Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model.However,due to the complexity of the milling system structure and the uncertainty of the milling failure index,it is often impossible to construct model expert knowledge effectively.Therefore,a milling system fault detection method based on fault tree analysis and hierarchical BRB(FTBRB)is proposed.Firstly,the proposed method uses a fault tree and hierarchical BRB modeling.Through fault tree analysis(FTA),the logical correspondence between FTA and BRB is sorted out.This can effectively embed the FTA mechanism into the BRB expert knowledge base.The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion.Secondly,evidence reasoning(ER)is used to ensure the transparency of the model reasoning process.Thirdly,the projection covariance matrix adaptation evolutionary strategies(P-CMA-ES)is used to optimize the model.Finally,this paper verifies the validity model and the method’s feasibility techniques for milling data sets.

Change Point Analysis to Detect the Effect of Pruning Severity on Tree Growth

The effect of pruning severity on tree growth was analyzed by change point detection using segmented regression. The present study applied this analysis to a well-known published data set including diameter growth response, tree age, pruning severity and pretreatment crown size. First, multiple regression analysis was performed to assess the effect of tree age, pruning severity and pretreatment crown size on diameter growth response. Next, segmented regression analysis was performed to assess the effect of pruning severity on diameter growth response. The results of the multiple regression showed that diameter growth response was significantly influenced by pruning severity and pretreatment crown size. The results of the segmented regression showed that in the whole data set, an abrupt change toward a decrease in diameter growth response was detected at 25% of the live crown removed. However, in the group of fully crowned and open-grown, diameter growth response continuously decreased with increasing pruning severity with no significant abrupt change, whereas in the group of 70% - 90% live crown, diameter growth response did not significantly decrease up to the break point (53% crown removed) and then abruptly decreased. This may be the first study to show the numerical evaluation of the effect of pruning severity on tree growth by change point analysis.

Geospatial Analysis of Urban Heat Island Effects and Tree Equity

In recent decades, Urban Heat Island Effects have become more pronounced and more widely examined. Despite great technological advances, our current societies still experience great spatial disparity in urban forest access. Urban Heat Island Effects are measurable phenomenon that are being experienced by the world’s most urbanized areas, including increased summer high temperatures and lower evapotranspiration from having impervious surfaces instead of vegetation and trees. Tree canopy cover is our natural mitigation tool that absorbs sunlight for photosynthesis, protects humans from incoming radiation, and releases cooling moisture into the air. Unfortunately, urban areas typically have low levels of vegetation. Vulnerable urban communities are lower-income areas of inner cities with less access to heat protection like air conditioners. This study uses mean evapotranspiration levels to assess the variability of urban heat island effects across the state of Tennessee. Results show that increased developed land surface cover in Tennessee creates measurable changes in atmospheric evapotranspiration. As a result, the mean evapotranspiration levels in areas with less tree vegetation are significantly lower than the surrounding forested areas. Central areas of urban cities in Tennessee had lower mean evapotranspiration recordings than surrounding areas with less development. This work demonstrates the need for increased tree canopy coverage.

Attribute-driven Fuzzy Fault Tree Model for Adaptive Lubricant Failure Diagnosis

Lubricant diagnosis serves as a crucial accordance for condition-based maintenance(CBM)involving oil changing and wear examination of critical parts in equipment.However,the accuracy of traditional end-to-end diagnosis models is often limited by the inconsistency and random fluctuations in multiple monitoring indicators.To address this,an attribute-driven adaptive diagnosis method is developed,involving three attributes:physicochemical,contamination,and wear.Correspondingly,a fuzzy fault tree(termed FFT)-based model is constructed containing the logic correlations from monitoring indicators to attributes and to lubricant failures.In particular,inference rules are integrated to mitigate conflicts arising from the reverse degradation of multiple indicators.With this model,the lubricant conditions can be accurately assessed through rule-based reasoning.Furthermore,to enhance its intelligence,the model is dynamically optimized with lubricant analysis knowledge and monitoring data.For verification,the developed model is tested with lubricant samples from both the fatigue experiment and actual aero-engines.Fatigue experiments reveal that the proposed model can improve the lubricant diagnosis accuracy from 73.4%to 92.6%compared with the existing methods.While for the engine lubricant test,a high accuracy of 90%was achieved.

