RESUMO
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.
Assuntos
Algoritmos , Água , Análise de Falha de EquipamentoRESUMO
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.
RESUMO
The release of phenolic-contaminated treated palm oil mill effluent (TPOME) poses a severe threat to human and environmental health. In this work, manganese-modified black TiO2 (Mn-B-TiO2) was produced for the photodegradation of high concentrations of total phenolic compounds from TPOME. A modified glycerol-assisted technique was used to synthesize visible-light-sensitive black TiO2 nanoparticles (NPs), which were then calcined at 300 °C for 60 min for conversion to anatase crystalline phase. The black TiO2 was further modified with manganese by utilizing a wet impregnation technique. Visible light absorption, charge carrier separation, and electron-hole pair recombination suppression were all improved when the band structure of TiO2 was tuned by producing Ti3+ defect states. As a result of the enhanced optical and electrical characteristics of black TiO2 NPs, phenolic compounds were removed from TPOME at a rate of 48.17%, which is 2.6 times higher than P25 (18%). When Mn was added to black TiO2 NPs, the Ti ion in the TiO2 lattice was replaced by Mn, causing a large redshift of the optical absorption edges and enhanced photodegradation of phenolic compounds from TPOME. The photodegradation efficiency of phenolic compounds by Mn-B-TiO2 improved to 60.12% from 48.17% at 0.3 wt% Mn doping concentration. The removal efficiency of phenolic compounds from TPOME diminished when Mn doping exceeded the optimum threshold (0.3 wt%). According to the findings, Mn-modified black TiO2 NPs are the most effective, as they combine the advantages of both black TiO2 and Mn doping.