旋转机械设备关键部件故障诊断与预测方法研究
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摘要
科学技术的不断发展,使得人们的生活水平日益提高,人类社会不断向前发展。在工业社会,机械设备朝着集中化、大型化、自动化、高速化、连续化、精密化方向不断发展,起着举足轻重的作用。旋转机械设备已广泛应用于机械传动系统中,如汽车传动系统、高速列车、油砂泵等。例如,航天飞机技术的发展,使得人类遨游太空成为可能；喷气式飞机可以将乘客在两小时内送到千里之外的目的地；高速铁路技术不断发展,时速高达350km/的高速动车组大大缩短了北京到上海的时间,带动京沪铁路沿线的经济发展；油砂中蕴含有大量有价值的能源,可以从中提炼沥青、原油等,作为传输油砂这种物质的大型油砂泵,被广泛应用于石油工业,极大的提高了炼油系统的效率。然而复杂的机械设备将增加发生故障的可能性和故障类型的复杂性,机械的意外故障将会导致整个生产流程中断,产生重大经济损失,甚至危及人身安全[1-4]。油砂泵生产线一旦由于油砂泵故障导致中止,将遭受每小时高达数百万美金的损失。2003年2月1日,美国“哥伦比亚”号航天飞机发生空中解体事故,7名宇航员全部遇难,给美国航天产业带来沉重打击[6]。2003年4月2日,某潜艇出海训练,返航上浮时启动柴油主机,由于未能打开指挥台上方的进气阀门,造成主机吸空潜艇内的空气,并使1-7舱在负压力下无法迅速打开舱口盖,70位艇员全部在战位上短时间内窒息牺牲[7]。2011年7月23日20时30分左右,北京至福州的D301次列车行驶至温州市双屿路段时,与杭州开往福州的D3115次列车追尾,导致D301次1、2、3列车厢侧翻,从高架桥上坠落,毁坏严重,事故已造成40人死亡,200多人受伤[8]。2004年10月,在浙赣线、石太线、京沪线等处,发生4次因车轴疲劳断裂和1次因车轮疲劳断裂造成的货物列车脱轨重大事故。多年来,复杂设备的意外故障已经造成了巨大的经济损失和人员伤亡。为了尽量避免上述现象,美国每年支出高达6000亿美金来维护设备[20],油砂公司不得不在油砂泵关键部件损坏之前早早更换部件来尽量避免意外故障,而这些过度维护造成了极大的资源浪费和经济损失。因此,采用经济、有效的手段保障设备稳定、可靠运行,对设备关键部件进行状态监测,可以保证生产的有序进行,有效预防灾难事故的发生,也是当前科技发展的重要研究课题之一[12-15]。
机械设备故障诊断与预测技术(Mechanical Fault Diagnosis and Prognosis)是一种利用各种测量和监视方法,对设备状态进行记录、分析,对异常状态进行报警,并对设备剩余寿命进行预测的先进技术。运用上述技术,可以及时发现早期故障,避免恶性事件的发生,还可以避免维修不足和过度维修带来的经济损失,具有巨大的经济效益和社会效益。
滚动轴承是将运转的轴与轴座之间的滑动摩擦变为滚动摩擦,从而减少摩擦损失的一种精密的机械元件,是旋转机械设备的重要部件,被用于高速列车、发动机、汽轮机等设备中。在使用过程中,轴承承担了较大的负荷,由于加工制造、安装、使用环境等因素,必然会受到各种机械应力、热应力,使得轴承的健康状态发生变化,出现不同程度的损伤从而产生缺陷,当缺陷发展到一定程度导致机械设备故障。在2003年一年内全国货运列车轴承故障就有913起。统计表明,轴承的正常运行直接影响到整个设备的健康状态,旋转机械设备的故障有很大比例是由于轴承的故障引起的[16]。齿轮是机械设备中最常用的传动方式,齿轮箱是旋转机械设备的关键部件,它广泛应用于机床、车辆等场合,对工业等各个领域具有重要的意义。齿轮磨损为其常见故障之一[17],齿轮磨损会影响齿轮间的正常啮合,由此产生的冲击将严重降低设备性能。因此,其健康状况影响着整个机械系统的工作状态,意外故障的不及时处理将导致难以想象的后果,油砂泵叶轮作为油砂泵的关键零件,长期经受油砂物质的冲击、腐蚀,成为油砂泵的最易损件之一。轴承、齿轮、油砂泵叶轮等旋转设备的关键部件,影响到整个机械系统的稳定运行,对它们进行有效的状态监测、诊断、预测,具有十分重要的科学意义。
轴承、齿轮、油砂泵叶轮等旋转设备的关键部件发生缺陷后,在旋转过程中,缺陷不规则表面受碰撞将导致采集到的振动或声音信号中产生冲击成分[22]。因此,振动或声音信号包含丰富的设备状态信息,对这些信号进行分析是常用的工程方法之一。
基于信号处理技术的设备状态特征提取是机械故障诊断与预测技术的关键,方法主要有时域处理、频域处理、时频域处理三大类。在时域处理方法中,常见的统计特征有均值、峰值、均方根、方根幅值、绝对平均值、方差、歪度、峭度等有量纲参数以及波形指标、峰值指标、脉冲指标、裕度指标、峭度指标等无量纲参数。它们与设备状态信息有密切关系,其中,均方根为信号的有效值,反映信号的能量大小；方差反映信号分散程度；歪度反映信号幅值概率密度函数对纵轴的不对称度；峭度是表征曲线陡峭程度的物理量,对大幅值敏感,随着故障的出现,峰值,均方根,峭度值都会增加,且峭度增加快,对探测信号的脉冲成分尤其有效。另外一种时域方法为时域同步平均法,可以保留特征频率成分,其他噪声成分相互抵消并随着平均次数增加而趋于消失从而增强故障特征成分。匹配追踪方法是将一已知讯号拆解成由许多被称作为原子讯号的加权总和,而且企图找到与原来讯号最接近的解。该方法可以有效提取机械故障信号中的瞬态成分。Freudinger等人[43]介绍了一种对时间信号和Laplace小波求向量内积的相关滤波方法获取模态动力学特性。Wang等人[45]利用基于参数识别的瞬态模型与故障信号进行相关滤波得到的最大相关系数进行故障识别,均取得了显著效果。另一种时域处理方法为数学形态学滤波方法,它的基本思想是用具有一定形态的结构元素去量度和提取图像中的对应形状以达到对图像分析和识别的目的。它是由赛拉(J. Serra)博士和导师马瑟荣教授在从事铁矿核分析及预测其开采价值的研究中提出“击中/击不中变换”的理论,并在理论层面上第一次引入了形态学的表达式,建立了颗粒分析方法。数学形态学的算法具有天然的并行实现的结构,实现了形态学分析和处理算法的并行,大大提高了图像分析和处理的速度。作为一种基于数学形态学的非线性滤波方法被广泛应用到各个领域。例如Nikolaou等人利用平直结构元素对轴承振动信号进行分析[24],Hao等人[46]利用形态小波变换对被噪声湮没的轴承故障信号进行分解,提取故障特征,Wang等人[47]对形态滤波器加以改进,提高对故障信号的滤波性能,均取得不错的效果。频域处理方法可以很方便的分析信号频域成分,能够提供比时域处理方法更丰富的信息。傅里叶变换为经典的频域处理方法,针对现实中获取的离散信号,相应发展了离散傅里叶变换以及快速傅里叶变换,可以观察我们感兴趣的特定频率成分。除此以外,其他频域方法诸如倒频谱[53]可以检测功率谱中的谐波和边频带成分。对于非高斯信号,高阶频谱可以提供比功率谱更丰富的诊断信息,他们已广泛应用于故障诊断中。对于非平稳信号中出现的故障相关的瞬态成分,不能很好地被时域或频域方法获取。随之发展的时频分析方法提供了时间域与频率域的联合分布信息,清楚地描述了信号频率随时间变化的关系。例如短时傅里叶变换方法通过不同的时间窗将信号分成多个部分,分别进行傅里叶变换,可以看到频域随时间变化的信息。Wang等人[55]提出基于短时傅里叶变化的谱峭度方法,比时域谱峭度方法对故障特征更敏感。Zanardelli等人[56,57]利用短时傅里叶变换系数作为特征向量识别永磁交流电机的电气与机械故障。然而它不能兼顾时间分辨率和频域分辨率,且窗函数宽度、形状需要人为设定,只能识别信号中缓慢变化的非稳态成分。