A Study Of The Diagnostic Amplitude Of Rolling Bearing Under Increasing Radial Clearance Using Modulation Signal Bispectrum

The rolling element bearing is a key part of machines. The accurate and timely diagnosis of its faults is critical for predictive maintenance. Most re-searches have focused on the fault location identification. To estimate the fault severity accurately, this paper focuses on the study of roller bearing vibration amplitude under increasing radial clearances due to inevitable wear using the modulation signal bispectrum (MSB). The experiment is carried out for bearings with two different clearances for the inner race fault and the outer race fault cases. The results show that the vibration amplitudes at fault characteristic frequencies exhibit significant changes with increasing clearances. However, the amplitudes of vibrations tend to increase with the severity of the outer race fault and decrease with the severity of the inner race fault. Therefore, it is necessary to take into account these effects in diagnosing the size of defect

Fine Particulate Matter Concentrations in Urban Chinese Cities, 2005–2016: A Systematic Review

Background: Particulate matter pollution has become a growing health concern over the past few decades globally. The problem is especially evident in China, where particulate matter levels prior to 2013 are publically unavailable. We conducted a systematic review of scientific literature that reported fine particulate matter (PM2.5) concentrations in different regions of China from 2005 to 2016. Methods: We searched for English articles in PubMed and Embase and for Chinese articles in the China National Knowledge Infrastructure (CNKI). We evaluated the studies overall and categorized the collected data into six geographical regions and three economic regions. Results: The mean (SD) PM2.5 concentration, weighted by the number of sampling days, was 60.64 (33.27) µg/m3 for all geographic regions and 71.99 (30.20) µg/m3 for all economic regions. A one-way ANOVA shows statistically significant differences in PM2.5 concentrations between the various geographic regions (F = 14.91, p < 0.0001) and the three economic regions (F = 4.55, p = 0.01). Conclusions: This review identifies quantifiable differences in fine particulate matter concentrations across regions of China. The highest levels of fine particulate matter were found in the northern and northwestern regions and especially Beijing. The high percentage of data points exceeding current federal regulation standards suggests that fine particulate matter pollution remains a huge problem for China. As pre-2013 emissions data remain largely unavailable, we hope that the data aggregated from this systematic review can be incorporated into current and future models for more accurate historical PM2.5 estimates

A study of two bispectral features from envelope signals for bearing fault diagnosis

: To accurately detect and diagnose bearing faults, bispectral analysis has received more attention recently because of its unique property of noise reduction and nonlinearity extraction. Particularly this study investigates two typical bispectra: conventional bispectrum (CB) and modulation signal bispectrum (MSB) for suppressing noise influences in envelope signals and hence obtaining more accurate diagnostic features. The first component from the diagonal slice of CB results and that of the subdiagonal slices of MSB results are taken as the diagnostic features considering effective inclusion of information and easy of computations. Simulative and experimental studies show that both MSB and CB features result in good diagnostic performances but MSB may outperform CB slightly in that it shows smaller variance in attaining the feature and more sensitive to weak fault signatures. This merit of MSB may be due to that the MSB feature has more diagnostic information as it is the combination of first three harmonics, whereas the CB feature is combined from just the first two harmonics

Analysis of the current status and influencing factors of cross-regional hospitalization services utilization by basic medical insurance participants in China − taking a central province as an example

