Knowledge Base For Diagnosis Research Articles

Ontology construction of traditional Chinese medicine diagnosis and treatment for myopia

Objective: To keep up with the progress of treatment for myopia in traditional Chinese medicine (TCM) through literature research, so as to determine data source of TCM diagnosis and treatment for myopia, from which entities are extracted for integration of relevant knowledge, and further formal expression and visualization of knowledge. Methods: Literature research and comparative analysis were used to determine the data source, and the seven-step method of constructing ontology was referred to for extraction of TCM diagnosis and treatment entities, which then was terminalized for development of classes and class hierarchy, and class properties, as well as creation of instances before consistency test of the ontology. Results: The ontology of TCM diagnosis and treatment for myopia includes 26 classes and 124 instances, 9 object properties and 1 data property. Conclusion: The combination of ontology technology and TCM diagnosis and treatment knowledge of myopia contributes to the construction of knowledge base of TCM diagnosis and treatment ontology for myopia, which realizes formal expression and visualization of relevant TCM knowledge, lays a foundation for the construction of TCM top-level ontology and ophthalmology ontology, and facilitates knowledge reusing and sharing.

Using transfer learning-based causality extraction to mine latent factors for Sjögren's syndrome from biomedical literature

Understanding causality is a longstanding goal across many different domains. Different articles, such as those published in medical journals, disseminate newly discovered knowledge that is often causal. In this paper, we use this intuition to build a model that leverages causal relations to unearth factors related to Sjögren's syndrome from biomedical literature. Sjögren's syndrome is an autoimmune disease affecting up to 3.1 million Americans. Due to the uncommon nature of the illness, symptoms across different specialties coupled with common symptoms of other autoimmune conditions such as rheumatoid arthritis, it is difficult for clinicians to diagnose the disease timely. Due to the lack of a dedicated dataset for causal relationships built from biomedical literature, we propose a transfer learning-based approach, where the relationship extraction model is trained on a wide variety of datasets. We conduct an empirical analysis of numerous neural network architectures and data transfer strategies for causal relation extraction. By conducting experiments with various contextual embedding layers and architectural components, we show that an ELECTRA-based sentence-level relation extraction model generalizes better than other architectures across varying web-based sources and annotation strategies. We use this empirical observation to create a pipeline for identifying causal sentences from literature text, extracting the causal relationships from causal sentences, and building a causal network consisting of latent factors related to Sjögren's syndrome. We show that our approach can retrieve such factors with high precision and recall values. Comparative experiments show that this approach leads to 25% improvement in retrieval F1-score compared to several state-of-the-art biomedical models, including BioBERT and Gram-CNN. We apply this model to a corpus of research articles related to Sjögren's syndrome collected from PubMed to create a causal network for Sjögren's syndrome. The proposed causal network for Sjögren's syndrome will potentially help clinicians with a holistic knowledge base for faster diagnosis.

Study on the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng

To explore the methods of the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng. The medical case records data of Jiusheng was collected, the frequency statistic was analyzed based on Python3.8.6, complex network analysis was performed using Gephi9.2 software, community analysis was performed by the ancient and modern medical case cloud platform V2.3.5, and analysis and verification of correlation graph and weight graph were proceed by Neo4j3.5.25 image database. The disease systems with frequency≥10 % were surgery, ophthalmology and otorhinolaryngology, locomotor, digestive and respiratory systems. The diseases under the disease system were mainly carbuncle, arthritis, lumbar disc herniation and headache. The commonly used moxibustion methods were fumigating moxibustion, blowing moxibustion, direct moxibustion and warming acupuncture. The core prescription of points obtained by complex network analysis included Yatong point, Zhiyang(GV 9), Sanyinjiao(SP 6), Dazhui(GV 14), Zusanli(ST 36), Lingtai(GV 10), Xinshu(BL 15), Zhijian point and Hegu(LI 4), which were basically consistent with high-frequency points. A total of 6 communities were obtained by community analysis, corresponding to different diseases. Through the analysis of correlation graph, 13 pairs of strong association rule points were obtained. The correlation between Zhiyang(GV 9)-Dazhui(GV 14) and Yatong point-Lingtai(GV 10) was the strongest. The acupoints with high correlation with Yatong point were Zhiyang(GV 9), Lingtai(GV 10), Dazhui(GV 14), Zusanli(ST 36) and Sanyinjiao(SP 6). In the weight graph of the high-frequency disease system, the relationship of the first weight of the surgery system disease was fumigating moxibustion-carbuncle-Yatong point, and the relationship of the first weight of the ophthalmology and otorhinolaryngology system disease was blowing moxibustion-laryngitis-Hegu (LI 4). The results of correlation graph and weight graph are consistent with the results of data mining, which can be used as an effective way to study the knowledge base of moxibustion diagnosis and treatment in the future.