Wigner-Ville分布是一种经典的时频分析方法,它是信号中心协方差函数的傅里叶变换,揭示了信号瞬时功率谱密度。该时频分布时频聚集性好但出现交叉项,影响对信号特征的分析和理解[60]。近年来,小波变换理论作为一种先进的时频分析方法,被广泛应用于机械故障诊断中,它是由Morlet等人[65]于1984年提出。与傅里叶变换相比,小波变换是时间和频率的局部变换,通过伸缩和平移等运算功能可对信号进行多尺度的细化分析。Kankar等人[68]计算连续小波系数的统计特征并输入到机器学习工具中对轴承故障进行诊断,Qiu等人[69]利用小波滤波器和重组定义的指数来评估轴承健康状态随时间变化情况,Wang等人[70]利用离散小波变换建立健康指数来描述轴承退化状况。He等人[72]对手表信号进行小波分解来分析奇异性。小波分解需要对小波奇函数、分解层次等进行预定义,存在人为因素。经验模态分解方法是由台湾学者黄锷博士[77]提出的一种新的自适应信号处理方法,它可以将信号分解成若干不同的振动模态(本征模态函数)。Yan等人[79]运用经验模态分解方法得到的瞬时频率探测轴承的状态退化,Fan等人[80]利用本征模态函数的幅值加速度能量表述轴承和齿轮的故障特征,Dong等人[82]利用改进的经验模态分解方法将原始信号分解成若干本征模态函数,然后选择合适的本征模态函数进行故障诊断,均取得较好的效果。经验模态分解方法不涉及基函数选择、窗口设定、能量泄漏等问题,具有较好的适应性,但是模态混叠问题、边际点效应问题影响着经验模态分解方法的性能。随后,整体平均经验模态分解算法被提出[86],它是一种噪声辅助的方法,可以有效解决模态混叠问题。已被成功运用于转子、轴承、齿轮等关键部件的故障诊断[87]。Zhang等人[88]对整体平均经验模态分解算法的参数设定问题进行了系统总结。
另一种方法是通过建立物理模型刻画设备退化过程的方法,该方法需要对研究对象的系统结构、故障机理、故障演变等具有系统性的理解。Loparo等人[91]通过建立模型的方法研究了机械故障的诊断。如果一个准确的模型被建立,结果性能会优于其他方法,但是由于设备的复杂性,建立一个合理的物理模型将变得非常困难。
信号处理的故障诊断方法需要诊断者具备专业的技能,能从信号时域、频域、时频域中掌握设备的故障状态。近年来,随着计算技术的发展,基于人工智能的故障模式识别方法被逐渐引入到故障诊断与预测领域中,其一般包含以下几步：
特征提取,通过常见的信号预处理算法,如时域、频域、时频域处理方法,提取如表5.1,表6.1中所列的统计特征,作为特征向量,描述设备状态[92]。
特征选择,从信号中所提取的包含多个统计量的特征向量维数较大,其中并不是每个特征都对故障状态很敏感,同时,额外的特征会增加运算负担并影响识别性能。因此,对特征进行合理的筛选是很关键的一步。主成分分析、独立成分分析是常见的特征选择方法[98-100]。Yuan等人[101]利用主成分分析减少故障特征维数对涡轮给水泵进行诊断,Widodo等人[102]利用独立成分分析减少异步电机的特征数目。另外一种有效的特征选择方法为距离评估技术,Lei等人[103-105]多次运用该方法有效地减少特征数目,并实现了机械故障的高精度识别。
故障诊断与预测,借助于人工智能算法,可以为特征向量与故障模式或剩余寿命之间的关系建立非线性模型。人工神经网络是一种应用类似于大脑神经突触联接的结构进行信息处理的数学模型,具备多输入多输出结构。误差反向传播神经网络为常用的数学模型,常见的3层神经网络如图2.2所示,包含输入层,隐含层,输出层。Rafiee等人[111]利用人工神经网络诊断齿轮故障,Lei等人[112]通过自适应神经模糊推理系统对旋转机械进行故障诊断。Zhang等人[115]提取频域特征,利用人工神经网络进行剩余寿命预测。然而,该模型在层数以及节点数目的确定上缺乏理论依据[113]。此外,人工神经网络收敛速度慢,训练过程中存在过度拟合或拟合不足等问题。近年来,Vapnik等人[116]研究出了基于统计学习理论的支持向量机方法,数学理论完善,泛化能力强,且训练过程中不需要大量样本,已被大量应用于机械故障诊断与预测工作中。Li等人[119]应用支持向量机对旋转机械进行故障诊断,结果优于人工神经网络方法,Hao等人[121]用形态多尺度分析进行特征提取,支持向量机进行轴承故障分类、识别。作为支持向量机理论的延伸,支持向量回归理论[122-124]被研究用于回归分析问题,Moura等人[125]将支持向量回归器与其他学习方法进行比较,对工程部件的剩余寿命进行预测,结果表明,支持向量回归方法更为优越。
其他人工智能算法,例如相关向量机,隐马尔科夫模型,K均值,模糊C均值等方法亦被用于故障诊断与预测领域。
在本论文中,主要针对以下问题进行研究：
形态学滤波算法作为一种图像处理算法,近年来被引入到故障诊断领域,其包含4种基本算子,不同算子具备不同的信号处理性能,通过不同算子间适当的结合可以发挥不同算子的优势。结构元素的长度选择也是一个关键工作,长度不当会导致滤波无效或信息去除过多的问题。目前常见的方法有两类,一类是固定尺度法[24],即设定结构元素长度为故障周期的某个倍数,因此需要预知故障周期；另一类方法为多尺度方法[131],利用不同长度的结构元素进行形态滤波,然后对所有结果进行平均,该方法需要多次进行形态滤波,相对耗时。
高速列车轴承在高速情况下承载整个车厢重量,一旦轴承发生故障将面临脱轨危险。道旁声学检测系统通过采集轴承的声音信号进行分析,由于运动列车与采集系统之间存在相对运动,采集到的信号受多普勒效应影响严重,考虑多普勒效应影响下的轴承声学信号分析是一大难题。
信号处理的故障诊断方法需要专业技能判断故障,且在工作环境复杂,设备结构复杂的情况下,很难获得有效的设备故障状态信号,很难单纯的通过信号处理方法进行故障识别。而人工智能的识别方法通过数据特征与故障类型之间的非线性关系可以自动识别故障,具有很好的应用前景,如何更有效的提取和筛选特征,实现故障类型的高精度识别是研究重点。
轴承故障是从萌生、发展、逐步到严重的过程。在故障初始阶段,故障轻微、状态特征微弱,这就使得实施诊断,尤其是定量诊断非常困难,如何有效提取反映故障状态、程度的特征,建立有效的轴承定量诊断模型,对掌握轴承的健康状态,具有重要意义。
油砂泵作为油砂公司的重要设备,一旦生产中断,将带来重大经济损失,一些学者对油砂泵模型、剩余寿命预测进行了研究[155,156],但是很多工作都是基于实验室环境,实际工况下的剩余寿命预测工作很少。
为了解决上述问题,本文进行了以下工作：
考虑信号中噪声的分布特点,我们对不同时刻的信号采用不同的结构元素长度,结构元素的长度由该时刻信号极值点间隔来动态决定,同时为了综合滤波算子的优势,我们提出了变尺度差分算子进行形态滤波。对仿真数据和实际轴承信号进行分析,结果表明,提出的变尺度形态学滤波算法可以增强故障频率特征,且计算效率优于传统方法。
对于受多普勒效应影响的机车轴承信号,本文提出了多普勒瞬态模型,选择Laplace小波为基函数建立周期性瞬态成分,然后人为加入多普勒效应产生畸变,即通过声学理论,研究推导信号从发生到接受情况下时间的偏移和幅值调制情况,在参数选择上,以多普勒瞬态模型和实际故障信号的相关系数为目标函数进行优化。通过机车轴承内外圈故障数据分析来看,最优多普勒瞬态模型脉冲间隔与轴承内外圈故障脉冲理论值较为吻合。
在基于人工智能的故障诊断方面,本文提出了一种新的故障智能识别策略,利用小波包对原始信号进行分解,然后对所有小波包进行特征提取,利用距离评估技术对所有特征进行敏感性评估。结合支持向量回归理论提出新的支持向量回归分类器用于最终故障状态识别。对轴承、齿轮的故障数据分析表明,本文的方法在识别精度上要优于传统方法。
