Bearing fault diagnosis based on intrinsic time-scale decomposition and improved Support vector machine model

In order to achieve the bearing fault diagnosis so as to ensure the steadiness of rotating machinery. This article proposed a model based on intrinsic time-scale decomposition (ITD) and improved support vector machine method (ISVM), so as to deal with the non-stationary and nonlinear characteristics of bearing vibration signals. Firstly, the feature extraction method intrinsic time-scale decomposition (ITD) is used and the energy entropy are extracted so as to process the vibration signal in this paper. Then, the local tangent space alignment (LTSA) method is introduced to extract the characteristic features and reduce the dimension of the selected entropy features. Finally, the features are used to train the ISVM model as to classify bearings defects. Cases of actual were analyzed. The results validate the effectiveness of the proposed algorithm

Beyond nanosilica: Geopolymeric nanoaluminosilicates for functional nanocomposites

Nanoaggregates such as nanosilica and carbon black are two of the most important inorganic nanomaterials used in modern technologies including nanocomposites. By using sustainable geopolymer chemistry, we introduce new aluminosilicate nanoaggregates and nanostructured zeolites which may become as important as the aforementioned materials in nanocomposites, with their own unique functionalities. Geopolymer has been extensively studied and utilized as “green cement” in addressing global warming issues, one of the most challenging problems in human sustainability. At the same time, it is one of the few inorganic material systems that can be produced in a large scale and thus has a potential to meet the demand of large-scale applications. We will describe the nature of the sustainable, scalable production methods and discuss the key features of the materials including morphologies, surface areas, porosity, aggregate size, and zeolitic crystallinity. The nanostructured zeolite products demonstrate the ”nano” effect of their own, in terms of the short diffusion lengths within individual crystals and of the high surface area. Examples of their superior performances will be given for their applications in their neat form. Expansion of the original synthetic method has allowed organic-modified nanoaluminosilicates with increased hydrophobicity which can be important in nanocomposite fabrication

Immense potential of geopolymeric nanomaterials for sustainability applications

Geopolymer has been extensively studied and utilized as “green cement” in addressing global warming issues, one of the most challenging problems in human sustainability. It is one of the few inorganic material systems that can be produced in a large scale and thus has a potential to truly address such large-scale problems. In connection to the innate “nano” properties of geopolymer materials, we present some of our new progresses in the pursuit of new geopolymeric aluminosilicate nanomaterials and their sustainability applications. We will first briefly describe syntheses and properties of three different types of the new nanomaterials (Figure 1) and will illustrate their uses. For example, nanoporous geopolymer materials could be produced and used as an excellent arsenic absorbent for ground water purification and as a highly effective biodiesel catalyst. High-structure geopolymer nanoaggregates can be synthesized with controlled zeolicity for polymer nanocomposite applications with excellent energy-saving performances. Highly-crystalline hierarchical zeolites have been discovered to show an exceptional CO2 capacity, sorption kinetics, selectivity and regeneration capability essential for cost-effective CO2 separation. Superior ion exchange kinetics of the material has been observed for silver-ion zeolite with a superb antibacterial efficacy against antibiotics-resistant MRSA bacteria. Their out-of-the-lab usages are currently being realized in industry with future goal of megatonic production. Please click Additional Files below to see the full abstract

Bearing fault diagnosis based on intrinsic time-scale decomposition and improved Support vector machine model

In order to achieve the bearing fault diagnosis so as to ensure the steadiness of rotating machinery. This article proposed a model based on intrinsic time-scale decomposition (ITD) and improved support vector machine method (ISVM), so as to deal with the non-stationary and nonlinear characteristics of bearing vibration signals. Firstly, the feature extraction method intrinsic time-scale decomposition (ITD) is used and the energy entropy are extracted so as to process the vibration signal in this paper. Then, the local tangent space alignment (LTSA) method is introduced to extract the characteristic features and reduce the dimension of the selected entropy features. Finally, the features are used to train the ISVM model as to classify bearings defects. Cases of actual were analyzed. The results validate the effectiveness of the proposed algorithm

A fault diagnosis model based on singular value manifold features, optimized SVMs and multi-sensor information fusion

To achieve better fault diagnosis of rotating machinery, this paper presents a novel intelligent fault diagnosis model based on singular value manifold features (SVMF), optimized support vector machines (SVMs) and multi-sensor information fusion. Firstly, a new fault feature named SVMF is developed to better represent faults. SVMF is acquired by extracting manifold topology features of the singular spectrum. Compared with frequently-used fault features, the feature scale of SVMF is constant for variable rotating speed, and the extraction process of SVMF also has the effect of self-weighting. So SVMF has a better representation of faults. Then, to select optimal parameters for model training of SVMs, an improved fruit fly algorithm is proposed by introducing a guidance search mechanism and enhanced local search operation, and as a result both the convergence speed and accuracy are improved. Finally, the Dempster–Shafer evidence theory is introduced to fuse decision-level information from SVM models of multiple sensors. Information fusion eliminates the conflict of conclusions on fault diagnosis from multiple sensors, which leads to high robustness and accuracy of the fault diagnosis model. As a summary, the proposed method combines the advantages of SVMF in fault representation, SVMs in fault identification and the Dempster–Shafer evidence theory in information fusion, and as a result the proposed method will perform better at fault diagnosis. The proposed intelligent fault diagnosis model is subsequently applied to fault diagnosis of the gearbox. Experimental results show that the proposed diagnostic framework is versatile at detecting faults accurately