Study on the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng

To explore the methods of the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng. The medical case records data of Jiusheng was collected, the frequency statistic was analyzed based on Python3.8.6, complex network analysis was performed using Gephi9.2 software, community analysis was performed by the ancient and modern medical case cloud platform V2.3.5, and analysis and verification of correlation graph and weight graph were proceed by Neo4j3.5.25 image database. The disease systems with frequency≥10 % were surgery, ophthalmology and otorhinolaryngology, locomotor, digestive and respiratory systems. The diseases under the disease system were mainly carbuncle, arthritis, lumbar disc herniation and headache. The commonly used moxibustion methods were fumigating moxibustion, blowing moxibustion, direct moxibustion and warming acupuncture. The core prescription of points obtained by complex network analysis included Yatong point, Zhiyang(GV 9), Sanyinjiao(SP 6), Dazhui(GV 14), Zusanli(ST 36), Lingtai(GV 10), Xinshu(BL 15), Zhijian point and Hegu(LI 4), which were basically consistent with high-frequency points. A total of 6 communities were obtained by community analysis, corresponding to different diseases. Through the analysis of correlation graph, 13 pairs of strong association rule points were obtained. The correlation between Zhiyang(GV 9)-Dazhui(GV 14) and Yatong point-Lingtai(GV 10) was the strongest. The acupoints with high correlation with Yatong point were Zhiyang(GV 9), Lingtai(GV 10), Dazhui(GV 14), Zusanli(ST 36) and Sanyinjiao(SP 6). In the weight graph of the high-frequency disease system, the relationship of the first weight of the surgery system disease was fumigating moxibustion-carbuncle-Yatong point, and the relationship of the first weight of the ophthalmology and otorhinolaryngology system disease was blowing moxibustion-laryngitis-Hegu (LI 4). The results of correlation graph and weight graph are consistent with the results of data mining, which can be used as an effective way to study the knowledge base of moxibustion diagnosis and treatment in the future.探究周楣声《灸绳》医案灸法隐性知识显性化及其知识图谱构建的方法。收集《灸绳》医案数据，基于Python3.8.6进行频数统计分析，利用Gephi9.2软件进行复杂网络分析，登录古今医案云平台V2.3.5进行社团分析，并运用Neo4j3.5.25图数据库进行关联图谱和权值图谱的分析及验证。出现频率≥10%的疾病系统分别为外科、五官、运动、消化和呼吸系统。疾病系统下属疾病以痈、关节炎、腰椎间盘突出症、头痛为主。治疗常用灸法为熏灸、吹灸、直接灸和温针灸。复杂网络分析所得腧穴核心组方为压痛穴、至阳、三阴交、大椎、足三里、灵台、心俞、指尖、合谷，基本与高频腧穴相一致。社团分析共得到6个社团，分别与不同疾病相对应。关联图谱分析得到强关联规则穴对13条，至阳-大椎、压痛穴-灵台的关联性最强，与压痛穴关联性较高的腧穴为至阳、灵台、大椎、足三里、三阴交。高频疾病系统权值图谱中，外科系统疾病权值排名第一的关系为熏灸-痈-压痛穴，五官系统疾病权值排名第一的关系为吹灸-喉炎-合谷。关联图谱及权值图谱所得结论与数据挖掘结果相一致，可作为未来研究灸法诊疗知识库的有效途径。.

Expert system: use of CLIPS software to evaluate solar energy for residences and businesses

The use of energy is important in all sectors, considered essential in industrial, commercial and residential activities. In view of this and the environmental problems caused by energy sources with fossil fuel origin, methods of energy production with renewable sources have been increasingly addressed. Studies that estimate the impact and evaluations of solar technologies in homes are scarce. In this sense, the applied method addresses the use of CLIPS for the development of a knowledge base for preliminary diagnosis and evaluation of solar energy for homes and businesses, thus obtaining a prototype of an expert system, the method also approaches the use of artificial intelligence with a heuristic characteristic and its target audience is people with little or no knowledge in the area, thus contributing to the social impact. As a result, the developed programming helps people with little or no knowledge about solar energy and its residential application to have access and thus verify the applicability. It is worth noting that this is the first empirical study carried out in Brazil, a country of crucial importance for the development of solar energy.