在上一步工作的基础上,本文又提出了双层支持向量回归决策机构用于轴承的定性与定量识别,分别从时域、频域提取故障特征,然后利用第一层的故障模式识别函数和第二层的故障程度识别函数进行定性、定量诊断。结果表明,双层支持向量回归决策机构可以较为理想的识别轴承故障和尺寸,且方法优于传统的二元支持向量机和人工神经网络。
国外某知名油砂公司为本研究提供了宝贵的油砂泵工业环境下的全寿命振动数据。选择信号频谱的若干敏感子频带进行频域能量特征提取,然后对历史特征进行统计,选择历史方差、均值等作为特征向量,建立油砂泵磨损状态识别模型,通过结合样本历史剩余寿命,推导剩余寿命预测方程。结果表明,该方法可以有效预测油砂泵剩余寿命,达到寿命预测的目的。
Over the past decades, rotating machinery has been widely used in a range of mechanical transmission systems, including automobile transmission systems, high-speed trains and slurry pumps. Unexpected failures in such machinery can cause breakdowns, leading to significant economic losses and, even worse, human casualties. Components like bearings, gearboxes and impellers, are commonly used in rotating machinery to support their rotating shafts, transmit torque and deliver liquid respectively. Statistical surveys suggest that these components are frequently failed and the major causes of machine breakdown. Hence, to avoid catastrophe and minimise machinery downtime, the machine fault diagnosis and its prognosis are of great industrial significance. Accordingly, the development of machine fault diagnosis and prognosis methods have attracted considerable research attention in recent decades. This thesis aims to report the development of a variety of effective methods to identify vital fault indicators, perform proper fault diagnosis and predict the remaining useful life of a number of key components in rotating machinery. The followings are the descriptions of each developed effective method and its tested results.
The first method presented is the method called morphology. The mathematical morphology is a kind of nonlinear analysis method that has been developed and successfully applied to various applications, such as image processing and signal analysis. It has drawn the attentionof researchers in bearing fault diagnosis recently. In this method, the determination of the length of the structure element (SE) is crucial and may significantly affect the results. To ensure a success application, some of the proposed algorithms need some prior knowledge of the raw signal, such as the periodic interval of generated impulses or a fixed SE length in single-scale morphology analysis. Besides, some of the existing techniques need to repeat the morphological transform for many times with different scales. Based on these considerations, an adaptive varying-scale morphology analysis method is introduced here. Different from the previous morphology analyses, the SEs are determined by the time interval between two adjacent local impulsive peaks. Compared to other currently using morphology analyses, the improved method needs little prior knowledge and does not require intensive calculations as that needed by conventional multi-scale morphology analysis.
The second developed method is the new Doppler transient modelfor the locomotive bearing fault diagnosis. A well-known scenario is that Doppler effect may significantly distort the collected acoustic signals during high movement speeds, consequently increase the difficulty in monitoring locomotive bearings online. In this thesis, a new Doppler transient model based on the acoustic theory and the Laplace wavelet are presented for the identification of fault-related impact intervals embedded in the collected acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. This new method has been proven that it can reveal the parameters used for simulated transients (the time intervals of the simulated transients) embedded in the acoustic signals. Therefore, the time intervalsof localized impacts generated by a defective bearing become detectable once the parameters of transients have been successfully identified.
Third, anew intelligent fault diagnosis methodbased on the support vector regressionmodel has been developed. For complex machinery, it is difficult to extract the fault-induced signatures via signal processing methods as the fault-related features could be greatly overwhelmed by useless components, like noise and irrelevant vibrations generated by non-related components. Accordingly, the artificial intelligence-based fault diagnosis method is introduced here as it has the potential to tackle these kinds of problem.The intelligent fault diagnosis method is consisted of the extraction of statistical parameters from the paving of a wavelet packet transform, a distance evaluation technique and a support vector regressionbased generic multi-class solver. The collected signals are first pre-processed by the wavelet packet transform at different decomposition depths. Statistical parameters are then extracted from the signals via the wavelet packet transform at different decomposition depths. A distance evaluation technique is then employed to reduce the dimensionality of the feature space. Finally, a support vector regressivebased generic multi-class solver is used to identify each existing fault pattern. The intelligent fault diagnosis methoddemonstrates that it can provide higher accuracy and robustness in machine fault diagnosis as compared to that provided by other existing methods.
The deterioration of a normal bearing is a gradual process in functional failure.Bearing failure usually occurs when an early bearing fault grows to a severe extent. Maintenance actions taken too early or late are not suitable, as they may lead to too much or too little maintenance. Hence, the fourth development is a method that can trace the size of a bearing's defect is meaningful for timely maintenance actions. A comprehensive bearing fault diagnostic and prognostic method should not only recognize each fault pattern, but also determine the defect size of each pattern so that the deteriorating rate of a particular kind of defective bearing can be evaluated. In order to establish a systematic scheme for diagnosing early bearing fault patterns and sizes, a new two-layer structure consists of support vector regression machines (SVRMs) is proposed to accurately recognize both bearing fault patterns and defect sizes. Selected statistical features are first extracted from the raw vibration signals collected from a machine under different bearing health conditions. The two-layer SVRMs are then trained with the feature vectors by defining decision-making functions based on the regression functions which have continuous outputs for the two layers. Finally, the bearing fault patterns and sizes can be intelligently recognized by the nonlinear models created in the two layers of SVRM.