Fault Tree Analysis of the Reliability of Electric Vehicles in India

Innovations for electric vehicles have advanced quickly in latest decades. Large-scale business use of these vehicles is still constrained by reliability-related issues. By utilising fault tree (FT) and Monte Carlo simulation, a mathematical prototype is created that includes the reliability traits of all major electrical parts of the vehicle system, including the battery, motor, drive, controllers. The research demonstrates that by raising the component restoration rates, the vehicle’s survivability can be raised. A thorough discussion of this paradigm is provided, along with a presentation and analysis of the reliability estimations based on an electric vehicle. This research on the reliability design and maintenance of an electric vehicle can be supported by the ideas that are outlined in the paper. Additionally, the findings of this study may be helpful to those who build electric vehicle, especially when upgrading the components efficiency and planning for reliability increase.

A SIMULATED ANNEALING METHOD FOR FAULT TREE ANALYSIS

This paper presents an approximate algorithm based on simulated annealing to achieve the maximum probability of the minimal cut sets for a fault tree. Near optimal minimal cut sets and important sequence of the basic events are also solved by the method. Computer simulations show that the algorithm performs very well.

FAULT TREE ANALYSIS OF SPONTANEOUS COMBUSTION OF SULPHIDE ORES AND ITS RISK ASSESSMENT

A logic fault tree of mine spontaneous combustion of sulphide ores was built by the fault tree analysis (FTA) based on a lot of mechanism investigation of sulphide ore spontaneous combustion in more than ten mines and review of a great amount of relevant

Assessment of gas and dust explosion in coal mines by means of fuzzy fault tree analysis

During the past decade, coal dust and gas explosions have been the most two serious types of disasters in China, threatening the lives of miners and causing significant losses in terms of national property. In this paper, an evaluation model of coal dust and gas explosions was constructed based on a fuzzy fault tree by taking the Xingli Coal Mine as a research site to identify the risk factors of coal dust and gas explosions.Furthermore, the hazards associated with such explosions were evaluated for this particular coal mine.After completing an on-site investigation, the fuzzy probabilities of basic events were obtained through expert scoring, and these expert opinions were then aggregated as trapezoidal fuzzy numbers to calculate the degrees of importance of all basic events. Finally, these degrees of importance were sorted. According to the resulting order, the basic events with higher probabilities were determined to identify key hazards in the daily safety management of this particular coal mine. Moreover, effective measures for preventing gas and coal dust explosions were derived. The fuzzy fault tree analysis method is of high significance in the analysis of accidental coal mine explosions and provides theoretical guidance for improving the efficiency of coal mine safety management in a scientific and feasible manner.

Qualitative analysis for state/event fault trees using formal model checking

A state/event fault tree(SEFT)is a modeling technique for describing the causal chains of events leading to failure in software-controlled complex systems.Such systems are ubiquitous in all areas of everyday life,and safety and reliability analyses are increasingly required for these systems.SEFTs combine elements from the traditional fault tree with elements from state-based techniques.In the context of the real-time safety-critical systems,SEFTs do not describe the time properties and important timedependent system behaviors that can lead to system failures.Further,SEFTs lack the precise semantics required for formally modeling time behaviors.In this paper,we present a qualitative analysis method for SEFTs based on transformation from SEFT to timed automata(TA),and use the model checker UPPAAL to verify system requirements’properties.The combination of SEFT and TA is an important step towards an integrated design and verification process for real-time safety-critical systems.Finally,we present a case study of a powerboat autopilot system to confirm our method is viable and valid after achieving the verification goal step by step.

Fault Tree Analysis of Dust Suppression Mechanism in a Spray System with Wetting Agent

By using the fault tree analysis in reliability theory as the systematical analysis approach, the dust suppression mechanism in a spray system with wetting agent is shown in a logic tree and some graphical models. From these diagrams, all factors related to the spray system and their cause and effect relationship can be seen clearly. Based on the built logic tree, several mathematical models and new ideas for expressing the dust suppressing efficiency in the spray system are put forward. The significance of all factors related to the efficiency of suppressing dust is qualitatively described. Furthermore, the new concepts, such as, the effective reaction time between dust particle and droplet, the expansion phenomenon of laden dust droplet, the functions of volatile and the relative size distribution efficiency of wetting agent are presented. All this richenes the existing mechanism of dust abatement by spraying wetting agent. At last, several problems that need to be further investigated are also suggested in the paper.

Risk Assessment of Deep-Water Horizontal X-Tree Installation

Due to the high potential risk and many influencing factors of subsea horizontal X-tree installation,to guarantee the successful completion of sea trials of domestic subsea horizontal X-trees,this paper established a modular risk evaluation model based on a fuzzy fault tree.First,through the analysis of the main process oftree down and combining the Offshore&Onshore Reliability Data(OREDA)failure statistics and the operation procedure and the data provided by the job,the fault tree model of risk analysis of the tree down installation was established.Then,by introducing the natural language of expert comprehensive evaluation and combining fuzzy principles,quantitative analysis was carried out,and the fuzzy number was used to calculate the failure probability of a basic event and the occurrence probability of a top event.Finally,through a sensitivity analysis of basic events,the basic events of top events significantly affected were determined,and risk control and prevention measures for the corresponding high-risk factors were proposed for subsea horizontal X-tree down installation.

System Reliability Analysis Method Based on T-S FTA and HE-BN

For high-reliability systems in military,aerospace,and railway fields,the challenges of reliability analysis lie in dealing with unclear failure mechanisms,complex fault relationships,lack of fault data,and uncertainty of fault states.To overcome these problems,this paper proposes a reliability analysismethod based on T-S fault tree analysis(T-S FTA)and Hyper-ellipsoidal Bayesian network(HE-BN).The method describes the connection between the various systemfault events by T-S fuzzy gates and translates them into a Bayesian network(BN)model.Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation,a reliability modeling method is proposed that can fully reflect the fault characteristics of complex systems.Experts describe the degree of failure of the event in the form of interval numbers.The knowledge and experience of experts are fused with the D-S evidence theory to obtain the initial failure probability interval of the BN root node.Then,the Hyper-ellipsoidal model(HM)constrains the initial failure probability interval and constructs a HE-BN for the system.A reliability analysismethod is proposed to solve the problem of insufficient failure data and uncertainty in the degree of failure.The failure probability of the system is further calculated and the key components that affect the system’s reliability are identified.The proposedmethod accounts for the uncertainty and incompleteness of the failure data in complex multi-state systems and establishes an easily computable reliability model that fully reflects the characteristics of complex faults and accurately identifies system weaknesses.The feasibility and accuracy of the method are further verified by conducting case studies.

Analysis of superheater's pipe wall overtemperature by fault tree diagnose

After research on a 2000t/h subcritical forced-circulation balanced ventilation were applied boiler and the structure and operation of its auxiliary system builds up this heat transfer model of a superheater's pipe wall and analyze the effect of primary factors on the overtemperature of the pipe wall. Fault tree structure was used to uncover the multiplayer logic between the overtemperature of the superheater's pipe wall and the faults.

Reliability Analysis of Electrical System of CNC Machine Tool Based on Dynamic Fault Tree Analysis Method

The electrical system of CNC machine tool is very complex which involves many uncertain factors and dynamic stochastic characteristics when failure occurs.Therefore,the traditional system reliability analysis method,fault tree analysis(FTA)method,based on static logic and static failure mechanism is no longer applicable for dynamic systems reliability analysis.Dynamic fault tree(DFT)analysis method can solve this problem effectively.In this method,DFT first should be pretreated to get a simplified fault tree(FT);then the FT was modularized to get the independent static subtrees and dynamic subtrees.Binary decision diagram(BDD)analysis method was used to analyze static subtrees,while an approximation algorithm was used to deal with dynamic subtrees.When the scale of each subtree is smaller than the system scale,the analysis efficiency can be improved significantly.At last,the usefulness of this DFT analysis method was proved by applying it to analyzing the reliability of electrical system.

Identification of novel mammalian viruses in tree shrews(Tupaia belangeri chinensis)

The Chinese tree shrew(Tupaia belangeri chinensis),a member of the mammalian order Scandentia,exhibits considerable similarities with primates,including humans,in aspects of its nervous,immune,and metabolic systems.These similarities have established the tree shrew as a promising experimental model for biomedical research on cancer,infectious diseases,metabolic disorders,and mental health conditions.Herein,we used metatranscriptomic sequencing to analyze plasma,as well as oral and anal swab samples,from 105 healthy asymptomatic tree shrews to identify the presence of potential zoonotic viruses.In total,eight mammalian viruses with complete genomes were identified,belonging to six viral families,including Flaviviridae,Hepeviridae,Parvovirinae,Picornaviridae,Sedoreoviridae,and Spinareoviridae.Notably,the presence of rotavirus was recorded in tree shrews for the first time.Three viruses-hepacivirus 1,parvovirus,and picornavirus-exhibited low genetic similarity(<70%)with previously reported viruses at the whole-genome scale,indicating novelty.Conversely,three other viruses-hepacivirus 2,hepatovirus A and hepevirus-exhibited high similarity(>94%)to known viral strains.Phylogenetic analyses also revealed that the rotavirus and mammalian orthoreovirus identified in this study may be novel reassortants.These findings provide insights into the diverse viral spectrum present in captive Chinese tree shrews,highlighting the necessity for further research into their potential for crossspecies transmission.

