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

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

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

Condition monitoring of slow speed slewing bearing based on largest lyapunov exponent algorithm and circular-domain feature extractions

This paper presents a combined nonlinear and circular features extraction-based condition monitoring method for low speed slewing bearing. The proposed method employs the largest Lyapunov exponent (LLE) algorithm as a signal processing method based on vibration data. LLE is used to detect chaos existence in vibration data in discrete angular positions of the shaft. From the processed data, circular features such as mean, skewness and kurtosis are calculated and monitored. It is shown that the onset and the progressively deteriorating bearing condition can be detected more clearly in circular-domain features compared to time-domain features. The application of the method is demonstrated with laboratory run slewing bearing data

Application of the largest Lyapunov exponent algorithm for feature extraction in low speed slew bearing condition monitoring

This paper presents a new application of the largest Lyapunov exponent (LLE) algorithm for feature extraction method in low speed slew bearing condition monitoring. The LLE algorithm is employed to measure the degree of non-linearity of the vibration signal which is not easily monitored by existing methods. The method is able to detect changes in the condition of the bearing and demonstrates better tracking of the progressive deterioration of the bearing during the 139 measurement days than comparable methods such as the time domain feature methods based on root mean square (RMS), skewness and kurtosis extraction from the raw vibration signal and also better than extracting similar features from selected intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD) result. The application of the method is demonstrated with laboratory slew bearing vibration data and industrial bearing data from a coal bridge reclaimer used in a local steel mill

Degradation trend estimation and prognosis of large low speed slewing bearing lifetime

In many applications, degradation of bearing conditions is usually monitored by changes in time-domain features. However, in low speed (\u3c 10 rpm) slewing bearing, these changes are not easily detected because of the low energy and low frequency of the vibration. To overcome this problem, a combined low pass filter (LPF) and adaptive line enhancer (ALE) signal preconditioning method is used. Time-domain features such as root mean square (RMS), skewness and kurtosis are extracted from the output signal of the combined LPF and ALE method. The extracted features show accurate information about the incipient of fault as compared to extracted features from the original vibration signal. This information then triggers the prognostic algorithm to predict the remaining lifetime of the bearing. The algorithm used to determine the trend of the nonstationary data is auto-regressive integrated moving average (ARIMA)