The fifth developed method is aimed to predict the remaining useful life of a particular component of a slurry pump normally used in the oil production or mining fields. The impeller is an important and frequently failed component in the slurry pump. The mixtures transported by the pumps may include liquid and different sizes of solid, such as sand and rocks. The continuous bombardment to the impeller surface by these sand and rocks is definitely making the pump works in a very abrasive and erosive environment. Long duration working in such environment definitely causes the impeller suffering from continuous wear. To monitor the wear rate on impeller, an effective data driven technique for estimating the practical remaining useful life of impeller has been developed based on the estimation of the failure probability density function that can be obtained by using support vector machine (SVM) again. Selected spectral energy features are extracted from the raw signals under different wear conditions that have known values of remaining useful life. Then an intelligent impeller health status probability estimation model has been constructed by using the one-against-one SVM. Finally, by identifying the relationship of each health status probability to the historical remaining useful life values, the SVM is able to predict the remaining useful life of a wearing impeller.
A total of five contributionshave been briefly introduced here. With the help of these five methods, different degrees in the abilities of mechanical component fault diagnosis and prognosis have been developed for rotating machinery. These methods are all adaptive to the monitored machine components which have different mechanical characteristics and operating in different working conditions. The results generated from simulations and industrial case studies, they provesthat the aforementioned five methods yield satisfactory performance in fault diagnosis and prognosis and are superior to other conventional methods.
机械设备故障诊断与预测技术(Mechanical Fault Diagnosis and Prognosis)是一种利用各种测量和监视方法,对设备状态进行记录、分析,对异常状态进行报警,并对设备剩余寿命进行预测的先进技术。运用上述技术,可以及时发现早期故障,避免恶性事件的发生,还可以避免维修不足和过度维修带来的经济损失,具有巨大的经济效益和社会效益。
滚动轴承是将运转的轴与轴座之间的滑动摩擦变为滚动摩擦,从而减少摩擦损失的一种精密的机械元件,是旋转机械设备的重要部件,被用于高速列车、发动机、汽轮机等设备中。在使用过程中,轴承承担了较大的负荷,由于加工制造、安装、使用环境等因素,必然会受到各种机械应力、热应力,使得轴承的健康状态发生变化,出现不同程度的损伤从而产生缺陷,当缺陷发展到一定程度导致机械设备故障。在2003年一年内全国货运列车轴承故障就有913起。统计表明,轴承的正常运行直接影响到整个设备的健康状态,旋转机械设备的故障有很大比例是由于轴承的故障引起的[16]。齿轮是机械设备中最常用的传动方式,齿轮箱是旋转机械设备的关键部件,它广泛应用于机床、车辆等场合,对工业等各个领域具有重要的意义。齿轮磨损为其常见故障之一[17],齿轮磨损会影响齿轮间的正常啮合,由此产生的冲击将严重降低设备性能。因此,其健康状况影响着整个机械系统的工作状态,意外故障的不及时处理将导致难以想象的后果,油砂泵叶轮作为油砂泵的关键零件,长期经受油砂物质的冲击、腐蚀,成为油砂泵的最易损件之一。轴承、齿轮、油砂泵叶轮等旋转设备的关键部件,影响到整个机械系统的稳定运行,对它们进行有效的状态监测、诊断、预测,具有十分重要的科学意义。
轴承、齿轮、油砂泵叶轮等旋转设备的关键部件发生缺陷后,在旋转过程中,缺陷不规则表面受碰撞将导致采集到的振动或声音信号中产生冲击成分[22]。因此,振动或声音信号包含丰富的设备状态信息,对这些信号进行分析是常用的工程方法之一。
基于信号处理技术的设备状态特征提取是机械故障诊断与预测技术的关键,方法主要有时域处理、频域处理、时频域处理三大类。在时域处理方法中,常见的统计特征有均值、峰值、均方根、方根幅值、绝对平均值、方差、歪度、峭度等有量纲参数以及波形指标、峰值指标、脉冲指标、裕度指标、峭度指标等无量纲参数。它们与设备状态信息有密切关系,其中,均方根为信号的有效值,反映信号的能量大小；方差反映信号分散程度；歪度反映信号幅值概率密度函数对纵轴的不对称度；峭度是表征曲线陡峭程度的物理量,对大幅值敏感,随着故障的出现,峰值,均方根,峭度值都会增加,且峭度增加快,对探测信号的脉冲成分尤其有效。另外一种时域方法为时域同步平均法,可以保留特征频率成分,其他噪声成分相互抵消并随着平均次数增加而趋于消失从而增强故障特征成分。匹配追踪方法是将一已知讯号拆解成由许多被称作为原子讯号的加权总和,而且企图找到与原来讯号最接近的解。该方法可以有效提取机械故障信号中的瞬态成分。Freudinger等人[43]介绍了一种对时间信号和Laplace小波求向量内积的相关滤波方法获取模态动力学特性。Wang等人[45]利用基于参数识别的瞬态模型与故障信号进行相关滤波得到的最大相关系数进行故障识别,均取得了显著效果。另一种时域处理方法为数学形态学滤波方法,它的基本思想是用具有一定形态的结构元素去量度和提取图像中的对应形状以达到对图像分析和识别的目的。它是由赛拉(J. Serra)博士和导师马瑟荣教授在从事铁矿核分析及预测其开采价值的研究中提出“击中/击不中变换”的理论,并在理论层面上第一次引入了形态学的表达式,建立了颗粒分析方法。数学形态学的算法具有天然的并行实现的结构,实现了形态学分析和处理算法的并行,大大提高了图像分析和处理的速度。作为一种基于数学形态学的非线性滤波方法被广泛应用到各个领域。例如Nikolaou等人利用平直结构元素对轴承振动信号进行分析[24],Hao等人[46]利用形态小波变换对被噪声湮没的轴承故障信号进行分解,提取故障特征,Wang等人[47]对形态滤波器加以改进,提高对故障信号的滤波性能,均取得不错的效果。频域处理方法可以很方便的分析信号频域成分,能够提供比时域处理方法更丰富的信息。傅里叶变换为经典的频域处理方法,针对现实中获取的离散信号,相应发展了离散傅里叶变换以及快速傅里叶变换,可以观察我们感兴趣的特定频率成分。除此以外,其他频域方法诸如倒频谱[53]可以检测功率谱中的谐波和边频带成分。对于非高斯信号,高阶频谱可以提供比功率谱更丰富的诊断信息,他们已广泛应用于故障诊断中。对于非平稳信号中出现的故障相关的瞬态成分,不能很好地被时域或频域方法获取。随之发展的时频分析方法提供了时间域与频率域的联合分布信息,清楚地描述了信号频率随时间变化的关系。例如短时傅里叶变换方法通过不同的时间窗将信号分成多个部分,分别进行傅里叶变换,可以看到频域随时间变化的信息。Wang等人[55]提出基于短时傅里叶变化的谱峭度方法,比时域谱峭度方法对故障特征更敏感。Zanardelli等人[56,57]利用短时傅里叶变换系数作为特征向量识别永磁交流电机的电气与机械故障。然而它不能兼顾时间分辨率和频域分辨率,且窗函数宽度、形状需要人为设定,只能识别信号中缓慢变化的非稳态成分。Wigner-Ville分布是一种经典的时频分析方法,它是信号中心协方差函数的傅里叶变换,揭示了信号瞬时功率谱密度。该时频分布时频聚集性好但出现交叉项,影响对信号特征的分析和理解[60]。近年来,小波变换理论作为一种先进的时频分析方法,被广泛应用于机械故障诊断中,它是由Morlet等人[65]于1984年提出。与傅里叶变换相比,小波变换是时间和频率的局部变换,通过伸缩和平移等运算功能可对信号进行多尺度的细化分析。Kankar等人[68]计算连续小波系数的统计特征并输入到机器学习工具中对轴承故障进行诊断,Qiu等人[69]利用小波滤波器和重组定义的指数来评估轴承健康状态随时间变化情况,Wang等人[70]利用离散小波变换建立健康指数来描述轴承退化状况。He等人[72]对手表信号进行小波分解来分析奇异性。小波分解需要对小波奇函数、分解层次等进行预定义,存在人为因素。经验模态分解方法是由台湾学者黄锷博士[77]提出的一种新的自适应信号处理方法,它可以将信号分解成若干不同的振动模态(本征模态函数)。Yan等人[79]运用经验模态分解方法得到的瞬时频率探测轴承的状态退化,Fan等人[80]利用本征模态函数的幅值加速度能量表述轴承和齿轮的故障特征,Dong等人[82]利用改进的经验模态分解方法将原始信号分解成若干本征模态函数,然后选择合适的本征模态函数进行故障诊断,均取得较好的效果。经验模态分解方法不涉及基函数选择、窗口设定、能量泄漏等问题,具有较好的适应性,但是模态混叠问题、边际点效应问题影响着经验模态分解方法的性能。随后,整体平均经验模态分解算法被提出[86],它是一种噪声辅助的方法,可以有效解决模态混叠问题。已被成功运用于转子、轴承、齿轮等关键部件的故障诊断[87]。Zhang等人[88]对整体平均经验模态分解算法的参数设定问题进行了系统总结。
另一种方法是通过建立物理模型刻画设备退化过程的方法,该方法需要对研究对象的系统结构、故障机理、故障演变等具有系统性的理解。Loparo等人[91]通过建立模型的方法研究了机械故障的诊断。如果一个准确的模型被建立,结果性能会优于其他方法,但是由于设备的复杂性,建立一个合理的物理模型将变得非常困难。
信号处理的故障诊断方法需要诊断者具备专业的技能,能从信号时域、频域、时频域中掌握设备的故障状态。近年来,随着计算技术的发展,基于人工智能的故障模式识别方法被逐渐引入到故障诊断与预测领域中,其一般包含以下几步：
特征提取,通过常见的信号预处理算法,如时域、频域、时频域处理方法,提取如表5.1,表6.1中所列的统计特征,作为特征向量,描述设备状态[92]。
特征选择,从信号中所提取的包含多个统计量的特征向量维数较大,其中并不是每个特征都对故障状态很敏感,同时,额外的特征会增加运算负担并影响识别性能。因此,对特征进行合理的筛选是很关键的一步。主成分分析、独立成分分析是常见的特征选择方法[98-100]。Yuan等人[101]利用主成分分析减少故障特征维数对涡轮给水泵进行诊断,Widodo等人[102]利用独立成分分析减少异步电机的特征数目。另外一种有效的特征选择方法为距离评估技术,Lei等人[103-105]多次运用该方法有效地减少特征数目,并实现了机械故障的高精度识别。
故障诊断与预测,借助于人工智能算法,可以为特征向量与故障模式或剩余寿命之间的关系建立非线性模型。人工神经网络是一种应用类似于大脑神经突触联接的结构进行信息处理的数学模型,具备多输入多输出结构。误差反向传播神经网络为常用的数学模型,常见的3层神经网络如图2.2所示,包含输入层,隐含层,输出层。Rafiee等人[111]利用人工神经网络诊断齿轮故障,Lei等人[112]通过自适应神经模糊推理系统对旋转机械进行故障诊断。Zhang等人[115]提取频域特征,利用人工神经网络进行剩余寿命预测。然而,该模型在层数以及节点数目的确定上缺乏理论依据[113]。此外,人工神经网络收敛速度慢,训练过程中存在过度拟合或拟合不足等问题。近年来,Vapnik等人[116]研究出了基于统计学习理论的支持向量机方法,数学理论完善,泛化能力强,且训练过程中不需要大量样本,已被大量应用于机械故障诊断与预测工作中。Li等人[119]应用支持向量机对旋转机械进行故障诊断,结果优于人工神经网络方法,Hao等人[121]用形态多尺度分析进行特征提取,支持向量机进行轴承故障分类、识别。作为支持向量机理论的延伸,支持向量回归理论[122-124]被研究用于回归分析问题,Moura等人[125]将支持向量回归器与其他学习方法进行比较,对工程部件的剩余寿命进行预测,结果表明,支持向量回归方法更为优越。
其他人工智能算法,例如相关向量机,隐马尔科夫模型,K均值,模糊C均值等方法亦被用于故障诊断与预测领域。
在本论文中,主要针对以下问题进行研究：
形态学滤波算法作为一种图像处理算法,近年来被引入到故障诊断领域,其包含4种基本算子,不同算子具备不同的信号处理性能,通过不同算子间适当的结合可以发挥不同算子的优势。结构元素的长度选择也是一个关键工作,长度不当会导致滤波无效或信息去除过多的问题。目前常见的方法有两类,一类是固定尺度法[24],即设定结构元素长度为故障周期的某个倍数,因此需要预知故障周期；另一类方法为多尺度方法[131],利用不同长度的结构元素进行形态滤波,然后对所有结果进行平均,该方法需要多次进行形态滤波,相对耗时。
高速列车轴承在高速情况下承载整个车厢重量,一旦轴承发生故障将面临脱轨危险。道旁声学检测系统通过采集轴承的声音信号进行分析,由于运动列车与采集系统之间存在相对运动,采集到的信号受多普勒效应影响严重,考虑多普勒效应影响下的轴承声学信号分析是一大难题。
信号处理的故障诊断方法需要专业技能判断故障,且在工作环境复杂,设备结构复杂的情况下,很难获得有效的设备故障状态信号,很难单纯的通过信号处理方法进行故障识别。而人工智能的识别方法通过数据特征与故障类型之间的非线性关系可以自动识别故障,具有很好的应用前景,如何更有效的提取和筛选特征,实现故障类型的高精度识别是研究重点。
轴承故障是从萌生、发展、逐步到严重的过程。在故障初始阶段,故障轻微、状态特征微弱,这就使得实施诊断,尤其是定量诊断非常困难,如何有效提取反映故障状态、程度的特征,建立有效的轴承定量诊断模型,对掌握轴承的健康状态,具有重要意义。
油砂泵作为油砂公司的重要设备,一旦生产中断,将带来重大经济损失,一些学者对油砂泵模型、剩余寿命预测进行了研究[155,156],但是很多工作都是基于实验室环境,实际工况下的剩余寿命预测工作很少。
为了解决上述问题,本文进行了以下工作：
考虑信号中噪声的分布特点,我们对不同时刻的信号采用不同的结构元素长度,结构元素的长度由该时刻信号极值点间隔来动态决定,同时为了综合滤波算子的优势,我们提出了变尺度差分算子进行形态滤波。对仿真数据和实际轴承信号进行分析,结果表明,提出的变尺度形态学滤波算法可以增强故障频率特征,且计算效率优于传统方法。
对于受多普勒效应影响的机车轴承信号,本文提出了多普勒瞬态模型,选择Laplace小波为基函数建立周期性瞬态成分,然后人为加入多普勒效应产生畸变,即通过声学理论,研究推导信号从发生到接受情况下时间的偏移和幅值调制情况,在参数选择上,以多普勒瞬态模型和实际故障信号的相关系数为目标函数进行优化。通过机车轴承内外圈故障数据分析来看,最优多普勒瞬态模型脉冲间隔与轴承内外圈故障脉冲理论值较为吻合。
在基于人工智能的故障诊断方面,本文提出了一种新的故障智能识别策略,利用小波包对原始信号进行分解,然后对所有小波包进行特征提取,利用距离评估技术对所有特征进行敏感性评估。结合支持向量回归理论提出新的支持向量回归分类器用于最终故障状态识别。对轴承、齿轮的故障数据分析表明,本文的方法在识别精度上要优于传统方法。
在上一步工作的基础上,本文又提出了双层支持向量回归决策机构用于轴承的定性与定量识别,分别从时域、频域提取故障特征,然后利用第一层的故障模式识别函数和第二层的故障程度识别函数进行定性、定量诊断。结果表明,双层支持向量回归决策机构可以较为理想的识别轴承故障和尺寸,且方法优于传统的二元支持向量机和人工神经网络。
国外某知名油砂公司为本研究提供了宝贵的油砂泵工业环境下的全寿命振动数据。选择信号频谱的若干敏感子频带进行频域能量特征提取,然后对历史特征进行统计,选择历史方差、均值等作为特征向量,建立油砂泵磨损状态识别模型,通过结合样本历史剩余寿命,推导剩余寿命预测方程。结果表明,该方法可以有效预测油砂泵剩余寿命,达到寿命预测的目的。
Over the past decades, rotating machinery has been widely used in a range of mechanical transmission systems, including automobile transmission systems, high-speed trains and slurry pumps. Unexpected failures in such machinery can cause breakdowns, leading to significant economic losses and, even worse, human casualties. Components like bearings, gearboxes and impellers, are commonly used in rotating machinery to support their rotating shafts, transmit torque and deliver liquid respectively. Statistical surveys suggest that these components are frequently failed and the major causes of machine breakdown. Hence, to avoid catastrophe and minimise machinery downtime, the machine fault diagnosis and its prognosis are of great industrial significance. Accordingly, the development of machine fault diagnosis and prognosis methods have attracted considerable research attention in recent decades. This thesis aims to report the development of a variety of effective methods to identify vital fault indicators, perform proper fault diagnosis and predict the remaining useful life of a number of key components in rotating machinery. The followings are the descriptions of each developed effective method and its tested results.
