Fault diagnosis of high-voltage pulse track circuit 2 based on IHHO-KELM

Aiming at the problem of low fault diagnosis accuracy of high-voltage pulse track circuit, a track circuit fault diagnosis method based on kernel extreme learning machine (KELM) optimized by improved Harris hawks optimization (IHHO) is proposed. Firstly, in order to improve the optimization performance, the logarithmic convergence factor is used in combination with the symbiotic organisms search (SOS) algorithm to improve the basic Harris hawks optimization (HHO) algorithm. The benchmark test function is used for the experiment, which proves that the IHHO algorithm performs better in convergence accuracy and convergence speed. Secondly, the IHHO algorithm is used to optimize the kernel function parameter and penalty coefficient of the KELM model and then improves the fault diagnosis accuracy of the KELM model. Finally, the IHHO-KELM model is used to diagnose fault types of the high-voltage pulse track circuit. The experimental results show that the diagnostic accuracy of the proposed IHHO-KELM model is 93.3%, which is 6.6%, 5%, and 3.3% higher than that of the KELM model, GA (Genetic algorithm)-KELM model, and HHO-KELM model respectively. Further experiments verify that the IHHO-KELM model is superior to BP neural network, deep confidence network (DBN), long short-term memory (LSTM) network, and gated recurrent unit (GRU) network in terms of diagnostic accuracy, training time, and diagnostic mean square error, which proves the rapidity and stability of the IHHO-KELM model in track circuit fault diagnosis

Prediction of the Remaining Useful Life of a Switch Machine, Based on Multi-Source Data

Aimed at the shortcomings of a single feature to characterize the health status and accurately predict the remaining life span of the equipment, a prediction method for a switch machine, based on the weighted Mahalanobis distance (WDMD), is proposed. The method consists of two parts: the construction of a health indicator, based on the weighted Markov distance and the prediction of the remaining useful life, based on the hidden Markov model (HMM). Firstly, a kernel principal component analysis (KPCA) is used to extract the characteristics of the power curve data of the switch machine, and the characteristics with a high correlation with the degradation process are screened, according to the trend indicators. Secondly, the resulting features are combined with multi-source information, as the input, and a comprehensive health indicator (HI) is constructed by the weighted fusion of the WDMD algorithm, to characterize the degradation process of the switch machine. The degradation model of this HI is established and trained by the HMM, so as to predict the remaining life span of the equipment. Finally, the actual operation data of the railway field is selected to verify the prediction method proposed in the paper. The results show that the state recognition and the life prediction accuracy of the proposed method is higher, which can provide effective opinions for the predictive maintenance of the switch machine equipment

A meta-analysis of cognitive and functional outcomes in severe brain trauma cases

BackgroundSevere traumatic brain injuries (TBIs) are an important health issue worldwide, which are associated with harmful side effects. This meta-analysis investigates the cognitive and functional outcomes in severe brain trauma cases. It assesses the impact on memory, verbal and visual abilities, attention, learning, and the presence of depression. The study provides a comprehensive overview of the consequences of severe brain trauma injury on cognitive and functional domains.ObjectiveThe main objective of the current comprehensive meta-analysis study is to assess and analyze the impact of severe TBI on functional and cognitive outcomes, including verbal, visual, attention, learning, memory, and emotional stability.MethodsWe collected data from three online databases, including PubMed, Cochrane Library, and Embase. Case–control trials related to severe TBI association with cognitive and functional outcomes were included. Verbal strength, visual functions, learning abilities, attention, memory, and depression were considered primary outcomes.ResultsWe have included 13 case–control studies with 1,442 subjects in this meta-analysis, which provide adequate data to determine the pooled effect size for targeted outcomes. The effect of severe TBI on the inducement of depression and impairment of memory, verbal, visual, attention, and learning abilities compared to the control group showed statistically significant outcomes (p < 0.05).ConclusionSevere TBI is strongly associated with impaired cognitive and functional abilities, including visual and verbal disabilities, impaired memory, depression inducement, attention deficits, and learning disabilities

Association between Baseline Fasting Plasma Glucose Levels and Risk of Acute Pancreatitis in Non-obese Population: a Prospective Cohort Study

Background Previous studies have shown that the risk of acute pancreatitis (AP) is increased in obesity population, while obese patients are often combined with abnormal fasting plasma glucose (FPG). It still remians controversial whether FPG independently increases the risk of AP and the relationship between FPG and the risk of AP in non-obese patients has been rarely reported in China and abroad. Objective To explore the association between baseline FPG level and the risk of AP in non-obese population. Methods Using a prospective cohort study method, a total of 102 512 non-obese cases from the Kailuan study cohort who completed physical examination for the first time in KaiLuan General Hospital and its 10 affiliated hospitals from 2006 to 2009 were enrolled as study subjects. Epidemiological data, anthropometric data, laboratory test indicators and other information of the subjects were collected. The study subjects were divided into 4 groups according to the FPG quartile: the first quartile group (group Q1, FPG≤4.66 mmol/L, n=25 929) ; the second quartile group (group Q2, 4.66 mmol/L≤FPG<5.10 mmol/L, n=25 797) ; the third quartile group (group Q3, 5.10 mmol/L≤FPG<5.67 mmol/L, n=25 162) ; the fourth quartile group (group Q4, FPG≥5.67 mmol/L, n=25 624). The Kaplan-Meier method was used to plot the survival curves of new-onset AP in non-obese population. The cumulative incidence of AP in non-obese population in different FPG level groups were calculated and Log-rank method was used for inter-group test. The Cox proportional hazard regression model was used to analyze the influencing factors for the new-onset AP in non-obese population and the correlation between different FPG level groupings and new-onset AP in non-obese population. Results The median follow-up time in this study was (12.8±2.4) years with the cumulative incidence of 320 cases and incidence density of 2.44 cases per 10 000 person-years in AP. There were statistically significant differences in the cumulative incidence of AP among the 4 FPG level groups (χ2=13.96, P<0.001). The results of Cox proportional hazard regression analysis showed that advanced age〔HR=1.02, 95%CI (1.01, 1.03), P=0.001〕, high triacylglycerol (TG) level〔HR=1.22, 95%CI (1.13, 1.30), P<0.001〕, history of cholithiasis〔HR=2.79, 95%CI (1.88, 4.13), P<0.001〕were risk factors for new-onset AP in non-obese population. Years of education ≥9 years〔HR=0.65, 95%CI (0.47, 0.90), P<0.001〕was the protective factor for new-onset AP in non-obese population. The HR for new-onset AP in group Q4 was 1.40 〔95%CI (1.02, 1.92), P=0.038〕. After excluding the population applying hypoglycemic drugs, the conclusions were unchanged, the HR for new-onset AP in group Q4 was 1.40 〔95%CI (1.02, 1.92), P=0.036〕. Conclusion Advanced age, high TG levels, and history of cholithiasis are risk factors for new-onset AP, years of education ≥9 years is the protective factor for new-onset AP. And the risk of AP increases when FPG ≥5.67 mmol/L in non-obose population