Knowledge Graph Construction of Chronic Kidney Disease Diagnosis and Treatment with Traditional Chinese Medicine

Background. The purpose of this study is to construct a knowledge graph of chronic kidney disease (CKD) diagnosis and treatment with traditional Chinese medicine (TCM), reorganize its knowledge, and display it. It allows the inheritance, development, and utilization of CKD diagnosis and treatment experiences with TCM in a standard and scientific manner. Methods. First, we constructed a knowledge framework for TCM diagnosis and treatment on the basis of the Chinese Pharmacopoeia, government projected textbook, and the current TCM diagnosis and treatment standards. Then, we collected and sorted the electronic medical records of TCM inpatients, extracting and normalizing the diagnoses, symptoms, syndromes, prescriptions, and other diagnosis and treatment information, creating the knowledge base of TCM diagnosis and treatment for CKD. Finally, we stored TCM diagnosis and treatment CKD knowledge in Neo4j graph database, which refers to the knowledge framework and knowledge base. The frequent patterns and complex network knowledge mining methods are integrated to construct the TCM diagnosis and treatment CKD knowledge graph. Results. The knowledge graph of CKD diagnosis and treatment with TCM was constructed, including 807 nodes and 10476 relationships, which are 273 diagnoses, 130 symptoms, 34 syndromes, 370 Chinese herbal medicine (CHM) nodes, and 5483 diagnosis‐symptom, 1349 diagnosis‐syndrome, 3644 syndrome‐CHM relationships. Conclusion. The knowledge graph provides rich knowledge of TCM diagnosis and treatment of CKD, which is helpful to inherit the clinical experience of TCM diagnosis and treatment of CKD and assist clinical diagnosis and treatment of CKD.

Research on fault diagnosis of hot die forging multi-station feeding manipulator

In order to improve the efficiency of fault diagnosis of hot die forging multi-station feeding manipulator, according to the fault characteristics of the feeding manipulator, an expert system structure based on fault tree analysis and binary decision graph was designed, and a diagnosis method based on expert system was proposed. Firstly, according to the fault causes of multi-station feeding manipulator, the fault tree is established. Secondly, the binary decision graph is used to analyse the fault tree, acquire the diagnosis expert knowledge, and establish the knowledge base of fault diagnosis. Then, the binary decision graph is used to quantitatively analyse the fault tree, the importance of the minimum cut set is solved, and the rule priority is determined, so as to solve the rule conflict problem and improve the efficiency of fault diagnosis. Finally, a case study is conducted. The results prove the validity and feasibility of the fault diagnosis method proposed in this paper.

Application of Modern Multimedia and Sensing Technology in Fault Detection and Diagnosis of Hydraulic Agricultural Machinery

In order to solve the problem of fault diagnosis of agricultural machinery hydraulic system, the authors propose an application of modern multimedia and sensing technology in fault detection and diagnosis of hydraulic agricultural machinery. By analyzing the component faults and system faults of the hydraulic system of agricultural machinery, an expert system for fault diagnosis is constructed, and a knowledge base and inference engine suitable for fault diagnosis of the hydraulic system of agricultural machinery are designed. In order to further improve the diagnostic accuracy of the fault diagnosis expert system, a fault diagnosis based on sparse coding is designed, and the sparse coding fault diagnosis results are integrated with the expert system to improve the diagnostic accuracy. The experimental results show that after sparse coding fusion, the fault diagnosis accuracy can be improved to more than 91%. In conclusion, the model meets the requirements of fault diagnosis and puts forward a new idea for the fault diagnosis of agricultural machinery hydraulic system. Make the agricultural machinery and equipment industry develop healthily.

