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.

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.

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.

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.

Diagnosabilities of exchanged hypercube networks under the pessimistic one-step diagnosis strategy

The exchanged hypercube EH（s, t） （where s ≥ 1 and t ≥ 1） is obtained by systematically reducing links from a regular hypercube Q,＋t＋l. One-step diagnosis of exchanged hypercubes which involves only one testing phase during which processors test each other is discussed. The diagnosabilities of exchanged hypercubes are studied by using the pessimistic one-step diagno- sis strategy under two kinds of diagnosis models： the PMC model and the MM model. The main results presented here are the two proofs that the degree of diagnosability of the EH（s, t） under pessimistic one-step tl/tl fault diagnosis strategy is 2s where I ≤ s ≤ t （respectively, 2t, where 1 ≤ t ≤ s） based on the PMC model and that it is also 2s where 1 ≤ s ≤ t （respectively, 2t, where 1 ≤ t ≤ s） based on the MM＊ model.

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.

飞机全生命期过程建模方法与技术

论述了飞机全生命期过程建模的必要性,介绍了针对飞机这样的大型复杂过程的建模技术,已 开发的产品过程建模和模拟软件工具,以及该工具用于某型号飞机研制过程的建模情况。

基于 Chaw & Hakimi 模型的诊断算法

首次把系统级故障诊断中的集团概念引入Ｃｈａｗ＆Ｈａｋｉｍｉ模型中，并由此对满足Ｋ（Ｇ）≥ｔ的ｔ－可诊断系统提出ｔ－可诊断算法，对任意测试图首次通过求构造函数最小值的方法得到最优诊断算法．

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.

A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.

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.

MECHANISM ANALYSIS AND MATHEMATICAL DESCRIPTION OF MECHANICAL ABRUPT FAULTS

Gradual faults and abrupt faults of mechanical systems are differentiated by variation patterns of state varibles.After the reasons of abrupt faults are analyzed by nonlinear dynamics,two methods establishing the mathematical models for abrupt faults are presented.As an example,a cusp catastrophe model of the vibration amplitudes of a Jeffcott rotor with nonlinear stiffness and its parameter condition of the vibration state catastrophe are obtained.

A novel approach to equipment health management based on auto-regressive hidden semi-Markov model(AR-HSMM)

As a new maintenance method, CBM （condition based maintenance） is becoming more and more important for the health management of complicated and costly equipment. A prerequisite to widespread deployment of CBM technology and prac- tice in industry is effective diagnostics and prognostics. Recently, a pattern recog- nition technique called HMM （hidden Markov model） was widely used in many fields. However, due to some unrealistic assumptions, diagnositic results from HMM were not so good, and it was difficult to use HMM directly for prognosis. By relaxing the unrealistic assumptions in HMM, this paper presents a novel approach to equip- ment health management based on auto-regressive hidden semi-Markov model （AR-HSMM）. Compared with HMM, AR-HSMM has three advantages： 1） It allows explicitly modeling the time duration of the hidden states and therefore is capable of prognosis. 2） It can relax observations＇ independence assumption by accom- modating a link between consecutive observations. 3） It does not follow the unre- alistic Markov chain＇s memoryless assumption and therefore provides more pow- erful modeling and analysis capability for real problems. To facilitate the computa- tion in the proposed AR-HSMM-based diagnostics and prognostics, new forward- backward variables are defined and a modified forward-backward algorithm is de- veloped. The evaluation of the proposed methodology was carried out through a real world application case study： health diagnosis and prognosis of hydraulic pumps in Caterpillar Inc. The testing results show that the proposed new approach based on AR-HSMM is effective and can provide useful support for the decision- making in equipment health management.

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.