毕业论文（设计）构建基于数据挖掘的风机远程监测与故障诊断平台.doc

构建基于数据挖掘的风机远程监测与故障诊断平台* 侯澍旻 李友荣 刘光临/武汉科技大学摘要：构建了一套基于INTERNET、数据库和数据挖掘技术的风机远程监测与诊断平台。通过该平台，授权用户可以方便快捷地获取和交换诊断维护信息。关键词：风机 数据挖掘 故障诊断 中图分类号：TP273 文献标识码：B文章编号：1006-8155（2006）-02-0031-04Constructing the Long-distance Monitor and Trouble Diagnosis Platform of Fan Based on Data ExploitingAbstract: A long-distance monitoring and trouble diagnosis platform of fan that is based on INTERNET, data warehouse and data exploiting technology is constructed. With the platform, authorized user can acquire and exchange diagnosis maintenance information rapidly. Key words: Fan Data exploiting Trouble diagnosis 1 引言 大型风机（如引风机、送风机）等高速旋转关键设备的运行状态直接影响许多企业的生产。如何使这些设备在安全、可靠的状态下尽可能长时间地满负荷运行是企业永远关注的问题，在线监测成为一个普遍采用的手段。随着设备故障诊断技术和互联网的发展，通过网络对现场设备实施监测，并进行远程诊断也已成为现实。对于风机远程监测与故障诊断系统而言，许多分析方法必须以大量的历史数据为依托。因此，必须借助数据库和数据挖掘技术才能更好地进行故障诊断。根据以上分析，本文将数据挖掘技术引入远程故障诊断领域，构建了一套基于INTERNET、数据库和数据挖掘技术的风机远程监测与诊断平台。该平台为风机的安全运行提供了有力保障。2 数据库和数据挖掘简介数据库（DW）是一个用来更好地支持决策分析处理的，面向主题的、集成的、不可更改的、随时间不断变化的数据集合；是故障诊断系*基金项目：湖北省教育厅重点资助项目（Z200511016），武汉科技大学校基金资助（2005XY17）。统处理更复杂决策分析的较好途径。其主要实现过程是通过对数据的组织、抽取、清理和转换，建立若干个主题各异、格式统一的数据集市，存储现有的真实历史数据，从而尽可能降低物理上和语义上的不一致问题，实现数据应用于决策分析和管理的目的。从数据挖掘（DM）的角度看，数据库是数据挖掘实施的平台1。数据挖掘是从数据中发现趋势或模式的高级过程，又称为数据库知识发现（KDD）。DM是一种分析和决策手段。主要根据人工智能、机器学习和统计学习原理，以数据库为基础，分析和挖掘历史数据2。DM技术在远程诊断领域的应用提高了数据分析的效率和深度，为智能化诊断，特别是故障预测提供了有力的手段，从而使设备享有专家级远程诊断。3 风机远程监测与诊断平台的构建3.1 监测对象本系统监测对象为某烧结厂的2引风机，结合风机具体情况得到如下的监测项目：(1) 振动速度：同步电机输出轴轴承座和风机输入端轴承座水平和铅垂两个方向的振动速度信号，电机尾部轴承座（有止推轴瓦端）和风机非动力端轴承座水平、铅垂和轴向3个方向的振动速度信号，共10个速度信号。(2) 振动位移：风机轴水平和垂直两个方向的振动位移信号，风机非动力输出端轴位移信号，共5个位移信号。(3) 温度信号：电机和风机两端油膜轴承温度，共4个轴承温度信号；风机机壳温度，共4个信号。(4) 转速及鉴相：1个鉴相信号。(5) 工艺参数：流量；油温、油压；进、出口风压和风温；冷却水进、出口温度；电流、电压等。机组测点布置如图1所示，电机尾部轴承座的水平、铅垂和轴向分别安装1、2和3振动速度传感器；电机输出端轴承座的水平和铅垂方向分别安装4和5振动速度传感器；风机输入端轴承座水平和铅垂两个方向分别安装6和7振动速度传感器；风机非动力端轴承座水平、铅垂和轴向分别安装8、9和10振动速度传感器。在风机非动力输出端安装轴位移信号（11）；在风机输入、输出轴处的水平和垂直两个方向各安装两个振动位移传感器（12、13和1 4、15）。在电机与风机的联轴器处安装接近开关，监测转子的相位变化并得到转速（16）。在电机与风机两端油膜轴承上安装温度传感器（1720）。在风机机壳上安装4个温度传感器（2124）。图1 测点布置图3.2 诊断平台网络运行结构该远程监测与诊断平台是一个开放的多层分布式系统，主要由3个子系统即在线监测子系统、网络通信子系统和智能诊断专家子系统组成。其体系结构如图2所示。3.2.1 在线监测子系统 在线监测子系统包括数据采集模块和数据管理模块。数据采集包括传感器系统、信号采集系统和自主开发的实时在线监测软件，主要完成采集卡硬件的实时采样、实时数据显示、报警。数据管理模块接受和处理数据采集系统发送的各路信号数据，并将处理后的数据写入特征数据库和历史数据库。然后，借助微软Analysis Services,通过数据抽取、清理和转换，建立数据库。3.2.2 智能诊断专家子系统 当在线监测子系统监测到某个特征量超标时，系统就会启动智能诊断专家子系统，从综合状态数据库相关数据中提取特征征兆开始故障诊断。诊断中心子系统由诊断中心模块和知识库管理模块组成。诊断中心模块负责定期巡视监测轧机设备，接受、处理授权客户发来的诊断请求。完成诊断后返回故障诊断结果。诊断中心暂时不能处理的故障可通过Internet提交给领域专家处理，必要时，专家可直接控制现场设备。知识库管理模块可通过传统经验和专家经验得到故障规则，还可以利用数据挖掘技术形成规则算法。3.2.3 网络通信子系统 整个系统采用基于CORBA（Common Object Request Broker Architecture）客户/服务器的模式进行通讯，www服务器和浏览器之间则采用SOAP /Web Service技术基于http协议的通讯方式。