Circular domain features based condition monitoring for low speed slewing bearing

This paper presents a novel application of circular domain features calculation based condition monitoring method for low rotational speed slewing bearing. The method employs data reduction process using piecewise aggregate approximation (PAA) to detect frequency alteration in the bearing signal when the fault occurs. From the processed data, circular domain features such as circular mean, circular variance, circular skewness and circular kurtosis are calculated and monitored. It is shown that the slight changes of bearing condition during operation can be identified more clearly in circular domain analysis compared to time domain analysis and other advanced signal processing methods such as wavelet decomposition and empirical mode decomposition (EMD) allowing the engineer to better schedule the maintenance work. Four circular domain features were shown to consistently and clearly identify the onset (initiation) of fault from the peak feature value which is not clearly observable in time domain features. The application of the method is demonstrated with simulated data, laboratory slewing bearing data and industrial bearing data from Coal Bridge Reclaimer used in a local steel mill

Inherent and benzo[a]pyrene-induced differential aryl hydrocarbon receptor signaling greatly affects life span, atherosclerosis, cardiac gene expression, and body and heart growth in mice

Little is known of the environmental factors that initiate and promote disease. The aryl hydrocarbon receptor (AHR) is a key regulator of xenobiotic metabolism and plays a major role in gene/environment interactions. The AHR has also been demonstrated to carry out critical functions in development and disease. A qualitative investigation into the contribution by the AHR when stimulated to different levels of activity was undertaken to determine whether AHR-regulated gene/environment interactions are an underlying cause of cardiovascular disease. We used two congenic mouse models differing at the Ahr gene, which encodes AHRs with a 10-fold difference in signaling potencies. Benzo[a]pyrene (BaP), a pervasive environmental toxicant, atherogen, and potent agonist for the AHR, was used as the environmental agent for AHR activation. We tested the hypothesis that activation of the AHR of different signaling potencies by BaP would have differential effects on the physiology and pathology of the mouse cardiovascular system. We found that differential AHR signaling from an exposure to BaP caused lethality in mice with the low-affinity AHR, altered the growth rates of the body and several organs, induced atherosclerosis to a greater extent in mice with the high-affinity AHR, and had a huge impact on gene expression of the aorta. Our studies also demonstrated an endogenous role for AHR signaling in regulating heart size. We report a gene/environment interaction linking differential AHR signaling in the mouse to altered aorta gene expression profiles, changes in body and organ growth rates, and atherosclerosis

Assessment of possible impact of a health promotion program in Korea from health risk trends in a longitudinally observed cohort

BACKGROUND: Longitudinally observed cohort data can be utilized to assess the potential for health promotion and healthcare planning by comparing the estimated risk factor trends of non-intervened with that of intervened. The paper seeks (1) to estimate a natural transition (patterns of movement between states) of health risk state from a Korean cohort data using a Markov model, (2) to derive an effective and necessary health promotion strategy for the population, and (3) to project a possible impact of an intervention program on health status. METHODS: The observed transition of health risk states in a Korean employee cohort was utilized to estimate the natural flow of aggregated health risk states from eight health risk measures using Markov chain models. In addition, a reinforced transition was simulated, given that a health promotion program was implemented for the cohort, to project a possible impact on improvement of health status. An intervened risk transition was obtained based on age, gender, and baseline risk state, adjusted to match with the Korean cohort, from a simulated random sample of a US employee population, where a health intervention was in place. RESULTS: The estimated natural flow (non-intervened), following Markov chain order 2, showed a decrease in low risk state by 3.1 percentage points in the Korean population while the simulated reinforced transition (intervened) projected an increase in low risk state by 7.5 percentage points. Estimated transitions of risk states demonstrated the necessity of not only the risk reduction but also low risk maintenance. CONCLUSIONS: The frame work of Markov chain efficiently estimated the trend, and captured the tendency in the natural flow. Given only a minimally intense health promotion program, potential risk reduction and low risk maintenance was projected

The Dynamics of Functional Brain Networks:Integrated Network States during Cognitive Task Performance

Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions, however it is unclear how this mechanism manifests over time. In this study, we use time-resolved network analysis of functional magnetic resonance imaging data to demonstrate that the human brain traverses between two functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. The integrated state enables faster and more accurate performance on a cognitive task, and is associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Our data confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.Comment: 38 pages, 4 figure

Cloning whole bacterial genomes in yeast

Most microbes have not been cultured, and many of those that are cultivatable are difficult, dangerous or expensive to propagate or are genetically intractable. Routine cloning of large genome fractions or whole genomes from these organisms would significantly enhance their discovery and genetic and functional characterization. Here we report the cloning of whole bacterial genomes in the yeast Saccharomyces cerevisiae as single-DNA molecules. We cloned the genomes of Mycoplasma genitalium (0.6 Mb), M. pneumoniae (0.8 Mb) and M. mycoides subspecies capri (1.1 Mb) as yeast circular centromeric plasmids. These genomes appear to be stably maintained in a host that has efficient, well-established methods for DNA manipulation

