Broken Rotor Bar Detection in Induction Motors through Contrast Estimation.

Induction motors (IM) are key components of any industrial process; hence, it is important to carry out continuous monitoring to detect incipient faults in them in order to avoid interruptions on production lines. Broken rotor bars (BRBs), which are among the most regular and most complex to detect faults, have attracted the attention of many researchers, who are searching for reliable methods to recognize this condition with high certainty. Most proposed techniques in the literature are applied during the IM startup transient, making it necessary to develop more efficient fault detection techniques able to carry out fault identification during the IM steady state. In this work, a novel methodology based on motor current signal analysis and contrast estimation is introduced for BRB detection. It is worth noting that contrast has mainly been used in image processing for analyzing texture, and, to the best of the authors' knowledge, it has never been used for diagnosing the operative condition of an induction motor. Experimental results from applying the approach put forward validate Unser and Tamura contrast definitions as useful indicators for identifying and classifying an IM operational condition as healthy, one broken bar (1BB), or two broken bars (2BB), with high certainty during its steady state.

The complete mitogenome of the invasive Japanese mud snail Batillaria attramentaria (Gastropoda: Batillariidae) from Elkhorn Slough, California, USA.

Genomic analysis of the invasive marine snail Batillaria attramentaria from Elkhorn Slough, Moss Landing, California, USA using 150 bp paired-end Illumina sequences resulted in the assembly of its complete mitogenome. The mitogenome is 16,095 bp in length and contains 2 rRNA, 13 protein-coding, and 22 tRNA genes (GenBank Accession MN557850). Gene content and organization of B. attramentaria are identical to the Turritellidae and Pachychilidae. The phylogenetic analysis of B. attramentaria resolves it in a fully supported clade with these same two families in the superfamily Cerithioidea. Nucleotide BLAST searches of the Elkhorn Slough cox1 gene of B. attramentaria yielded identical sequences from invasive populations from California and British Columbia, and native populations from northeastern and central Japan. These data show that mitogenome sequencing is a useful tool for studying the classification and phylogenetic history Cerithioidea.

Analysis of the complete organellar genomes of the rockweed Fucus spiralis (Fucaceae, Phaeophyceae) supports its infraspecific recognition as Fucus vesiculosus var. spiralis.

Fucus spiralis L. is a broadly distributed monoecious intertidal seaweed. The specific status of F. spiralis however is debatable. Here, we contribute to the bioinformatics and systematics of F. spiralis by analysing the complete mitochondrial and plastid genomes of a specimen from California, U.S.A. The F. spiralis mitogenome is 36,396 base pairs (bp) in length and contains 67 genes, and the plastid genome is 125,066 bp in length and contains 171 genes. The F. spiralis genomes are 99.7% and 99.8% similar in nucleotide sequence to F. vesiculosus, and support the revised classification of F. spiralis to Fucus vesiculosus var. spiralis.

FPGA-based multiprocessor system for injection molding control.

The plastic industry is a very important manufacturing sector and injection molding is a widely used forming method in that industry. The contribution of this work is the development of a strategy to retrofit control of an injection molding machine based on an embedded system microprocessors sensor network on a field programmable gate array (FPGA) device. Six types of embedded processors are included in the system: a smart-sensor processor, a micro fuzzy logic controller, a programmable logic controller, a system manager, an IO processor and a communication processor. Temperature, pressure and position are controlled by the proposed system and experimentation results show its feasibility and robustness. As validation of the present work, a particular sample was successfully injected.

Fused smart sensor network for multi-axis forward kinematics estimation in industrial robots.

Flexible manipulator robots have a wide industrial application. Robot performance requires sensing its position and orientation adequately, known as forward kinematics. Commercially available, motion controllers use high-resolution optical encoders to sense the position of each joint which cannot detect some mechanical deformations that decrease the accuracy of the robot position and orientation. To overcome those problems, several sensor fusion methods have been proposed but at expenses of high-computational load, which avoids the online measurement of the joint's angular position and the online forward kinematics estimation. The contribution of this work is to propose a fused smart sensor network to estimate the forward kinematics of an industrial robot. The developed smart processor uses Kalman filters to filter and to fuse the information of the sensor network. Two primary sensors are used: an optical encoder, and a 3-axis accelerometer. In order to obtain the position and orientation of each joint online a field-programmable gate array (FPGA) is used in the hardware implementation taking advantage of the parallel computation capabilities and reconfigurability of this device. With the aim of evaluating the smart sensor network performance, three real-operation-oriented paths are executed and monitored in a 6-degree of freedom robot.

FPGA-based fused smart-sensor for tool-wear area quantitative estimation in CNC machine inserts.

Manufacturing processes are of great relevance nowadays, when there is a constant claim for better productivity with high quality at low cost. The contribution of this work is the development of a fused smart-sensor, based on FPGA to improve the online quantitative estimation of flank-wear area in CNC machine inserts from the information provided by two primary sensors: the monitoring current output of a servoamplifier, and a 3-axis accelerometer. Results from experimentation show that the fusion of both parameters makes it possible to obtain three times better accuracy when compared with the accuracy obtained from current and vibration signals, individually used.

FPGA-based fused smart sensor for dynamic and vibration parameter extraction in industrial robot links.

Intelligent robotics demands the integration of smart sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel smart sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary sensors: an encoder and a triaxial accelerometer. The proposed smart sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA).