Development of Fault Diagnosis System for Spacecraft Based on Fault Tree and G2

Some ideas in the development of fault diagnosis system for spacecraft are introduced. Firstly, the architecture of spacecraft fault diagnosis is proposed hierarchically with four diagnosis frames, i.e., system level, subsystem level, component level and element level. Secondly, a hierarchical diagnosis model is expressed with four layers, i.e., sensors layer, function layer, behavior layer and structure layer. These layers are used to work together to accomplish the fault alarm, diagnosis and localization. Thirdly, a fault-tree-oriented hybrid knowledge representation based on frame and generalized rule and its relevant reasoning strategy is put forward. Finally, a diagnosis case for spacecraft power system is exemplified combining the above with a powerful expert system development tool G2.

Updated roles of the gut microbiota in exploring shrimp etiology, polymicrobial pathogens, and disease incidence

Litopenaeus vannamei is the most extensively cultured shrimp species globally,recognized for its scale,production,and economic value.However,its aquaculture is plagued by frequent disease outbreaks,resulting in rapid and massive mortality.etiological research often lags behind the emergence of new diseases,leaving the causal agents of some shrimp diseases unidentified and leading to nomenclature based on symptomatic presentations,especially in cases involving co-and polymicrobial pathogens.Comprehensive data on shrimp disease statuses remain limited.In this review,we summarize current knowledge on shrimp diseases and their effects on the gut microbiome.Furthermore,we also propose a workflow integrating primary colonizers,“driver”taxa in gut networks from healthy to diseased states,disease-discriminatory taxa,and virulence genes to identify potential polymicrobial pathogens.We examine both abiotic and biotic factors(e.g.,external and internal sources and specific-disease effects)that influence shrimp gut microbiota,with an emphasis on the“holobiome”concept and common features of gut microbiota response to diverse diseases.After excluding the effects of confounding factors,we provide a diagnosis model for quantitatively predicting shrimp disease incidence using disease common-discriminatory taxa,irrespective of the causal agents.Due to the conservation of functional genes used in designing specific primers,we propose a practical strategy applying qPCR-assayed abundances of disease common-discriminatory functional genes.This review updates the roles of the gut microbiota in exploring shrimp etiology,polymicrobial pathogens,and disease incidence,offering a refined perspective for advancing shrimp aquaculture health management.

FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox＇s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

Hydraulic metal structure health diagnosis based on data mining technology

In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.

2D-HIDDEN MARKOV MODEL FEATURE EXTRACTION STRATEGY OF ROTATING MACHINERY FAULT DIAGNOSIS

A new feature extraction method based on 2D-hidden Markov model（HMM） is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.

USING STRUCTURE INFORMATION TO GUIDE DIAGNOSIS

For the Purpose of obtaining the best measurements quickly in 'model based diagnosis', we constructed binary structure models, which contain only the components of candidates. The relative causality between the components of the models is the same as the faulty device. Candidates are classified by structure information. The models can be directly applied to either a discrete or an analog device without any additional processing. Then a half split method is set forward, which gives an optimal measurement within O(N 2)′s computations. The algorithm used in this paper can be regarded as the extremal structure characteristics of de Kleer′s expected entropy algorithm.

The Effectiveness of a Multidisciplinary Collaborative Model for the Diagnosis and Treatment of Vertigo

Objective:To investigate the effectiveness of applying a multidisciplinary collaborative model for the diagnosis and treatment of patients with vertigo.Methods:The study was carried out in Xianyang Hospital of Yan’an University,in which 100 patients with vertigo were selected from April 2021 to April 2022 and were divided into two groups:the control group was under the single diagnosis and treatment model,whereas the experimental group was under the multidisciplinary collaborative diagnosis and treatment model,with 50 cases in each group.The diagnostic effects of the two groups were compared.Results:The diagnostic and therapeutic efficiency of the patients in the experimental group were 94%and 98%,respectively,while those of the patients in the control group were 78%and 82%,respectively,with a significant difference between the two groups(p<0.05).The balance scores of the patients in both groups were low before the treatment,in which the difference was not significant(p>0.05);after the treatment,the scores improved,with those of the patients in the experimental group being significantly higher than those in the control group(p<0.05).Moreover,the satisfaction rate of patients in the experimental group(98%)was significantly higher than that of the control group(80%)(p<0.05).Conclusion:The application of the multidisciplinary collaborative diagnosis and treatment model in the diagnosis of patients with vertigo is effective.The multidisciplinary model can improve clinical diagnosis,enhance the treatment effect,improve the clinical symptoms of patients,and increase the satisfaction of patient care.Hence,it is of high clinical application value.