各系统访问数据库则采用ADO连结的方式进行访问。图2 平台网络体系结构图3.3 平台诊断数据挖掘结构平台诊断数据挖掘结构见图3。其中知识库是基于征兆事实库、传统专家经验诊断规则和数据挖掘诊断规则的混合系统，各诊断机制充分发挥各自的优点。征兆事实库用于存放经过数据库处理并提取的系统推理所需要的征兆事实。知识库的维护包括扩展、修改和删除3种操作。授权用户通过HTML输入风机诊断信息，信息处理与诊断推理模块利用知识库中的知识，根据故障信息诊断故障设备，最后利用知识库对所有诊断方法得到的结果综合决策，判断具体的故障类型，并将诊断结论和维护参考信息在远端客户浏览器显示。结论解释模块为用户提供诊断推理过程和结论的解释，同时将故障实例经过知识提取存入知识库中。图3 平台诊断数据挖掘结构图知识库采用传统经验、专家经验、粗糙集知识及关联规则等数据挖掘方法来获取诊断知识。关联规则挖掘从特征数据中挖掘出特征变量间隐含的因果或关联关系，可用于故障趋势预测的挖掘。另外，基于粗糙集方法的规则挖掘可适用于离散型及连续型特征数据，它先约简知识，再根据知识约简挖掘规则3。4 数据挖掘技术应用4.1 远程诊断挖掘模型该系统远程诊断的主要内容是从大量的风机运行监测信息中发现潜在的规律，提取有用的知识，智能地判断风机当前运行状态，发现隐含的或已经存在的故障。数据挖掘是一个需要经过反复多次处理的过程，它可利用不断充实的诊断知识库和恰当的算法逐步挖掘真实的故障机理及诊断规则。其实现过程：首先将原始数据整理为与挖掘主题相关的信息；然后根据挖掘主题以及各种学习算法的特点设计数据挖掘算法，并对指定数据集进行知识的提取。通过对挖掘结果进行一致性、合理性检查，与预期目标进行对比。如果结果与预期目标偏差较大，返回算法设计阶段，调整或重新设计挖掘算法；如果偏差较小，返回算法设计阶段，对挖掘算法进行调整；如果结果理想，返回数据准备阶段，扩大数据集，重新开始一次挖掘过程。重复上述步骤，直至达到最终目标。挖掘模型处理过程如图4所示4。图4 挖掘模型处理过程4.2 关联规则挖掘知识的算法5关联规则（Association Rules）是如下的形式一种规则，“在购买面包和黄油的顾客中，有90的人同时也买了牛奶”（面包黄油 =牛奶 ）。用于关联规则发现的主要对象是事务型数据库。一个事务一般由事务处理时间和一组子项（items，如顾客购买的物品）组成。关联规则的正式描述如下。设是子项(item)空间，事件是任意子项的集合，即。是的集合。设是的任一子集，如果,事件。对任一，一条关联规则表示为，其中称为前件，称为后件，表示“如果成立，则成立”。置信度(confidence)和支持度(support):如果中有s%事件包含，则s称为该规则在中的支持度。如果在包含的事件中有c%包含，则c称为该规则的置信度。可以看出，支持度是规则前件在中发生的概率，置信度是在前件发生的前提下后件发生的条件概率。因此求解关联规则的问题可以分解成两个子问题：（1）找出中所有大于最小支持度的事件子集，且。即找出所有“频繁”出现的事件子集；（2）从“频繁”出现的事件子集中找出关联规则。关联规则的概念1993年由R.Agrawal等人提出6，随后他又提出了关联规则经典算法Apriori和AprioriHybrid。如今，关联规则是商业销售、股票价格、银行交易等许多领域进行数据挖掘的常用手段。对关联规则的研究则由串行算法转向并行算法，由对布尔型数据的挖掘转向对数值型数据（quantitative）的挖掘。在智能诊断中，关联规则可以用于发现事件间的内在联系，从而对设备运行进行预报。在风机远程监测与诊断平台中，采用Apriori算法来发现故障信息内在的联系。从数据库历史故障数据表抽取故障样本数据, 按这些值之间在时间上的先后序列关系排列（见表1）。表1 风机运行故障数据表发生时间故障描述代号2003-7-3-19-11-196#测点振动超限C62003-7-3-19-15-1018#测点温度超限C182003-7-3-20-03-217#测点振动超限C72003-7-8-15-23-525#测点振动超限C52003-7-8-15-30-486#测点振动超限C62003-7-8-16-15-177#测点振动超限C72003-8-2-13-03-366#测点振动超限C62003-8-2-13-55-207#测点振动超限C7注：代号中数字表示发生故障测点位置（图1所示）。然后将随时序变化的故障信息采用时间分割法,把时序变量转化为实数变量进行考察。取时间间隔T1h，得到表2。用关联规则进行挖掘，得到形如：的规则，表示当6#测点发生振动超限后的1 h内7#测点有80的可能发生振动超限。这样就为设备维护人员提供了检查故障的线索和采取相应措施的决策依据。表2 故障事务数据表（T1 h）ID事件子集1C6，C18，C72C5，C6，C73C6，C75 结论 本文将数据库、数据挖掘技术应用于某烧结厂风机远程监测与故障诊断平台，取得了初步研究成果。采用传统经验、专家经验，粗糙集知识、关联规则相融合的专家系统综合决策技术，可增强系统综合诊断能力；而SOAP /Web Service技术则为远程诊断提供了技术保证。因此，该平台具有较高的可靠性、稳定性和实际应用价值。参 考 文 献1 Tony Bain et al. SQL Server 2000数据仓库与Analysis Services. 中国电力出版社，2003.2 Han, J. and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufimann, San Francisco,2000.3 张文修，等. 粗糙集理论与方法.科学出版社，2001.4 高毅龙. 数据挖掘及其在工程诊断中的应用.西安交通科技大学，2000.5 Mehmed Kantardzic. 数据挖掘概念、模型、方法和算法.清华大学出版社,2003.6 R. Agrawal, Christos Faloutsos, and Arun Swami. Efficient Similarity search in sequence database, In FODO Conference, Evanston, Illinois, October 1993.Editor's note: Judson Jones is a meteorologist, journalist and photographer. He has freelanced with CNN for four years, covering severe weather from tornadoes to typhoons. Follow him on Twitter: jnjonesjr (CNN) - I will always wonder what it was like to huddle around a shortwave radio and through the crackling static from space hear the faint beeps of the world's first satellite - Sputnik. I also missed watching Neil Armstrong step foot on the moon and the first space shuttle take off for the stars. Those events were way before my time.As a kid, I was fascinated with what goes on in the sky, and when NASA pulled the plug on the shuttle program I was heartbroken. Yet the privatized space race has renewed my childhood dreams to reach for the stars.As a meteorologist, I've still seen many important weather and space events, but right now, if you were sitting next to me, you'd hear my foot tapping rapidly under my desk. I'm anxious for the next one: a space capsule hanging from a crane in the New Mexico desert.It's like the set for a George Lucas movie floating to the edge of space.You and I will have the chance to watch a man take a leap into an unimaginable free fall from the edge of space - live.The (lack of) air up there Watch man jump from 96,000 feet Tuesday, I sat at work glued to the live stream of the Red Bull Stratos Mission. I watched the balloons positioned at different altitudes in the sky to test the winds, knowing that if they would just line up in a vertical straight line "we" would be go for launch.I feel this mission was created for me because I am also a journalist and a photographer, but above all I live for taking a leap of faith - the feeling of pushing the envelope into uncharted territory.The guy who is going to do this, Felix Baumgartner, must have that same feeling, at a level I will never reach. However, it did not stop me from feeling his pain when a gust of swirling wind kicked up and twisted the partially filled balloon that would take him to the upper end of our atmosphere. As soon as the 40-acre balloon, with skin no thicker than a dry cleaning bag, scraped the ground I knew it was over.How claustrophobia almost grounded supersonic skydiverWith each twist, you could see the wrinkles of disappointment on the face of the current record holder and "capcom" (capsule communications), Col. Joe Kittinger. He hung his head low in mission control as he told Baumgartner