本文選題：無軌設(shè)備　切入點(diǎn)：狀態(tài)監(jiān)測　出處：《電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：無軌設(shè)備是在礦山礦井采礦中的主要作業(yè)設(shè)備,能適應(yīng)于作業(yè)過程中惡劣的工作環(huán)境的特殊車輛。然而,目前國內(nèi)研發(fā)的無軌設(shè)備的智能化和自動化程度還比較低,尤其是設(shè)備運(yùn)行過程的狀態(tài)監(jiān)測及智能化故障診斷能力非常的薄弱。大多數(shù)設(shè)備都是采用定期和事后維修方式,而且主要依靠技術(shù)人員的經(jīng)驗(yàn)進(jìn)行設(shè)備的維護(hù)和故障排除。研究并建立可靠而有效的設(shè)備狀態(tài)監(jiān)測及故障診斷系統(tǒng),對實(shí)現(xiàn)無軌設(shè)備的信息化改進(jìn)具有非常重要的意義。本文針對某公司主要的無軌設(shè)備,從工程實(shí)際出發(fā),設(shè)計并實(shí)現(xiàn)了地下無軌設(shè)備的狀態(tài)檢測及故障診斷系統(tǒng)。本文首先介紹了無軌設(shè)備的主要的組成結(jié)構(gòu)和系統(tǒng)構(gòu)件,分析了主要的無軌設(shè)備常見的故障類型與機(jī)理。結(jié)合無軌設(shè)備的結(jié)構(gòu)特點(diǎn)和故障特性,綜合考慮無軌設(shè)備的特點(diǎn)和監(jiān)測需求選取了監(jiān)測工況參數(shù),并提出了系統(tǒng)的總體軟件架構(gòu)和設(shè)計方案。然后針對設(shè)備工況參數(shù)檢測和特征提取問題,提出了一種基于CAN總線的分布式數(shù)據(jù)采集系統(tǒng),可根據(jù)不同設(shè)備需求選配所需的數(shù)據(jù)采集模塊。考慮到不同工況參數(shù)的特征不同,因此采用不同的特征提取方法,對于溫度和壓力等瞬時參數(shù),主要采用基于閾值準(zhǔn)則提取故障特征,可及時提供狀態(tài)評估和報警信息;而無軌設(shè)備的發(fā)動機(jī)作為核心部件,故障特征比較復(fù)雜,其大部分故障都是由振動信號反映的。因此提出了基于EMD近似熵方法提取發(fā)動機(jī)振動信號故障特征,該方法可有效的降低噪聲干擾,提高故障特征提取的準(zhǔn)確性。之后依據(jù)無軌設(shè)備的技術(shù)要求和監(jiān)測需求,綜合分析所有的工況參數(shù)以提高故障診斷的準(zhǔn)確率,采用了基于信息融合技術(shù)方法實(shí)現(xiàn)故障的診斷。通過信號處理方法提取所有工況參數(shù)的特征,再采用信息融合方法構(gòu)建特征向量,將故障特征向量作為LSSVM的學(xué)習(xí)和測試樣本,實(shí)現(xiàn)故障診斷和決策。此方法可提高系統(tǒng)的故障診斷準(zhǔn)確性和可靠性。最后根據(jù)系統(tǒng)性能和要求設(shè)計并實(shí)現(xiàn)了無軌設(shè)備的狀態(tài)監(jiān)測及故障診斷系統(tǒng),主要包括狀態(tài)監(jiān)測和報警模塊、數(shù)據(jù)處理和故障診斷模塊、CANOpen協(xié)議的測試模塊、區(qū)域報警系統(tǒng)的設(shè)置和調(diào)試模塊、工況參數(shù)和通道基本信息的設(shè)置和工作界面的儀表顯示方式設(shè)置模塊。從配置系統(tǒng)、參數(shù)檢測系統(tǒng)、狀態(tài)監(jiān)測系統(tǒng)和故障診斷系統(tǒng)四個方面闡述了系統(tǒng)的軟件結(jié)構(gòu)和軟件的設(shè)計與實(shí)現(xiàn)。
[Abstract]:Trackless equipment is the main working equipment in mine and mine mining, which can adapt to the bad working environment in the working process. However, the intelligence and automation of trackless equipment developed in our country are still low at present. Especially, the ability of condition monitoring and intelligent fault diagnosis is very weak. And mainly rely on the experience of technical personnel for equipment maintenance and troubleshooting, research and establish a reliable and effective equipment condition monitoring and fault diagnosis system, It is very important to realize the information improvement of trackless equipment. The condition detection and fault diagnosis system of underground trackless equipment is designed and implemented. Firstly, the main structure and system components of trackless equipment are introduced. The common fault types and mechanisms of the main trackless equipment are analyzed. Combined with the structural and fault characteristics of the trackless equipment, the monitoring operating conditions parameters are selected in consideration of the characteristics and monitoring requirements of the trackless equipment. Then, a distributed data acquisition system based on CAN bus is proposed to detect and extract the operating parameters of the equipment. The data acquisition module can be selected according to the requirements of different equipments. Considering the different characteristics of the parameters under different operating conditions, different feature extraction methods are adopted, such as temperature and pressure, etc. Based on threshold criterion, fault features can be extracted in time to provide state evaluation and alarm information, while the engine of trackless equipment is the core component, and the fault features are complex. Most of the faults are reflected by vibration signals. Therefore, a method based on EMD approximate entropy is proposed to extract the fault features of engine vibration signals, which can effectively reduce the noise interference. Improve the accuracy of fault feature extraction. Then according to the technical requirements and monitoring requirements of trackless equipment comprehensive analysis of all operating conditions parameters to improve the accuracy of fault diagnosis. The fault diagnosis is realized based on information fusion technology. The feature vectors of all operating conditions are extracted by signal processing method, and then the feature vectors are constructed by information fusion method. The fault feature vectors are used as learning and testing samples of LSSVM. This method can improve the accuracy and reliability of fault diagnosis. Finally, according to the performance and requirement of the system, the condition monitoring and fault diagnosis system of trackless equipment is designed and realized. It mainly includes status monitoring and alarm module, data processing and fault diagnosis module, testing module of CANOpen protocol, setting and debugging module of regional alarm system, Working condition parameters and basic information of the channel and the working interface of the instrument display mode setting module. From the configuration system, parameter detection system, The software structure of the system and the design and implementation of the software are described in four aspects: the condition monitoring system and the fault diagnosis system.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TD52

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本文編號：1581580