Integrated analysis of human influenza A(H1N1)virus infectionrelated genes to construct a suitable diagnostic model

The genome characteristics and structural functions of coding proteins correlate with the genetic diversity of the H1N1 virus,which aids in the understanding of its underlying pathogenic mechanism.In this study,analyses of the characteristic of the H1N1 virus infection-related genes,their biological functions,and infection-related reversal drugs were performed.Additionally,we used multi-dimensional bioinformatics analysis to identify the key genes and then used these to construct a diagnostic model for the H1N1 virus infection.There was a total of 169 differently expressed genes in the samples between 21 h before infection and 77 h after infection.They were used during the protein-protein interaction(PPI)analysis,and we obtained a total of 1725 interacting genes.Then,we performed a weighted gene co-expression network analysis(WGCNA)on these genes,and we identified three modules that showed significant potential for the diagnosis of the H1N1 virus infection.These modules contained 60 genes,and they were used to construct this diagnostic model,which showed an effective prediction value.Besides,these 60 genes were involved in the biological functions of this infectious virus,like the cellular response to type I interferon and in the negative regulation of the viral life cycle.However,20 genes showed an upregulated expression as the infection progressed.Other 36 upregulated genes were used to examine the relationship between genes,human influenza A virus,and infection-related reversal drugs.This study revealed numerous important reversal drug molecules on the H1N1 virus.They included rimantadine,interferons,and shikimic acid.Our study provided a novel method to analyze the characteristic of different genes and explore their corresponding biological function during the infection caused by the H1N1 virus.This diagnostic model,which comprises 60 genes,shows that a significant predictive value can be the potential biomarker for the diagnosis of the H1N1 virus infection.

Is laparoscopy equal to laparotomy in detecting and treating small bowel injuries in a porcine model?

AIM: To evaluate the safety and effectiveness of laparoscopy compared with laparotomy for diagnosing and treating small bowel injuries (SBIs) in a porcine model. METHODS: Twenty-eight female pigs were anesthetized and laid in the left recumbent position. The SBI model was established by shooting at the right lower quadrant of the abdomen. The pigs were then randomized into either the laparotomy group or the laparoscopy group. All pigs underwent routine exploratory laparotomy or laparoscopy to evaluate the abdominal injuries, particularly the types, sites, and numbers of SBIs. Traditional open surgery or therapeutic laparoscopy was then performed. All pigs were kept alive within the observational period (postoperative 72 h). The postoperative recovery of each pig was carefully observed. RESULTS: The vital signs of all pigs were stable within 1-2 h after shooting and none of the pigs died from gunshot wounds or SBIs immediately. The SBI model was successfully established in all pigs and definitively diagnosed with single or multiple SBIs either by exploratory laparotomy or laparoscopy. Compared with exploratory laparotomy, laparoscopy took a significantly longer time for diagnosis (41.27 ± 12.04 min vs 27.64 ± 13.32 min, P = 0.02), but the time for therapeutic laparoscopy was similar to that of open surgery. The length of incision was significantly reduced in the laparoscopy group compared with the laparotomy group (5.27 ± 1.86 cm vs 15.73 ± 1.06 cm, P < 0.01). In the final post-mortem examination 72 h after surgery, both laparotomy and laparoscopy offered a definitive diagnosis with no missed injuries. Postoperative complications occurred in four cases (three following laparotomy and one following laparoscopy, P = 0.326). The average recovery period for bowel function, vital appearance, and food re-intake after laparoscopy was 10.36 ± 4.72 h, 14.91 ± 3.14 h, and 15.00 ± 7.11 h, respectively. All of these were significantly shorter than after laparotomy (21.27 ± 10.17 h, P = 0.004; 27.82 ± 9.61 h, P < 0.001; and 24.55 ± 9.72 h, respectively, P = 0.016). CONCLUSION: Compared with laparotomy, laparoscopy offers equivalent efficacy for diagnosing and treating SBIs, and reduces postoperative complications as well as recovery period.

Application Study of Trajectory Plume Model in Atmospheric Environmental Impact Assessment

[ Objective] The research aimed to study application of the trajectory plume model in atmospheric environmental impact assessment. [ Method] Trajectory plume model was used to retrospectively evaluate regional atmospheric improvement degree by fuel gas desulfurization project in Mawan Power Plant of Shenzhen. On this basis, we analyzed applicability of the model in atmospheric prediction of the construction project. [- Re- sult~ Under the situation of complex flow field and variable weather condition, the trajectory plume model displayed good prediction accuracy, to- gether with the use of flow field diagnosis model. Under complex weather condition, this model could be complementary to atmospheric environmen- tal quality prediction model recommended by new atmosphere guidelines, which had the value of popularization in future atmospheric environmental evaluation and planning. [ Conduslon~ Trajectory plume model had broad application potential in atmospheric environmental impact assessment.