the disappointing news: Mission aborted.The supersonic descent could happen as early as Sunday.The weather plays an important role in this mission. Starting at the ground, conditions have to be very calm - winds less than 2 mph, with no precipitation or humidity and limited cloud cover. The balloon, with capsule attached, will move through the lower level of the atmosphere (the troposphere) where our day-to-day weather lives. It will climb higher than the tip of Mount Everest (5.5 miles/8.85 kilometers), drifting even higher than the cruising altitude of commercial airliners (5.6 miles/9.17 kilometers) and into the stratosphere. As he crosses the boundary layer (called the tropopause), he can expect a lot of turbulence.The balloon will slowly drift to the edge of space at 120,000 feet (22.7 miles/36.53 kilometers). Here, "Fearless Felix" will unclip. He will roll back the door.Then, I would assume, he will slowly step out onto something resembling an Olympic diving platform.Below, the Earth becomes the concrete bottom of a swimming pool that he wants to land on, but not too hard. Still, he'll be traveling fast, so despite the distance, it will not be like diving into the deep end of a pool. It will be like he is diving into the shallow end.Skydiver preps for the big jumpWhen he jumps, he is expected to reach the speed of sound - 690 mph (1,110 kph) - in less than 40 seconds. Like hitting the top of the water, he will begin to slow as he approaches the more dense air closer to Earth. But this will not be enough to stop him completely.If he goes too fast or spins out of control, he has a stabilization parachute that can be deployed to slow him down. His team hopes it's not needed. Instead, he plans to deploy his 270-square-foot (25-square-meter) main chute at an altitude of around 5,000 feet (1,524 meters).In order to deploy this chute successfully, he will have to slow to 172 mph (277 kph). He will have a reserve parachute that will open automatically if he loses consciousness at mach speeds.Even if everything goes as planned, it won't. Baumgartner still will free fall at a speed that would cause you and me to pass out, and no parachute is guaranteed to work higher than 25,000 feet (7,620 meters).It might not be the moon, but Kittinger free fell from 102,800 feet in 1960 - at the dawn of an infamous space race that captured the hearts of many. Baumgartner will attempt to break that record, a feat that boggles the mind. This is one of those monumental moments I will always remember, because there is no way I'd miss this.