Diagnosis of bearing faults using multi fusion signal processing techniques and mutual information

878-888Bearing is a widely used rotating component in most of the industrial machinery. Failure of bearings can incur substantial losses in the industries. During operation, to prohibit failure in bearing, it becomes necessary to identify faults that occur in bearings. In the present work, bearing vibration signals have been taken for the detection of faults in bearings. In the next step, features obtained from various signal processing techniques such as ensemble empirical mode decomposition (EEMD), walsh hadamard transform (WHT) and discrete wavelet transform (DWT) have been used to detect bearing faults (inner race defect, outer race defect, and ball defects). To select the mother wavelet, the maximum energy to entropy ration criteria has been used. Mutual Information feature ranking algorithm is used to select the relevant features. Machine learning techniques such as Random Forest, Support Vector Machine, Artificial Neural Network, and IBK are used. Training and tenfold cross-validation procedures applied to all ranked features. Results reveal that random forest gives 100 % training accuracy with one ranked feature and 98.43 % ten-fold cross-validation accuracy with seven features. From the results, it is observed that the proposed methodology can be reliable and it may serve as an effective tool for fault diagnosis of bearing

Diagnosis of bearing faults using multi fusion signal processing techniques and mutual information

Bearing is a widely used rotating component in most of the industrial machinery. Failure of bearings can incur substantial losses in the industries. During operation, to prohibit failure in bearing, it becomes necessary to identify faults that occur in bearings. In the present work, bearing vibration signals have been taken for the detection of faults in bearings. In the next step, features obtained from various signal processing techniques such as ensemble empirical mode decomposition (EEMD), walsh hadamard transform (WHT) and discrete wavelet transform (DWT) have been used to detect bearing faults (inner race defect, outer race defect, and ball defects). To select the mother wavelet, the maximum energy to entropy ration criteria has been used. Mutual Information feature ranking algorithm is used to select the relevant features. Machine learning techniques such as Random Forest, Support Vector Machine, Artificial Neural Network, and IBK are used. Training and tenfold cross-validation procedures applied to all ranked features. Results reveal that random forest gives 100 % training accuracy with one ranked feature and 98.43 % ten-fold cross-validation accuracy with seven features. From the results, it is observed that the proposed methodology can be reliable and it may serve as an effective tool for fault diagnosis of bearing

Patient-specific 3D-printed surgical guides for pedicle screw insertion: comparison of different guide design approaches

AIM: Patient-specific 3D-printed guides for pedicle screw insertion in spinal deformity surgery offer an alternative to image-guided, robotic and free-hand methods. Different design features can impact their accuracy and clinical applicability. The aim of this study was to compare the performance of three different guide designs with the nonguided free-hand technique. MATERIAL & METHODS: 3D-printed guides were design and tested using anatomical models of human spines and porcine cadaveric specimens. Three different guided groups (low, medium and full contact) and one nonguided group was formed. RESULTS & CONCLUSIONS: The design approach affected level of accuracy of screw placement. A variability in terms of accuracy of screw insertion between surgeon’s experience using nonguided/guided techniques was also observed, suggesting benefit for junior surgeons in improving surgical accuracy

Automated computation and analysis of accuracy metrics in stereoencephalography

Background: Implantation accuracy of electrodes during neurosurgical interventions is necessary to ensure safety and efficacy. Typically, metrics are computed by visual inspection which is tedious, prone to inter-/intra-observer variation, and difficult to replicate across sites. New Method: We propose an automated approach for computing implantation metrics and investigate potential sources of error. We focus on accuracy metrics commonly reported in the literature to validate our approach against metrics computed manually including entry point (EP) and target point (TP) localisation errors and angle differences between planned and implanted trajectories in 15 patients with a total of 158 stereoelectroencephalography (SEEG) electrodes. We evaluate the effect of line-of-best-fit approaches, EP definition and lateral versus Euclidean distance on metrics to provide recommendations for reporting implantation accuracy metrics. Results: We found no bias between manual and automated approaches for calculating accuracy metrics with limits of agreement of ±1 mm and ±1°. Automated metrics are robust to sources of errors including registration and electrode bending. We observe the highest error in EP deviations of μ = 0.25 mm when the post-implantation CT is used to define the point of entry. Comparison with Existing Method(s): We found no reports of automated approaches for quality assessment of SEEG electrode implantation. Neither the choice of metrics nor the possible errors that could occur have been investigated previously. Conclusions: Our automated approach is useful to avoid human errors, unintentional bias and variation that may be introduced when manually computing metrics. Our work is relevant and timely to facilitate comparisons of studies reporting implantation accuracy

A generative model of hyperelastic strain energy density functions for multiple tissue brain deformation

