Proposal of noninvasive failure diagnosis of electrical motor using googlenet

Fault diagnosis is a useful tool that reduces system maintenance risks and costs. However, data related to the system's nominal and fault operating behavior is often not collected and stored adequately, it is difficult to identify and suggest automated fault detection methods. This study proposes a solution to apply deep learning technique on the convolutional neural network (CNN) to identify some common errors on induction motors based on operation sound. The opreration sound signal emitted from on a 0.37 kW two-pole induction motor is collected in some cases such as normal operation, phase loss, phase difference and bearing breakage. Their 2-D scalogram images are analyzed by continuous Wavelet transformation which is used to train and evaluate the deep learning CNN (i.e. GoogLeNet) to identify the above faults. Experimental results show that this method can diagnose induction motor faults with accuracy up to 98.8%.

Observations of Cyanogen Bromide (BrCN) in the Global Atmosphere during the NASA Atmospheric Tomography mission (ATom) and Implications for Active Bromine Chemistry.

<p>Bromine activation (the production of Br in an elevated oxidation state) represents a mechanism for ozone destruction and mercury removal in the global troposphere, and has been a common feature of both polar boundary layers, often accompanied by nearly complete ozone destruction. The chemistry and budget of active bromine compounds (e.g. Br<sub>2</sub>, BrCl, HOBr) reflects the cycling of Br and ultimately its impact on the environment. Cyanogen bromide (BrCN) has recently been measured by iodide ion high resolution time-of-flight mass spectrometry (I<sup>-</sup> CIMS) during the NASA Atmospheric Tomography mission, and could be a previously unquantified participant in active Br chemistry. BrCN mixing ratios ranged from below detection limit (1.5pptv) up to as high as 48 pptv (10sec avg) and enhancements were almost exclusively confined to the polar boundary layers (PBL). Likely BrCN formation pathways involve the reactions of active Br (Br<sub>2</sub>, HOBr) with reduced nitrogen compounds. Gas phase loss processes due to reaction with radical species are likely quite slow and photolysis is known to be relatively slow. These features, and the lack of BrCN enhancements above the PBL, imply that surface reactions must be the major loss processes. Known liquid phase reactions of BrCN result in the conversion of the Br to bromide (Br<sup>-</sup>) or formation of C-Br bonded organic species, hence a loss of atmospheric active Br from that chemical cycle. Thus, accounting for the chemistry of BrCN will be an important aspect of understanding polar Br cycling.</p>

MSLp: Deep Superresolution for Meteorological Satellite Image

High-resolution meteorological satellite image is the basic data for weather forecasting, climate prediction, and early warning of various meteorological disasters. However, the poor image resolution is limited for both subjective and automated analyses. Through our investigation and study, we found that the meteorological satellite image is a kind of complex data with multimodal and multitemporal characteristics. Fortunately, based on zero-shot learning theory, the complexity of the meteorological satellite image can be used to enhance its own image resolution. In this work, we propose a novel framework called MSLp (Meteorological Satellite Loss phase). Specifically, we choose a zero-shot network as a backbone and propose a phase loss function. A mapping from low- to high-resolution meteorological satellite images was trained for improving the resolution by up to a factor of 4×. Our quantitative study demonstrates the superiority of the proposed approach over ZSSR and bicubic interpolation. For qualitative analysis, visual tests were performed by 7 meteorologists to confirm the utility of the proposed algorithm. The mean opinion score is 9.32 (the full score is 10). These meteorologists think that weather forecasters need higher-resolution meteorological satellite images and the high-resolution images obtained by our method have the potential to be a great help for weather analysis and forecasting.

Investigating active phase loss from supported ruthenium catalysts during supercritical water gasification

Ru loss mechanisms were investigated for the first time in SCWG by ICP-MS. Ru leaching at steady state was very low, close to thermodynamic models. Abrupt changes in process conditions must be avoided to prevent catalyst damage and higher Ru loss.

The design and practical implementation of a six-phase induction motor

This thesis presents a re-designed conventional three phase 5-hp squirrel cage, 4-pole, 48 slots induction motor to a six-phase induction motor (SPIM). It also presents the in-depth of a single layer winding of a three-phase motor that was re-design to the six-phase split winding layout which was practically explained to the understanding of both the engineers and the technicians who normally find it difficult with windings of electrical machines. The optimized re-designed SPIM is presented in the MATLAB/Simulink environment to perform a comparative assessment of the different phase loss scenarios of the six-phase configuration with respect to the six-phase healthy case and its conventional three-phase induction motor. The result shows a comparative benefit of the six-phase induction motor over the three-phase induction motor; in such that in the near future because of its effective way to provide a higher reliability and sustainability under the loss of phase/phases condition it will be practically applied in the power driven devices/machines like in the area of Electric Vehicles, etc.

Torque ripple minimization of PMBLDC motor using simple boost inverter

This paper proposes the implementation of simple boost circuit incorporated in inverter fed Brushless DC (BLDC) motor drive to boost the performance of torque. BLDC motor becoming subtle because of its performance. But the motor performance is inferior due to the voltage source inverter fed operation of BLDC motor which initiates torque ripple during commutation. Here the usage of Switched Boost Inverter (SBI) which minimizes the storage elements (passive elements), more active element and introduces shoot through mode during commutation as like Z-source inverter. The analyses of three phase switched boost inverter fed BLDC motor drive have been carried out. The performance of torque almost depends on the stator phase current of the motor. In BLDC motor during commutation interval, one phase loss its exact stator phase current hence it instigate ripple on the torque. The proposed method focuses two intentions to reduce the torque ripple. The first intension is to operate the BLDC motor at 180^ᵒ electrical conduction mode, second intension is to introduce the shoot through interval to boost the dc link voltage so as to maintain the stator phase current control which leads to suppress the torque ripple during commutation. The validation of the proposed SBI based BLDC motor control is demonstrated both by MATLAB/Simulink and Field programmable gate array (FPGA) controller-SPARRTAN III processor. The experimental results of the developed SBI based BLDC motor drive is working over a wide speed range with minimal torque ripple compared to the normal PWM based inverter control.