DiagDO: an efficient model based diagnosis approach with multiple observations

Model-based diagnosis(MBD)with multiple observations shows its significance in identifying fault location.The existing approaches for MBD with multiple observations use observations which is inconsistent with the prediction of the system.In this paper,we proposed a novel diagnosis approach,namely,the Diagnosis with Different Observations(DiagDO),to exploit the diagnosis when given a set of pseudo normal observations and a set of abnormal observations.Three ideas are proposed in this paper.First,for each pseudo normal observation,we propagate the value of system inputs and gain fanin-free edges to shrink the size of possible faulty components.Second,for each abnormal observation,we utilize filtered nodes to seek surely normal components.Finally,we encode all the surely normal components and parts of dominated components into hard clauses and compute diagnosis using the MaxSAT solver and MCS algorithm.Extensive tests on the ISCAS'85 and ITC'99 benchmarks show that our approach performs better than the state-of-the-art algorithms.

A novel machine learning-assisted clinical diagnosis support model for early identification of pancreatic injuries in patients with blunt abdominal trauma:a cross-national study

Background:The recognition of pancreatic injury in blunt abdominal trauma is often severely delayed in clinical practice.The aim of this study was to develop a machine learning model to support clinical diagnosis for early detection of abdominal trauma.Methods:We retrospectively analyzed of a large intensive care unit database(Medical Information Mart for Intensive Care[MIMIC]-IV)for model development and internal validation of the model,and performed outer validation based on a cross-national data set.Logistic regres-sion was used to develop three models(PI-12,PI-12-2,and PI-24).Univariate and multivariate analyses were used to determine variables in each model.The primary outcome was early detection of a pancreatic injury of any grade in patients with blunt abdominal trauma in the first 24 hours after hospitalization.Results:The incidence of pancreatic injuries was 5.56%(n=18)and 6.06%(n=6)in the development(n=324)and internal validation(n=99)cohorts,respectively.Internal validation cohort showed good discrimination with an area under the receiver operator characteristic curve(AUC)value of 0.84(95%confidence interval[CI]:0.71–0.96)for PI-24.PI-24 had the best AUC,specificity,and positive predictive value(PPV)of all models,and thus it was chosen as the final model to support clinical diagnosis.PI-24 performed well in the outer validation cohort with an AUC value of 0.82(95%CI:0.65–0.98),specificity of 0.97(95%CI:0.91–1.00),and PPV of 0.67(95%CI:0.00–1.00).Conclusion:A novel machine learning-based model was developed to support clinical diagnosis to detect pancreatic injuries in patients with blunt abdominal trauma at an early stage.

Traditional Chinese medicine diagnosis and treatment based on systematics

Background:Traditional Chinese Medicine(TCM)is a well-established medical system with a long history.However,the overall concept and systematic thinking in TCM are not comprehensively understood and applied in its inheritance and development.Objective This study aims to provide a basic theory for TCM diagnosis using systematics as the guiding principle.Using modern scientific and technological achievements,we aim to explore a new TCM diagnosis method.Methods:We analyzed previous studies on TCM diagnosis and treatment,and reviewed clinical research on TCM diagnosis and treatment from the viewpoint of systematics.We propose a new process model based on systematics for TCM diagnosis and treatment.This is a generalized model that summarizes the process of“establishing an image to express meaning”.Results:The proposed model was implemented in the clinical practice of TCM.We monitored the detailed treatment process of patients in the Department of Liver Diseases at Beijing Hospital.One patient underwent a treatment program that lasted 1 year and 45 days,consisting of 12 iterative cycles,each guided by the proposed diagnostic model and tailored to the patient's evolving condition.This case study validates the effectiveness of the proposed model in the diagnosis and treatment of liver disease in TCM.The therapeutic efficacy has been validated through the examination of both TCM indicators and Western medical auxiliary parameters.Among these,the TCM indicators consist of 2 components:tongue diagnosis and pulse diagnosis.Meanwhile,the Western medical auxiliary indicators encompass a range of assessments,including whole blood cell analysis,professional liver function examination,a series of liver function assessments,a high-sensitivity hepatitis B pentathlete test,as well as color Doppler ultrasound evaluations of the liver,bile duct,pancreas,spleen,and assessments of liver elasticity,among other related examination parameters.In conclusion,it is evident that the syndrome of liver depression and spleen deficiency has significantly diminished,and the viral load has decreased to levels below the detectable threshold,thereby confirming the restoration of normal liver function.These findings indicate that the disease is now under control.Conclusions:In this study,we applied the guiding principle of systematics to the study of TCM diagnosis and treatment,and combined it with modern medical technology.We proposed a TCM diagnosis and treatment process model,and a TCM model to establish an image,which can effectively support the diagnosis and treatment of TCM diseases.We illustrated the effectiveness of these models by applying them to TCM liver disease.

DIAGNOSABILITY OF CAYLEY GRAPH NETWORKS GENERATED BY TRANSPOSITION TREES UNDER THE COMPARISON DIAGNOSIS MODEL

Diagnosability of a multiprocessor system is one important study topic.Cayley graph network Cay（Tn,Sn） generated by transposition trees Tnis one of the attractive underlying topologies for the multiprocessor system.In this paper,it is proved that diagnosability of Cay（Tn,Sn） is n-1 under the comparison diagnosis model for n ≥ 4.