Type-2 Fuzzy Neural System for Diagnosis of Diabetes

Diabetes is a chronic disease that is characterized by insufficient production or utilization of insulin and a consequent high increase in blood sugar. Diagnosis of diabetes is a complex process and requires a high level of expertise. The disease is characterized by a set of signs and symptoms. Some of these symptoms are obtained through laboratory analysis. Creation of a knowledge base and automation of disease diagnosis are important and allow fast detection and treatment. Various techniques have been used to develop a high-accuracy system for the diagnosis of diabetes. Fuzzy logic is one of the appropriate methodologies for the development of such medical diagnostic systems. Several research studies have used fuzzy models to diagnose medical diseases due to the imprecision and uncertainty associated with medical data. Moreover, a high level of uncertainty in medical data requires a type-2 fuzzy system to handle these uncertainties and diagnose diabetes. The paper proposes the integration of a type-2 fuzzy system and neural networks for the diagnosis of diabetes. Using the structure of type-2 fuzzy neural network (T2FNN) and statistical data, the system’s design for the diagnosis of diabetes is performed. A number of simulations have been done in order to evaluate the performance of the designed system. The comparative results demonstrated the efficiency of using the T2FNN system in the diagnosis of diabetes. The physician can use the system for diabetes’ diagnosis.

Cohort Profile: Chinese Cervical Cancer Clinical Study.

Cervical cancer is the fourth most common cancer worldwide, but its incidence varies greatly in different countries. Regardless of incidence or mortality, the burden of cervical cancer in China accounts for approximately 18% of the global burden. The Chinese Cervical Cancer Clinical Study is a hospital-based multicenter open cohort. The major aims of this study include (i) to explore the associations of therapeutic strategies with complications as well as mid- and long-term clinical outcomes; (ii) to widely assess the factors which may have an influence on the prognosis of cervical cancer and then guide the treatment options, and to estimate prognosis using a prediction model for precise post-treatment care and follow-up; (iii) to develop a knowledge base of cervical clinical auxiliary diagnosis and prognosis prediction using artificial intelligence and machine learning approaches; and (iv) to roughly map the burden of cervical cancer in different districts and monitoring the trend in incidence of cervical cancer to potentially inform prevention and control strategies. Patients eligible for inclusion were those diagnosed with cervical cancer, whether during an outpatient visit or hospital admission, at 47 different types of medical institutions in 19 cities of 11 provinces across mainland China between 2004 and 2018. In a total, 63 926 patients with cervical cancer were enrolled in the cohort. Since the project inception, a large number of standardized variables have been collected, including epidemiological characteristics, cervical cancer-related symptoms, physical examination results, laboratory testing results, imaging reports, tumor biomarkers, tumor staging, tumor characteristics, comorbidities, co-infections, treatment and short-term complications. Follow-up was performed at least once every 6 months within the first 5 years after receiving treatment and then annually thereafter. At present, we are developing a cervical cancer imaging database containing Dicom files with data of computed tomography/magnetic resonance imaging examination. Additionally, we are also collecting original pathological specimens of patients with cervical cancer. Potential collaborators are welcomed to contact the corresponding authors, and anyone can submit at least one specific study proposal describing the background, objectives and methods of the study.

A self-learning and self-optimizing framework for the fault diagnosis knowledge base in a workshop

Abstract The knowledge base is an essential part of the fault diagnosis system, which is crucial to the performance of fault recognition. As the intelligence of the fault diagnosis system has made persistent advance, the increasing demands for diversity and dynamic update have posed challenges to the knowledge base. In this paper, a framework for the fault diagnosis knowledge base is proposed to address the challenges mentioned above. Firstly, a dynamic clustering model is designed using the proposed semi-supervised multi-spatial manifold clustering method to recognize attribute clusters and aggregate new types. When new types are added to this model, it is constantly updated to achieve the automatic evolution of the knowledge base for the diversity of fault. Then, a knowledge evolution model is established by the generative adversarial network algorithm to achieve self-learning and self-optimizing capabilities of the knowledge base. This method learns the distribution of knowledge elements and generates new knowledge elements to optimize the clustering model. Finally, a series of comparative experiments are carried out on bearing datasets to verify the validity of the mentioned framework and models. The comparison results indicate that the proposed method has better performance in fault diagnosis. This research can not only update the knowledge base, but also provide a feasible approach for designing an autonomous knowledge base with self-optimizing and self-learning capabilities.

A health management system for large vertical mill

The large vertical mill has complicated structure and tens of thousands of parts, which is a critical grinding equipment for slag and cinder. As large vertical mill always works in severe conditions, the on-line monitoring, timely fault diagnosis, and trend prediction are very important guarantees for the safe service and saving maintaining costs. To address this issue, the health management system for large vertical mill is developed. More specifically, in order to manage reservoirs of state-related running data, the intrinsic physic data, and diagnosis knowledge base, an entity-relationship-model-based database is first constructed. Based on the fault diagnosis reasoning of experts, the fault tree is developed and the fault diagnosis rules are derived. Especially, a hybrid condition prognosis method based on backtracking search optimization algorithm and neural network is developed, and in comparison with traditional back propagation neural network and ant colony neural network, the developed backtracking search optimization algorithm and neural network gets superior hybrid prediction performance in prediction accuracy and training efficiency. Finally, the health management system, including the functions of condition monitoring, fault diagnosis, and trend prediction for large vertical mill is implemented using Microsoft Visual Studio C # and Microsoft SQL Server.