Fault Tree Analysis:Investigation of Epidemic Hemorrhagic Fever Infection Acquired in Animal Laboratories in China

Epidemic hemorrhagic fever has been an ongoing threat to laboratory personnel involved in animal care and use. Laboratory transmissions and severe infections occurred over the past twenty years, even though the standards and regulations for laboratory biosafety have been issued, upgraded, and implemented in China. Therefore, there is an urgent need to identify risk factors and to seek effective preventive measures that can curb the incidences of epidemic hemorrhagic fever among laboratory personnel. In the present study, we reviewed literature that relevant to animals laboratory-acquired hemorrhagic fever infections reported from 1995 to 2015, and analyzed these incidences using fault tree analysis （FTA）.

Correlation analysis between fault frequency and service time of underground fluid instruments

In this paper, we make a statistical analysis of the fault information of the underground fluid instruments of 12 models in China from January 2021 to May 2022 based on the Pearson correlation coefficient, and compare the fault statistics of the meteorological three-element instruments of 3 models during the study period. The results show that:(1) The numbers of faults of the underground fluid instruments of 12models with different service times are basically positively correlated with the numbers of the corresponding instruments, with good consistency. Moreover, the automatic observation instruments(8models) with more than 30 units are significantly correlated at a 0.05 significance level(95% confidence level). Even at a 0.01 significance level(99% confidence level), there are 7 models(7/8) with significant correlation.(2) The positive and negative correlations between the monthly average number of faults and the corresponding service times of the underground fluid instruments of 12 models with different service times are random, and there are 9 models(75%) with no significant correlation at a 0.05 significance level(95% confidence level), while 12 models(100%) with no significant correlation at a 0.01significance level(99% confidence level).(3) The monthly average numbers of faults of the underground fluid instruments of 12 models are basically 0.02-0.05 times/(unit·month), and the overall fault frequency is low.(4) The fault statistics results of the meteorological three-element instruments of 3 models are consistent with the characteristics of the underground fluid instruments of 12 models. In general,there is no significant correlation between the fault frequency and the service time of underground fluid instruments.(5) The results of this paper demonstrate that the service time of underground fluid instruments cannot be taken as the main reason for whether to update the instruments. Similarly, the fault frequency of the instruments cannot be taken as the main reason for the service life of the instruments in the process of formulating the service life standards of underground fluid instruments.

Fault Tree Analysis of Fire and Explosion Accidents for Dual Fuel （Diesel/Natural Gas） Ship Engine Rooms

In recent years, China's increased interest in environmental protection has led to a promotion of energy-efficient dual fuel(diesel/natural gas) ships in Chinese inland rivers. A natural gas as ship fuel may pose dangers of fire and explosion if a gas leak occurs. If explosions or fires occur in the engine rooms of a ship, heavy damage and losses will be incurred. In this paper, a fault tree model is presented that considers both fires and explosions in a dual fuel ship; in this model, dual fuel engine rooms are the top events. All the basic events along with the minimum cut sets are obtained through the analysis.The primary factors that affect accidents involving fires and explosions are determined by calculating the degree of structure importance of the basic events.According to these results, corresponding measures are proposed to ensure and improve the safety and reliability of Chinese inland dual fuel ships.

Reliability Analysis of Hydraulic System of Anchor Drilling Rigs Based on Fuzzy Fault Tree

Considering that the fault phenomenon of the power head cannot feed under actual working conditions,fuzzy mathematics theory is introduced into fault tree analysis(FTA)according to the structural characteristics of hydraulic system of anchor drilling rigs in this paper.The triangle fuzzy number is used to describe the fault probability of the basic event,the fuzzy probability importance of the basic event is calculated,and the basic events are sorted by comparing the magnitude of the fuzzy probability importance.The results show that the gear wear of auxiliary oil pump,suction phenomena of gear pump,wear and leakage of No.1 and No.3 pumps are the key factors leading to power head failure.In order to improve the overall reliability of the hydraulic system,fault diagnosis and maintenance decisions provide a theoretical basis.

基于TREE-LSTM算法的船舶汽轮机组变负荷故障诊断

针对船舶汽轮机组变负荷过程故障诊断中的耦合参数时序特征难以捕捉以及正常参数变动的干扰等问题,引入TREE-LSTM神经网络模型以实现复杂非线性系统动态数据分类。首先建立某船舶汽轮机组仿真模型,分析并进行故障仿真;随后进行数据预处理与特征工程;最后训练TREE-LSTM模型进行故障诊断,并与SVM、LSTM等模型进行比较。TREE-LSTM模型对于船舶汽轮机组变负荷过程的故障诊断正确率为98.7%,正确率最高。由于引入时间序列与复杂神经网络拓扑结构,TREE-LSTM在处理非线性系统动态数据分类问题时效果更好。