An investigation into the condition monitoring of large slow speed slew bearings

The condition monitoring of slow speed roller bearings has been investigated. A test-rig was designed and constructed to enable detailed measurement of horizontal rotating bearing acceleration forces in both the axial and radial plane in the speed range of 0.5 to 10 revolutions per minute. These accelerations were carried out at both sonic and ultrasonic sampling rates to establish which technique is the most appropriate. Overall bearing displacement and surface temperatures were measured. Strains generated in the stress frame by the loading of the bearing were monitored along with the power used to drive the test-rig. Measurements were obtained from two full-size slew bearings operating in Bluescope Steel Limited. One bearing operated at 4.3 rpm continuously in the vertical plane. The other slew bearing operated intermittently and with partial rotation at approximately 1 rpm in the horizontal plane. During this project, the concepts of Symmetry and Stability have been developed as a fundamental approach to information analysis. A considerable number of novel signal processing methods including; Kurtosis/Correlation dimension plots, Symmetry State Space (SSS), Symmetric Wave Decomposition (SWD), Compressed Eigenvector Deconvolution Spectral Analysis (CEDSA), Ring Matrix Fault Values (RMFV) have been developed. These methods all utilize symmetry, antisymmetry, symmetry ’breaking’, stability and enable the assessment of which sensor methodology combination is best for the situation considered. It will be shown, among other things, that ultrasonic measurements using sensors designed for Acoustic Emission (AE) permit an implementation of an early warning system for slow speed bearings. This will enable the operator to carry the minimum inventory in bearings and to plan shut downs without incurring additional costs from unplanned outages resulting from failed bearings

Vibration event detection: a monitoring method for slow speed bearings

Traditional signal analysis methods appear to fail in their ability to provide consistent meaningful information when presented with data from large slow moving slew bearings. A number of reasons for this are presented. Statistics obtained from vibration data collected from a large Coal Reclaimer and an experimental test-rig is discussed. The Coal Reclaimer rotates at 4.3 rpm about two vertically mounted, large, slew bearings. The experimental test-rig rotates at 1 rpm in the horizontal plane. These statistics are compared to the results obtained using a simple event detection algorithm. The event detection algorithm is detailed and its strengths discussed relative to other methods. It is found that the event detection method provides a consistent statistical view of the condition of the slew bearing but not necessarily better than simple statistical measures. The event detection algorithm is now being used as a condition monitoring tool on the test-rig designed to specifically condition monitor horizontally mounted slow speed (1 rpm) bearings to failure

Symmetry methods applied to the condition-monitoring of slow speed slew bearings

All data has symmetry and anti-symmetry embedded in it. This paper discusses various aspects of symmetrical transformations that can be applied to data to give machine condition information. These transformations lead to novel views of the information. Combining these symmetrical transformations with the more traditional methods of Eigenvalue/vector and Fast Fourier Transform analyses provides views of the source and extent of the problem. To illustrate the various views of the vibration information, results from a large (4.2m diameter) Coal Reclaimer slow speed (4rpm) slew bearing are presented. Many of the transformations are also currently being evaluated on an experimental test-rig specifically designed for the monitoring of horizontal slow speed (1 to 4 rpm) slew bearings

Symmetric wave decomposition as a means of identifying the number of damaged elements in a slow speed bearing

This paper discusses a symmetry transformation (Symmetric Wave Decomposition - SWD) that identifies the ‘source’ forcing functions that are contained in a time-series. Knowledge of the number of active elements (principal components) in a roller bearing is crucial to identifying how many elements may be defective and or if external forcing functions are dominant. With an understanding of these ‘active components’ the vibration data is decomposed into each active component wave. From this it can be estimated how many active components are associated with an event or events, if the damage is local or likely to be external and the extent of the damage. Vibration data from a large industrial machine containing large (4.2m diameter) slow speed (4 rpm) slew bearings has been processed with the SWD transform and the results are presented. The SWD algorithm is being implemented on an experimental test-rig that has been specifically built for the monitoring of slow speed (1rpm) bearings

De-convolution of time-series data using translational symmetry and eigenstate analysis

Translational Symmetry permits a time invariant matrix (a Ring Matrix) of a time series to be formed. Traditional Eigenvalue/vector and Fast Fourier Transform techniques applied to the Ring Matrix provide views of all the vibration modes, their magnitude and frequency content. The Ring Matrix has many interesting features associated with it that enable it to be used as an additional tool in defining the ‘condition state’ of a bearing. This paper introduces the Ring Matrix as a significant step in the ‘Blind’ deconvolution process. Application of the de-convolution method to vibration data obtained from both a large and small slow speed (1 to 4rpm) slew bearing is presented. The Ring Matrix is being applied to the condition monitoring of a bearing which forms part of an experimental test rig specifically built to monitor slow speed (1 rpm) slew bearings with a view to predicting their remaining useful life. Results, from a Coal Reclaimer which has slow speed (4rpm) slew bearings, are also presented to illustrate the various views of the vibration information