Bearing remain life prediction based on weighted complex SVM models

Aiming to achieve the bearing remaining life prediction, this research proposed a method based on the weighted complex support vector machine (SVM) model. Firstly, the features are extracted by time domain, time-frequency domain method, so as the extract the original features. However, the extracted original features still with high dimensional and include superfluous information, the multi-features fusion technique principal component analysis (PCA) is used to merge the features and reduce the dimension. And the bearing degradation indicator is constructed based on the first principal component, which can indicate the bearing early failure state precisely. Then, based on the life condition indicator, the weighted complex SVM model is used to achieve the bearing remain life prediction, in this model, the particle swarm algorithm (PSO) method is used to select the SVM internal parameters, the phase space reconstruction algorithm is used to determine the structure of the SVM. Cases of actual were analyzed, the results proved the effectiveness of the methodology

Research on dynamical characteristics of planetary gear system with tooth pitting

The dynamical characteristics research of planetary gear system with tooth pitting is useful for early fault diagnosis and monitor. However, it is an unsolved puzzle to establish the relationship between tooth pitting and dynamical characteristics. In this study, a pitting fault analytical model is proposed to investigate the effects of tooth pitting on the gear mesh stiffness. Then this mesh stiffness with tooth pitting is incorporated into a dynamical model of planetary gear system, and the effects of the tooth pitting on the vibration characteristics is investigated. The simulated results show that the time-varying mesh stiffness is reduced with tooth pitting propagations along width or depth direction. The mesh frequency and its harmonics are mainly frequencies components in the frequency spectrum of dynamic mesh force, but sidebands caused by the tooth pitting are more sensitive than the mesh frequency and its harmonics. The tooth pitting frequency and its harmonics also increase with the rising rotational speed of the sun gear. In addition, both relative statistical indicators of RMS and Kurtosis increase with the growth of tooth pitting size. But the relative indicators have different sensitivity on the vibration signal type. These results could supply some guidance to the condition monitoring and fault diagnosis of planetary gear system, especially to the gear tooth pitting at early stage

Genome-wide association and genomic prediction for resistance to southern corn rust in DH and testcross populations

Southern corn rust (SCR), caused by Puccinia polysora Underw, is a destructive disease that can severely reduce grain yield in maize (Zea mays L.). Owing to P. polysora being multi-racial, it is very important to explore more resistance genes and develop more efficient selection approaches in maize breeding programs. Here, four Doubled Haploid (DH) populations with 384 accessions originated from selected parents and their 903 testcross hybrids were used to perform genome-wide association (GWAS). Three GWAS processes included the additive model in the DH panel, additive and dominant models in the hybrid panel. As a result, five loci were detected on chromosomes 1, 7, 8, 8, and 10, with P-values ranging from 4.83×10-7 to 2.46×10-41. In all association analyses, a highly significant locus on chromosome 10 was detected, which was tight chained with the known SCR resistance gene RPPC and RPPK. Genomic prediction (GP), has been proven to be effective in plant breeding. In our study, several models were performed to explore predictive ability in hybrid populations for SCR resistance, including extended GBLUP with different genetic matrices, maker based prediction models, and mixed models with QTL as fixed factors. For GBLUP models, the prediction accuracies ranged from 0.56-0.60. Compared with traditional prediction only with additive effect, prediction ability was significantly improved by adding additive-by-additive effect (P-value< 0.05). For maker based models, the accuracy of BayesA and BayesB was 0.65, 8% higher than other models (i.e., RRBLUP, BRR, BL, BayesC). Finally, by adding QTL into the mixed linear prediction model, the accuracy can be further improved to 0.67, especially for the G_A model, the prediction performance can be increased by 11.67%. The prediction accuracy of the BayesB model can be further improved significantly by adding QTL information (P-value< 0.05). This study will provide important valuable information for understanding the genetic architecture and the application of GP for SCR in maize breeding

Pixel-Level Concrete Crack Segmentation Using Pyramidal Residual Network with Omni-Dimensional Dynamic Convolution

Automated crack detection technologies based on deep learning have been extensively used as one of the indicators of performance degradation of concrete structures. However, there are numerous drawbacks of existing methods in crack segmentation due to the fine and microscopic properties of cracks. Aiming to address this issue, a crack segmentation method is proposed. First, a pyramidal residual network based on encoder–decoder using Omni-Dimensional Dynamic Convolution is suggested to explore the network suitable for the task of crack segmentation. Additionally, the proposed method uses the mean intersection over union as the network evaluation index to lessen the impact of background features on the network performance in the evaluation and adopts a multi-loss calculation of positive and negative sample imbalance to weigh the negative impact of sample imbalance. As a final step in performance evaluation, a dataset for concrete cracks is developed. By using our dataset, the proposed method is validated to have an accuracy of 99.05% and an mIoU of 87.00%. The experimental results demonstrate that the concrete crack segmentation method is superior to the well-known networks, such as SegNet, DeeplabV3+, and Swin-unet

Influence of reciprocating friction on friction and wear characteristics of MoS2 films

Molybdenum disulfide has the characteristics of reducing friction and wear resistance and is often used as a solid lubricant in spacecraft. Due to the particularity of space missions, part of the rotating structure needs to perform reciprocating motion, which accelerates the wear of the molybdenum disulfide film. In this study, the molecular dynamics simulation method was used to study the reciprocating friction characteristics of molybdenum disulfide thin films. The effects of load and temperature on the friction and wear characteristics of molybdenum disulfide films during reciprocating friction were studied. The results show that the monolayer and bilayer molybdenum disulfide films have different damage thresholds. This study provides a new idea at the atomic level for the study of lubrication under the reciprocating motion of spacecraft