Coal mill fault diagnosis based on Gaussian process regression

A typical operating set of equipment can be obtained through cluster analysis of historical data. Two state monitoring models for HP medium speed coal mill are established based on Gaussian process regression and the similarity index calculated by this model can be used for measuring the operating status of HP mills. Finally a method for fault diagnosis of HP mill based on Gaussian regression modelling is proposed combined with fault diagnosis knowledge base of this HP mill. Taking the HP medium speed mill of a 660MW thermal power unit as an example, the real operating data is collected and used for modelling and analysis. Results shows that the equipment parameter estimation calculated by Gaussian process regression is accurate. It can be used for early-warning and diagnosed of equipment fault and also for practical engineering application.

Plant Leaf Disease Detection Using Support Vector Machine

Abstract Agriculture has special importance in that it is a major source of food and clothing and is an important economic source for countries. Agriculture is affected by a variety of factors, biotic such as diseases resulting from bacteria, fungi, viruses and non-biotic such as water and temperature and other environmental factors. detection of these diseases requires people to expert in addition to a set of equipment and it is expensive in terms of time and money Therefore, the development of a computer based system that detection the diseases of plants is very helpful for farmers As well as to specialists in the field of plant protection. the proposed plant disease detection system consists of two phases, in the first phase we establish the knowledge base and this by introducing a set of training samples in a series of processing that include first use pre-processing techniques such cropping , resizing, fuzzy histogram equalization ,next extract a set of color and texture feature and used to great the knowledge base that used as training data for support vector machine classifier . In the second phase of the work we use the classifier that was trained using the knowledge base for detection and diagnosis of plant leaf diseases. To create the knowledge base we used 799 sample images and divided it by 80% training and 20% testing. We have use Three crops each yield three diseases in addition to the proper state of each crop .the accuracy of disease detection was 88.1% .

Clinical outcomes of two patients with a novel pathogenic variant in ASNS: response to asparagine supplementation and review of the literature

Asparagine synthetase deficiency (ASNSD, OMIM #615574) is a rare autosomal recessive neurometabolic inborn error that leads to severe cognitive impairment. It manifests with microcephaly, intractable seizures, and progressive cerebral atrophy. Currently, there is no established treatment for this condition. In our pediatric cohort, we discovered, by whole-exome sequencing in two siblings from Turkey, a novel homozygous missense mutation in asparagine synthetase at NM_133436.3 (ASNS_v001): c.1108C>T that results in an amino acid exchange p.(Leu370Phe), in the C-terminal domain. After identification of the metabolic defect, treatment with oral asparagine supplementation was attempted in both patients for 24 months. Asparagine supplementation was well tolerated, and no further disease progression was observed during treatment. One of our patients showed mild developmental progress with increased levels of attention and improved nonverbal communication. These results support our hypothesis that asparagine supplementation should be further investigated as a treatment option for ASNSD. We further reviewed all previously reported ASNSD cases with regard for their clinical phenotypes and brain imaging findings to provide an essential knowledge base for rapid diagnosis and future clinical studies.

Person Centered Primary Heath Care: The Role and Positionof the Family Physician-Generalist and Implications for Research

Aim. This paper analyses how the role and function of family physicians in primary health care relates to person centeredness. Method. Based on the model of the Ecology of Medical Care the interaction of individuals with health care has been deduced and from this were identified main implications for the provision of care. Results. Individuals experience a large variety of health problems over time, of which only a minority is presented to health care professionals, with care in the direct vicinity (community) the most likely place of contact. From this, common health problems and their prognosis, self-care and autonomy, reasons for contact, multi-morbidity and community related social determinants of health define the conditions to provide health care for all health problems in all individuals.Discussion and Conclusions. The variety of health problems presented over time for individual-specific expectations, make the person with the health problem the self-evident focus of care. The person-centered approach serves to relate the patient’s agenda to professional considerations of the nature and prognosis of the health problem identified and the risks and benefits of treatment. From this, the importance can be established of continuity of care, shared decision making, individual prognostication and ascertaining individual risks from community-specific determinants of health. Priorities of research are to come to a better understanding of the mechanisms of continuity of care and the building of professional relations of trust. Other priorities of research are to strengthen the knowledge base of diagnosis, prognostication and interventions of frequent health problems in the community.

