本文選題：風(fēng)電設(shè)備 + 知識(shí)管理　； 參考：《湖南大學(xué)》2014年博士論文

【摘要】：設(shè)備維護(hù)和故障診斷是保證風(fēng)電設(shè)備正常運(yùn)行，，降低企業(yè)運(yùn)營(yíng)成本的關(guān)鍵因素。提升風(fēng)電設(shè)備的維護(hù)和故障診斷水平不僅有助于企業(yè)實(shí)現(xiàn)其經(jīng)營(yíng)目標(biāo)，提高企業(yè)效益，也將推動(dòng)環(huán)境與社會(huì)的和諧發(fā)展。隨著現(xiàn)代技術(shù)的發(fā)展，設(shè)備維護(hù)和故障診斷的技術(shù)和理念不斷更新，對(duì)設(shè)備現(xiàn)有知識(shí)的利用成為實(shí)現(xiàn)設(shè)備維護(hù)優(yōu)化和故障智能診斷的重要途徑。基于知識(shí)的設(shè)備維護(hù)通過維護(hù)知識(shí)集成和維護(hù)流程優(yōu)化，減少設(shè)備維護(hù)工作的復(fù)雜性，實(shí)現(xiàn)維護(hù)效益的最大化；基于知識(shí)的故障診斷可以在融合不同企業(yè)診斷經(jīng)驗(yàn)知識(shí)的同時(shí)，利用知識(shí)的推理功能，以智能生成診斷策略的形式來輔助維護(hù)人員的診斷工作。 知識(shí)管理是實(shí)現(xiàn)基于知識(shí)的設(shè)備維護(hù)和故障診斷的基礎(chǔ)，而風(fēng)電設(shè)備的知識(shí)分布在不同企業(yè)和部門內(nèi)，具有多源異構(gòu)的特點(diǎn)，傳統(tǒng)方法難以實(shí)現(xiàn)知識(shí)的高效融合。為了有效提升風(fēng)電設(shè)備的知識(shí)管理水平，使其能為設(shè)備維護(hù)優(yōu)化和故障智能診斷研究提供知識(shí)保障，本文在國(guó)家高技術(shù)研究發(fā)展計(jì)劃(863計(jì)劃)項(xiàng)目“大型風(fēng)力發(fā)電機(jī)組狀態(tài)監(jiān)控與故障診斷技術(shù)研究”(項(xiàng)目編號(hào)：2009AA04Z414)的資助下，將本體引入到風(fēng)電設(shè)備知識(shí)的表示與檢索中，對(duì)風(fēng)電設(shè)備多源異構(gòu)知識(shí)的管理和應(yīng)用進(jìn)行了深入系統(tǒng)的研究。 本文主要的研究工作和創(chuàng)新性成果有 （1）結(jié)合風(fēng)電設(shè)備知識(shí)管理的需求，提出了基于本體的風(fēng)電設(shè)備多源異構(gòu)知識(shí)管理模型。該模型采用本體實(shí)現(xiàn)各領(lǐng)域知識(shí)的描述，通過全局本體和局部本體的映射實(shí)現(xiàn)全局知識(shí)融合，在此基礎(chǔ)上進(jìn)行風(fēng)電設(shè)備的維護(hù)優(yōu)化和故障智能診斷，實(shí)現(xiàn)了對(duì)現(xiàn)有設(shè)備知識(shí)的高效利用。 （2）在多源知識(shí)融合的基礎(chǔ)上，針對(duì)傳統(tǒng)FMECA（Failure Mode, Effects andCriticality Ana lys is）在故障評(píng)估時(shí)風(fēng)險(xiǎn)屬性賦值模糊且不考慮風(fēng)險(xiǎn)因素權(quán)重的問題，采用本體描述模糊知識(shí)，并提出了模糊多準(zhǔn)則決策方法，由此實(shí)現(xiàn)了故障危害的量化評(píng)估；針對(duì)風(fēng)險(xiǎn)屬性精確賦值時(shí)不同故障具有相同RPN（Risk PriorityNumber）值的問題，提出數(shù)據(jù)包絡(luò)分析的方法進(jìn)行評(píng)估模式改進(jìn)，以增強(qiáng)FMECA對(duì)故障模式危害度的分辨能力，為優(yōu)化設(shè)備維護(hù)計(jì)劃提供決策依據(jù)。 （3）針對(duì)風(fēng)電設(shè)備維護(hù)計(jì)劃優(yōu)化的需求，提出了FMECA本體與故障樹本體結(jié)合的FTF（Fault Tree Failure）方法。該方法在相關(guān)領(lǐng)域知識(shí)采用本體表示的基礎(chǔ)上，將故障樹分析與FMECA結(jié)合，依據(jù)故障樹最小割集的綜合風(fēng)險(xiǎn)順序數(shù)實(shí)現(xiàn)維護(hù)計(jì)劃的優(yōu)化，解決了FMECA不能研究多故障的問題，有效提高了風(fēng)電設(shè)備維護(hù)效率。 （4）針對(duì)目前風(fēng)電設(shè)備故障診斷方法多、理論復(fù)雜、維護(hù)人員難以掌握的問題，提出了基于知識(shí)檢索的風(fēng)電設(shè)備故障智能診斷方法。該方法在各領(lǐng)域知識(shí)以本體表示的基礎(chǔ)上，建立診斷方法推理所需的規(guī)則集，通過知識(shí)推理輔助維護(hù)人員選擇合適的故障診斷方法。 （5）提出了基于FMECA本體的故障智能診斷方法。該方法以風(fēng)電設(shè)備FMECA本體為知識(shí)庫(kù)，通過JESS（Java Expert System Shell）規(guī)則引擎對(duì)知識(shí)庫(kù)進(jìn)行推理，以輔助維護(hù)人員快速查找定位故障原因，選擇合適的診斷方法。該方法通過基于知識(shí)的推理提高了故障診斷問題求解的能力，推理結(jié)果能夠?yàn)榫S護(hù)人員提供診斷決策支持。 （6）研發(fā)了風(fēng)電設(shè)備知識(shí)管理及應(yīng)用的原型系統(tǒng)，闡明了原型系統(tǒng)的研發(fā)需求和整體框架，介紹了原型系統(tǒng)的開發(fā)過程，包括本體知識(shí)庫(kù)和知識(shí)推理的設(shè)計(jì)，以及維護(hù)優(yōu)化、故障診斷功能模塊的開發(fā)，并通過應(yīng)用實(shí)例驗(yàn)證了原型系統(tǒng)的有效性。
[Abstract]:Equipment maintenance and fault diagnosis are the key factors to ensure the normal operation of the wind power equipment and reduce the operation cost of the enterprise. The improvement of the maintenance and fault diagnosis of the wind power equipment will not only help the enterprise to achieve its operating objectives, improve the efficiency of the enterprise, but also promote the harmonious development of the environment and the society. With the development of modern technology, equipment maintenance and The technology and concept of fault diagnosis are constantly updated. The use of existing knowledge of equipment is an important way to realize equipment maintenance optimization and fault intelligent diagnosis. Knowledge based equipment maintenance can reduce the complexity of equipment maintenance work by maintaining knowledge integration and maintenance process optimization, and realize maximum maintenance efficiency; knowledge based knowledge At the same time, the fault diagnosis can help the maintenance of the personnel's diagnosis by using the reasoning function of knowledge and the form of intelligent generation of diagnostic strategies, while combining different enterprises with the diagnosis of experience knowledge.
Knowledge management is the basis for realizing equipment maintenance and fault diagnosis based on knowledge. The knowledge of wind power equipment is distributed in different enterprises and departments and has the characteristics of multi source and heterogeneous. It is difficult to achieve efficient integration of knowledge by traditional methods. In order to effectively improve the knowledge management level of wind power equipment, it can optimize equipment maintenance and fault intelligence. With the support of the national high technology research and development plan (863 plan) project "state monitoring and fault diagnosis technology for large wind turbines" (project number: 2009AA04Z414), this paper introduces the ontology into the knowledge representation and retrieval of wind power equipment, and the multi source and heterogeneous knowledge of wind power equipment. The management and application have been studied in depth and systematically.
The main research work and innovative results in this article are
(1) combining the requirements of the knowledge management of wind power equipment, a multi-source heterogeneous knowledge management model of wind power equipment based on ontology is proposed. The model uses ontology to describe the knowledge of various fields, and realizes global knowledge fusion through the mapping of the global ontology and the local ontology. On this basis, the maintenance and optimization of wind power equipment and the fault intelligent diagnosis are carried out. It has realized the efficient utilization of the existing equipment knowledge.
(2) on the basis of multi source knowledge fusion, in view of the problem that the risk attribute value of the traditional FMECA (Failure Mode, Effects andCriticality Ana lys is) is fuzzy and does not consider the weight of the risk factors, the fuzzy knowledge is described by the ontology, and the fuzzy multiple criteria decision method is proposed, thus the quantitative assessment of the fault hazard is realized. In view of the problem that the different faults have the same RPN (Risk PriorityNumber) value when the risk attributes are accurately assigned, the method of data envelopment analysis is proposed to improve the evaluation mode, in order to enhance the resolution of the hazard degree of the failure mode of the FMECA and provide the decision basis for the optimization of the equipment maintenance plan.
(3) in view of the demand for the optimization of the maintenance plan of the wind power equipment, the FTF (Fault Tree Failure) method, which combines the FMECA ontology and the fault tree ontology, is proposed. The method combines the fault tree analysis with the FMECA on the basis of the ontology representation of the related domain knowledge, and realizes the optimization of the maintenance plan according to the comprehensive risk sequence number of the minimum cut set of the fault tree. It solves the problem that FMECA can not study multiple faults, and effectively improves the efficiency of wind power equipment maintenance.
(4) aiming at the problem that the fault diagnosis method of wind power equipment is many, the theory is complicated and the maintenance personnel are difficult to master, the intelligent diagnosis method of wind power equipment fault based on knowledge retrieval is put forward. This method establishes the rule set required by the diagnosis method reasoning on the basis of the knowledge of various fields, and assists the maintenance of the maintenance personnel through knowledge reasoning. Select the appropriate fault diagnosis method.
(5) a fault intelligent diagnosis method based on FMECA ontology is proposed. The method uses the FMECA ontology of wind power equipment as the knowledge base and the knowledge base through the JESS (Java Expert System Shell) rule engine to assist the maintenance personnel to find the cause of the fault quickly and select the suitable diagnosis method. This method is proposed by knowledge based reasoning. The ability of fault diagnosis is high, and the reasoning result can provide maintenance decision support.
(6) the prototype system of knowledge management and application of wind power equipment is developed, the development requirements and the overall framework of the prototype system are expounded. The development process of the prototype system is introduced, including the design of the ontology knowledge base and knowledge reasoning, the maintenance and optimization, the development of the function module of the fault diagnosis, and the prototype system is verified by the application examples. Efficiency.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM315

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