BackgroundThe geographically uneven distribution of healthcare resources has resulted in a dramatic increase of cross-regional hospitalization services in China. The over-use of cross-regional hospitalization services may hinder the utilization and improvement of local hospitalization services. It is of great practical significance to study the utilization of cross-regional hospitalization services and its influencing factors in order to effectively allocate medical resources and guide patients to seek medical treatment rationally. Therefore, this study aims to analyze the current situation and influencing factors of the utilization of cross-regional hospitalization services by patients insured by basic medical insurance in China.MethodsA total of 3,291 cross-provincial inpatients were randomly selected in a central province of China in 2020. The level of medical institutions, hospitalization expenses and actual reimbursement rate were selected as indicators of hospitalization service utilization. Exploratory factor analysis was used to assess the dimensionality of influencing factors and reduce the number of variables, and binomial logistic regression analysis and multiple linear regression analysis to explore the influencing factors of the utilization of cross-regional hospitalization services.ResultsThe proportion of cross-provincial inpatients choosing tertiary hospitals was the highest with average hospitalization expenses of 24,662 yuan and an actual reimbursement rate of 51.0% on average. Patients insured by Urban Employees’ Basic Medical Insurance (UEBMI) were more frequently (92.9% vs. 88.5%) to choose tertiary hospitals than those insured by Urban and Rural Residents’ Basic Medical Insurance (URRBMI), and their average hospitalization expenses (30,727 yuan) and actual reimbursement rate (68.2%) were relatively higher (p &lt; 0.001). The factor “income and security,” “convenience of medical treatment” and “disease severity” had significant effects on inpatients’ selection of medical institution level, hospitalization expenses and actual reimbursement rate, while the factor “demographic characteristics” only had significant effects on hospitalization expenses and actual reimbursement rate.ConclusionCross-provincial inpatients choose tertiary hospitals more frequently, and their financial burdens of medical treatment are heavy. A variety of factors jointly affect the utilization of cross-provincial hospitalization services for insured patients. It is necessary to narrow down the gap of medical treatment between UEBMI and URRBMI patients, and make full use of high-quality medical resources across regions

Application of discrete event simulation in health care: a systematic review

Abstract Background The objective was to explore the current advances and extent of DES (Discrete Event Simulation) applied to assisting with health decision making, as well as to categorize the wide spectrum of health-related topics where DES was applied. Methods A systematic review was conducted of the literature published over the last two decades. Original research articles were included and reviewed if they concentrated on the topic of DES technique applied to health care management with model frameworks explicitly demonstrated. No restriction regarding the settings of DES application was applied. Results A total of 211 papers met the predefined inclusion criteria. The number of publications included increased significantly especially after 2010.101 papers (48%) stated explicitly disease areas targeted, the most frequently modeled of which are related to circulatory system, nervous system and Neoplasm. The DES applications were distributed unevenly into 4 major classes: health and care systems operation (HCSO) (65%), disease progression modeling (DPM) (28%), screening modeling (SM) (5%) and health behavior modeling (HBM) (2%). More than 68% of HCSO by DES were focused on specific problems in individual units. However, more attempts at modeling highly integrated health service systems as well as some new trends were identified. Conclusions DES technique has been an effective tool to approach a wide variety of health care issues. Among all DES applications in health care, health system operations research occupied the most considerable proportion and increased most significantly. Health Economic Evaluation (HEE) was the second most common topic for DES in health care, but with stable rather than increasing numbers of publications

An investigation of the current advancement of discrete event simulation in healthcare management and future development with consideration of environmental sustainability

In view of the versatility of and growing attention paid to discrete event simulations (DES) in the healthcare sector, this dissertation is intended to investigate the current application and future implication of discrete event simulations in support of evidenced-based healthcare decision-making. To do this, three pieces of scientific writing are prepared as three main lenses used to approach different aspects relating to DES applications in healthcare. A systematic review was carried out in module 1 to provide an up-to-date overview of the current development and newly emerging trends of healthcare-related DES models. Given few reviews critically assessing the reporting quality of these modeling studies and a lack of suitable appraisal instruments available, an 18-item appraisal checklist was developed in module 2 covering model conceptualization, parameterization and uncertainty assessment, validation, generalizability, and stakeholder involvement, and applied to the total of 211 DES studies included in module 1. Given that environmental factors are rarely touched on by DES studies, a novel framework applying German diagnosis-related-group cost data to estimating carbon footprints of hospital care pathways was developed in module 3 and could serve as a preparatory work for the integration of environmental impacts into the future development of healthcare-related DES models. Overall, this dissertation presents the current advancement, new trends and reporting quality of DES as a decision-analytic model in healthcare, and formulates an estimation framework for hospital care carbon footprinting which facilitates integrating environmental performance into DES modeling studies