Identifying Barriers to Delirium Screening and Prevention in the Pediatric ICU: Evaluation of PICU Staff Knowledge

Delirium in the pediatric intensive care unit (PICU) setting is often unrecognized and undertreated. The importance of screening and identification of ICU delirium has been identified in both adult and pediatric literature. Delirium increases ICU morbidity, length of mechanical ventilation and length of stay. The objective of this study was to determine the current knowledge level about delirium and its risk factors among pediatric critical care nurses through a short questionnaire. We hypothesized that before a targeted educational intervention, PICU care providers do not have an adequate knowledge base for accurate screening and diagnosis of delirium in critically ill children. A 17 question online survey was given to all nurses in a tertiary 36-bed PICU to assess current knowledge about delirium in children. The response rate was 73% (105/143). When asked to identify the correct way to diagnose pediatric delirium, 11.4% of nurses surveyed (12/105) incorrectly believed that Glasgow Coma Score is the appropriate screening tool. A large proportion of respondents (40/105) believed that benzodiazepines are helpful in treatment of delirium. The results of the survey identified specific knowledge gaps about risk factors and treatment of pediatric delirium in the critically ill child. There is a critical need for education about pediatric delirium and its risk factors among PICU staff prior to unit-wide implementation of a delirium screening and prevention program, specifically with regards to screening methods and pharmacologic risk factors. These results are likely generalizable to all physicians, nurses and staff who care for critically ill children.

A Research and Implementation of Internal Medicine Diagnosis Assisted by Intelligence Knowledge Base

Intelligent knowledge system is an important knowledge base for internal medicine diagnosis. Intelligent diagnosis of the knowledge base can be realized by establishing appropriate expert models to assist diagnosis and treatment. By building the hierarchical model of internal diseases, this paper established an internal medicine diagnostic system assisted by intelligence knowledge base with the mathematical model of analytic hierarchy. The hierarchical model is able to summarize characteristics of diseases and quantize the determinant criterion of diseases. The weighted value of a possible disease can be obtained through the judgment of physicians on the weight of factors of the criterion layer and the compared calculation of database. It is concluded that the analytic hierarchy model can realize the auxiliary diagnosis function of intelligence knowledge base and the weight of a disease providing diagnostic reference for physicians.

A research on intelligent fault diagnosis of wind turbines based on ontology and FMECA

Clean energy is an increasing concern as more and more countries pay attention to environmental protection, which brings the rapid development of wind power. More new wind farms and new wind turbines have been put into operation, this caused the problem that the diagnostic knowledge is lacking and diagnostic efficiency is low for new employed maintenance personnel. In order to meet the demands of fault diagnosis of wind turbines, a method of intelligent fault diagnosis based on ontology and FMECA (Failure Mode, Effects and Criticality Analysis) is proposed in this paper. In the proposed method, the FMECA of wind turbines is selected as the knowledge source, and deep knowledge and shallow knowledge extracted from this source are represented by ontology modeling. And then, the diagnosis knowledge base of wind turbines can be established. Reasoning on this knowledge base by virtue of JESS (Java Expert Shell System) rule engine, maintenance personnel can find the causes of faults of a wind turbine quickly, and choose the proper solutions. This method realizes the knowledge sharing between product design enterprises and wind farms. The knowledge base which combines the deep knowledge and the shallow knowledge can improve the capability of fault diagnosis and provide better supports for diagnostic decision making.

Application Research of the Pro/3 Inference Information Report and Transformer Fault Diagnosis Based on Forward Reasoning

The paper work had improved some inference information report show functions of the expert system shell Pro/3 by using graph in combination with text . By analyzing some forward reasoning application mechanisms of the Pro/3, this work had also created a transformer fault diagnosis knowledge base rule model based on dissolved gas analysis in transformers such that using the rules of sentence in model had implemented an expert system of transformer fault diagnosis. The basic application shows the effectiveness of the system.The modeling approach created by the Pro/3 can be also applied to some diagnosis or analysis of other equipments. Exploring the Pro/3 has certain practical significance for establishing some expert systems or assistance decision support systems.