PURPOSE: Estimation of brain deformation is crucial during neurosurgery. Whilst mechanical characterisation captures stress-strain relationships of tissue, biomechanical models are limited by experimental conditions. This results in variability reported in the literature. The aim of this work was to demonstrate a generative model of strain energy density functions can estimate the elastic properties of tissue using observed brain deformation. METHODS: For the generative model a Gaussian Process regression learns elastic potentials from 73 manuscripts. We evaluate the use of neo-Hookean, Mooney-Rivlin and 1-term Ogden meta-models to guarantee stability. Single and multiple tissue experiments validate the ability of our generative model to estimate tissue properties on a synthetic brain model and in eight temporal lobe resection cases where deformation is observed between pre- and post-operative images. RESULTS: Estimated parameters on a synthetic model are close to the known reference with a root-mean-square error (RMSE) of 0.1 mm and 0.2 mm between surface nodes for single and multiple tissue experiments. In clinical cases, we were able to recover brain deformation from pre- to post-operative images reducing RMSE of differences from 1.37 to 1.08 mm on the ventricle surface and from 5.89 to 4.84 mm on the resection cavity surface. CONCLUSION: Our generative model can capture uncertainties related to mechanical characterisation of tissue. When fitting samples from elastography and linear studies, all meta-models performed similarly. The Ogden meta-model performed the best on hyperelastic studies. We were able to predict elastic parameters in a reference model on a synthetic phantom. However, deformation observed in clinical cases is only partly explained using our generative model

Clinical applications of magnetic resonance imaging based functional and structural connectivity

Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective

Induction of protective immunity in chickens immunised with plasmid DNA encoding infectious bursal disease virus antigens

Direct DNA inoculations were used to determine the efficacy of gene immunisation of chickens to elicit protective immune responses against infectious bursal disease virus (IBDV). Thevp2 gene of IBDV strains GP40 and D78, and thevp2-vp4-vp3 encoding segment of strain D78 were cloned in an expression vector which consisted of human cytomegalovirus (HCMV) immediate early enhancer and promoter, adenovirus tripartite leader sequences and SV40 polyadenylation signal. For purification of vaccine-quality plasmid DNA fromE. coli, an effective method was developed. Chickens were vaccinated by inoculation of DNA by two routes (intramuscular and intraperitoneal). Two weeks later, chickens were boosted with DNA, and at 2 weeks post-boost, they were challenged with virulent IBDV strain. Low to undetectable levels of IBDV-specific antibodies and no protection were observed with DNA encoding VP2. However, plasmids encoding VP2-VP4-VP3 induced IBDV-specific antibodies and protection in the chickens. DNA immunisation opens a new approach to the development of gene vaccines for chickens against infectious diseases

The Crystal Structure and RNA-Binding of an Orthomyxovirus Nucleoprotein

Genome packaging for viruses with segmented genomes is often a complex problem. This is particularly true for influenza viruses and other orthomyxoviruses, whose genome consists of multiple negative-sense RNAs encapsidated as ribonucleoprotein (RNP) complexes. To better understand the structural features of orthomyxovirus RNPs that allow them to be packaged, we determined the crystal structure of the nucleoprotein (NP) of a fish orthomyxovirus, the infectious salmon anemia virus (ISAV) (genus Isavirus). As the major protein component of the RNPs, ISAV-NP possesses a bi-lobular structure similar to the influenza virus NP. Because both RNA-free and RNA-bound ISAV NP forms stable dimers in solution, we were able to measure the NP RNA binding affinity as well as the stoichiometry using recombinant proteins and synthetic oligos. Our RNA binding analysis revealed that each ISAV-NP binds ,12 nts of RNA, shorter than the 24ﾖ28 nts originally estimated for the influenza A virus NP based on population average. The 12-nt stoichiometry was further confirmed by results from electron microscopy and dynamic light scattering. Considering that RNPs of ISAV and the influenza viruses have similar morphologies and dimensions, our findings suggest that NP-free RNA may exist on orthomyxovirus RNPs, and selective RNP packaging may be accomplished through direct RNA-RNA interactions

Does administration of non-steroidal anti-inflammatory drug determine morphological changes in adrenal cortex: ultrastructural studies

Rofecoxib (Vioxx© made by Merck Sharp & Dohme, the USA) is a non-steroidal anti-inflammatory drug which belongs to the group of selective inhibitors of cyclooxygenasis-2, i.e., coxibs. Rofecoxib was first registered in the USA, in May 1999. Since then the drug was received by millions of patients. Drugs of this group were expected to exhibit increased therapeutic action. Additionally, there were expectations concerning possibilities of their application, at least as auxiliary drugs, in neoplastic therpy due to intensifying of apoptosis. In connection with the withdrawal of Vioxx© (rofecoxib) from pharmaceutical market, attempts were made to conduct electron-microscopic evaluation of cortical part of the adrenal gland in preparations obtained from animals under influence of the drug. Every morning animals from the experimental group (15 rats) received rofecoxib (suspension in physiological saline)—non-steroidal anti-inflammatory drug (Vioxx©, Merck Sharp and Dohme, the USA), through an intragastric tube in the dose of 1.25 mg during 8 weeks. In the evaluated material, there was found a greater number of secretory vacuoles and large, containing cholesterol and other lipids as well as generated glucocorticoids, lipid drops in cytoplasm containing prominent endoplasmic reticulum. There were also found cells with cytoplasm of smaller density—especially in apical and basal parts of cells. Mitochondria occasionally demonstrated features of delicate swelling. The observed changes, which occurred on cellular level with application of large doses of the drug, result from mobilization of adaptation mechanisms of the organism

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process