Fine Particulate Matter Concentrations in Urban Chinese Cities, 2005–2016: A Systematic Review

Background: Particulate matter pollution has become a growing health concern over the past few decades globally. The problem is especially evident in China, where particulate matter levels prior to 2013 are publically unavailable. We conducted a systematic review of scientific literature that reported fine particulate matter (PM2.5) concentrations in different regions of China from 2005 to 2016. Methods: We searched for English articles in PubMed and Embase and for Chinese articles in the China National Knowledge Infrastructure (CNKI). We evaluated the studies overall and categorized the collected data into six geographical regions and three economic regions. Results: The mean (SD) PM2.5 concentration, weighted by the number of sampling days, was 60.64 (33.27) μg/m3 for all geographic regions and 71.99 (30.20) μg/m3 for all economic regions. A one-way ANOVA shows statistically significant differences in PM2.5 concentrations between the various geographic regions (F = 14.91, p &lt; 0.0001) and the three economic regions (F = 4.55, p = 0.01). Conclusions: This review identifies quantifiable differences in fine particulate matter concentrations across regions of China. The highest levels of fine particulate matter were found in the northern and northwestern regions and especially Beijing. The high percentage of data points exceeding current federal regulation standards suggests that fine particulate matter pollution remains a huge problem for China. As pre-2013 emissions data remain largely unavailable, we hope that the data aggregated from this systematic review can be incorporated into current and future models for more accurate historical PM2.5 estimates

A Novel Condition Monitoring Method of Wind Turbines Based on GMDH Neural Network

The safety of power transmission systems in wind turbines is crucial to the wind turbine’s stable operation and has attracted a great deal of attention in condition monitoring of wind farms. Many different intelligent condition monitoring schemes have been developed to detect the occurrence of defects via supervisory control and data acquisition (SCADA) data, which is the most commonly applied condition monitoring system in wind turbines. Normally, artificial neural networks are applied to establish prediction models of the wind turbine condition monitoring. In this paper, an alternative and cost-effective methodology has been proposed, based on the group method of data handling (GMDH) neural network. GMDH is a kind of computer-based mathematical modelling and structural identification algorithm. GMDH neural networks can automatically organize neural network architecture by heuristic self-organization methods and determine structural parameters, such as the number of layers, the number of neurons in hidden layers, and useful input variables. Furthermore, GMDH neural network can avoid over-fitting problems, which is a ubiquitous problem in artificial neural networks. The effectiveness and performance of the proposed method are validated in the case studies

Diagnosis of Chronic Musculoskeletal Pain by Using Functional Near-Infrared Spectroscopy and Machine Learning

Chronic pain (CP) has been found to cause significant alternations of the brain’s structure and function due to changes in pain processing and disrupted cognitive functions, including with respect to the prefrontal cortex (PFC). However, until now, no studies have used a wearable, low-cost neuroimaging tool capable of performing functional near-infrared spectroscopy (fNIRS) to explore the functional alternations of the PFC and thus automatically achieve a clinical diagnosis of CP. In this case-control study, the pain characteristics of 19 chronic pain patients and 32 healthy controls were measured using fNIRS. Functional connectivity (FC), FC in the PFC, and spontaneous brain activity of the PFC were examined in the CP patients and compared to those of healthy controls (HCs). Then, leave-one-out cross-validation and machine learning algorithms were used to automatically achieve a diagnosis corresponding to a CP patient or an HC. The current study found significantly weaker FC, notably higher small-worldness properties of FC, and increased spontaneous brain activity during resting state within the PFC. Additionally, the resting-state fNIRS measurements exhibited excellent performance in identifying the chronic pain patients via supervised machine learning, achieving F1 score of 0.8229 using only seven features. It is expected that potential FC features can be identified, which can thus serve as a neural marker for the detection of CP using machine learning algorithms. Therefore, the present study will open a new avenue for the diagnosis of chronic musculoskeletal pain by using fNIRS and machine learning techniques