Defect detection and diagnosis in the transient regime of induction motors based on intelligent systems

Os motores de indução trifásicos são os principais elementos de conversão de energia elétrica em mecânica para o setor produtivo, visto que apresentam baixo custo e longa durabilidade. Além disso, a redução de custos de manutenção e a implantação de técnicas preditivas torna-se um incentivo para o desenvolvimento de sistemas capazes de identificar defeitos intrínsecos à máquina. Frente a isso, essa pesquisa visa constituir uma metodologia robusta baseada em sistemas inteligentes para o diagnóstico e a detecção de defeitos de barras quebradas em motores de indução trifásicos ainda no regime transitório de operação. Para isso, inicialmente foi realizada uma busca sistematizada da literatura com o objetivo de alcançar o estado da arte da temática de detecção e de diagnóstico de defeitos em máquinas elétricas rotativas. O sistema inteligente proposto foi validado e testado com dados experimentais obtidos em condições normais e defeituosas do motor. Dessa maneira, foi proposta uma configuração baseada inteligência computacional para a identificação e supervisão de defeitos nesses elementos, e essas arquiteturas foram avaliadas e comparadas por meios de uma medida multicritério, inédita para essa finalidade, para atestar a relevância da metodologia proposta em relação as já existentes. Os resultados mostraram uma metodologia ágil e robusta para a recomendação de sistemas de detecção e de diagnóstico de defeitos em motores de indução com base nas características dos algoritmos de seleção de atributos e de aprendizado de máquinas.Three-phase induction motors are the main elements for converting electrical energy into mechanics for the productive sector since they have low cost and long durability. Also, the reduction of maintenance costs and the implementation of predictive techniques becomes an incentive for developing systems capable of identifying defects intrinsic to the machine. Given this, this research aims to constitute a robust methodology based on intelligent systems for diagnosing and detecting broken bar defects in three-phase induction motors still in the transient regime of operation. To this end, a systematic search of the literature was initially carried out to achieve the state of art of detecting and diagnosing defects in rotating electrical machines. The proposed intelligent system was validated and tested with experimental data obtained under normal and defective engine conditions. In this way, a configuration based on computational intelligence was proposed to identify and supervise defects in these elements. These architectures were evaluated and compared employing a multicriteria measure, unprecedented for this purpose, to attest to the proposed methodology\'s relevance concerning the already existing ones. The results showed an agile and robust methodology for recommending defect detection and diagnosis systems in induction motors based on the characteristics of the attribute selection and machine learning algorithms

Biochar affects silicification patterns and physical traits of rice leaves cultivated in a desilicated soil (Ferric Lixisol)

Aims Increasing the leaf silicification of cereal crops to ameliorate defenses against stresses and improve yields constitutes a major challenge in (sub-)tropical regions with highly desilicated soils. We tested the efficacy of different biochars – as readily available alternatives to commercial fertilizers – to increase leaf silicification and understand subsequent impacts of leaf traits that might benefit crops. Methods We compared the application of two biochars (rice-derived biochar with 198 g kg− 1 of Si and cotton-derived biochar with 4 g kg− 1 of Si) and wollastonite (240 g kg− 1 of Si) at two application rates on the leaf silicification patterns and leaf traits of rice growing in pots containing highly desilicated soil (Ferric Lixisol) from Burkina Faso. Results Leaf Si increased from 19.0 to 70.4 g kg− 1 with Si addition (control  Conclusions We demonstrate the effective use of rice biochar in desilicated environments to improve the Si status of cereal crops and their associated leaf traits. In particular, the decrease in LMA with rice biochar application shows a promising capacity of rice biochar to reduce rice leaf C costs. In situ trials are now needed to investigate whether or not these beneficial effects may result in increased crop yields through resilience against environmental stresses

Recent Advances in the Studies on Luotonins

Luotonins are alkaloids from the aerial parts of Peganum nigellastrum Bunge. that display three major skeleton types. Luotonins A, B, and E are pyrroloquinazolino-quinoline alkaloids, luotonins C and D are canthin-6-one alkaloids, and luotonin F is a 4(3H)-quinazolinone alkaloid. All six luotonins have shown promising cytotoxicities towards selected human cancer cell lines, especially against leukemia P-388 cells. Luotonin A is the most active one, with its activity stemming from topoisomerase I-dependent DNA-cleavage. Such intriguing biological activities and unique structures have led not only to the development of synthetic methods for the efficient synthesis of these compounds, but also to interest in structural modifications for improving the biological properties. Recent progress in the study of luotonins is covered