A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings.

The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method.

Investigating the Impact of Cu2+ Doping on the Morphological, Structural, Optical, and Electrical Properties of CoFe2O4 Nanoparticles for Use in Electrical Devices.

This study investigated the production of Cu2+-doped CoFe2O4 nanoparticles (CFO NPs) using a facile sol-gel technique. The impact of Cu2+ doping on the lattice parameters, morphology, optical properties, and electrical properties of CFO NPs was investigated for applications in electrical devices. The XRD analysis revealed the formation of spinel-phased crystalline structures of the specimens with no impurity phases. The average grain size, lattice constant, cell volume, and porosity were measured in the range of 4.55-7.07 nm, 8.1770-8.1097 Å, 546.7414-533.3525 Å3, and 8.77-6.93%, respectively. The SEM analysis revealed a change in morphology of the specimens with a rise in Cu2+ content. The particles started gaining a defined shape and size with a rise in Cu2+ doping. The Cu0.12Co0.88Fe2O4 NPs revealed clear grain boundaries with the least agglomeration. The energy band gap declined from 3.98 eV to 3.21 eV with a shift in Cu2+ concentration from 0.4 to 0.12. The electrical studies showed that doping a trace amount of Cu2+ improved the electrical properties of the CFO NPs without producing any structural distortions. The conductivity of the Cu2+-doped CFO NPs increased from 6.66 × 10-10 to 5.26 × 10-6 ℧ cm-1 with a rise in Cu2+ concentration. The improved structural and electrical characteristics of the prepared Cu2+-doped CFO NPs made them a suitable candidate for electrical devices, diodes, and sensor technology applications.

Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19.

COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as 'hybrid images' (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.

Centrifugal Compressor Stall Control by the Application of Engineered Surface Roughness on Diffuser Shroud Using Numerical Simulations.

Downsizing in engine size is pushing the automotive industry to operate compressors at low mass flow rate. However, the operation of turbocharger centrifugal compressor at low mass flow rate leads to fluid flow instabilities such as stall. To reduce flow instability, surface roughness is employed as a passive flow control method. This paper evaluates the effect of surface roughness on a turbocharger centrifugal compressor performance. A realistic validation of SRV2-O compressor stage designed and developed by German Aerospace Center (DLR) is achieved from comparison with the experimental data. In the first part, numerical simulations have been performed from stall to choke to study the overall performance variation at design conditions: 2.55 kg/s mass flow rate and rotational speed of 50,000 rpm. In second part, surface roughness of magnitude range 0-200 µm has been applied on the diffuser shroud to control flow instability. It was found that completely rough regime showed effective quantitative results in controlling stall phenomena, which results in increases of operating range from 16% to 18% and stall margin from 5.62% to 7.98%. Surface roughness as a passive flow control method to reduce flow instability in the diffuser section is the novelty of this research. Keeping in view the effects of surface roughness, it will help the turbocharger manufacturers to reduce the flow instabilities in the compressor with ease and improve the overall performance.