The first method presented is the method called morphology. The mathematical morphology is a kind of nonlinear analysis method that has been developed and successfully applied to various applications, such as image processing and signal analysis. It has drawn the attentionof researchers in bearing fault diagnosis recently. In this method, the determination of the length of the structure element (SE) is crucial and may significantly affect the results. To ensure a success application, some of the proposed algorithms need some prior knowledge of the raw signal, such as the periodic interval of generated impulses or a fixed SE length in single-scale morphology analysis. Besides, some of the existing techniques need to repeat the morphological transform for many times with different scales. Based on these considerations, an adaptive varying-scale morphology analysis method is introduced here. Different from the previous morphology analyses, the SEs are determined by the time interval between two adjacent local impulsive peaks. Compared to other currently using morphology analyses, the improved method needs little prior knowledge and does not require intensive calculations as that needed by conventional multi-scale morphology analysis.
The second developed method is the new Doppler transient modelfor the locomotive bearing fault diagnosis. A well-known scenario is that Doppler effect may significantly distort the collected acoustic signals during high movement speeds, consequently increase the difficulty in monitoring locomotive bearings online. In this thesis, a new Doppler transient model based on the acoustic theory and the Laplace wavelet are presented for the identification of fault-related impact intervals embedded in the collected acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. This new method has been proven that it can reveal the parameters used for simulated transients (the time intervals of the simulated transients) embedded in the acoustic signals. Therefore, the time intervalsof localized impacts generated by a defective bearing become detectable once the parameters of transients have been successfully identified.
Third, anew intelligent fault diagnosis methodbased on the support vector regressionmodel has been developed. For complex machinery, it is difficult to extract the fault-induced signatures via signal processing methods as the fault-related features could be greatly overwhelmed by useless components, like noise and irrelevant vibrations generated by non-related components. Accordingly, the artificial intelligence-based fault diagnosis method is introduced here as it has the potential to tackle these kinds of problem.The intelligent fault diagnosis method is consisted of the extraction of statistical parameters from the paving of a wavelet packet transform, a distance evaluation technique and a support vector regressionbased generic multi-class solver. The collected signals are first pre-processed by the wavelet packet transform at different decomposition depths. Statistical parameters are then extracted from the signals via the wavelet packet transform at different decomposition depths. A distance evaluation technique is then employed to reduce the dimensionality of the feature space. Finally, a support vector regressivebased generic multi-class solver is used to identify each existing fault pattern. The intelligent fault diagnosis methoddemonstrates that it can provide higher accuracy and robustness in machine fault diagnosis as compared to that provided by other existing methods.
The deterioration of a normal bearing is a gradual process in functional failure.Bearing failure usually occurs when an early bearing fault grows to a severe extent. Maintenance actions taken too early or late are not suitable, as they may lead to too much or too little maintenance. Hence, the fourth development is a method that can trace the size of a bearing's defect is meaningful for timely maintenance actions. A comprehensive bearing fault diagnostic and prognostic method should not only recognize each fault pattern, but also determine the defect size of each pattern so that the deteriorating rate of a particular kind of defective bearing can be evaluated. In order to establish a systematic scheme for diagnosing early bearing fault patterns and sizes, a new two-layer structure consists of support vector regression machines (SVRMs) is proposed to accurately recognize both bearing fault patterns and defect sizes. Selected statistical features are first extracted from the raw vibration signals collected from a machine under different bearing health conditions. The two-layer SVRMs are then trained with the feature vectors by defining decision-making functions based on the regression functions which have continuous outputs for the two layers. Finally, the bearing fault patterns and sizes can be intelligently recognized by the nonlinear models created in the two layers of SVRM.
The fifth developed method is aimed to predict the remaining useful life of a particular component of a slurry pump normally used in the oil production or mining fields. The impeller is an important and frequently failed component in the slurry pump. The mixtures transported by the pumps may include liquid and different sizes of solid, such as sand and rocks. The continuous bombardment to the impeller surface by these sand and rocks is definitely making the pump works in a very abrasive and erosive environment. Long duration working in such environment definitely causes the impeller suffering from continuous wear. To monitor the wear rate on impeller, an effective data driven technique for estimating the practical remaining useful life of impeller has been developed based on the estimation of the failure probability density function that can be obtained by using support vector machine (SVM) again. Selected spectral energy features are extracted from the raw signals under different wear conditions that have known values of remaining useful life. Then an intelligent impeller health status probability estimation model has been constructed by using the one-against-one SVM. Finally, by identifying the relationship of each health status probability to the historical remaining useful life values, the SVM is able to predict the remaining useful life of a wearing impeller.
A total of five contributionshave been briefly introduced here. With the help of these five methods, different degrees in the abilities of mechanical component fault diagnosis and prognosis have been developed for rotating machinery. These methods are all adaptive to the monitored machine components which have different mechanical characteristics and operating in different working conditions. The results generated from simulations and industrial case studies, they provesthat the aforementioned five methods yield satisfactory performance in fault diagnosis and prognosis and are superior to other conventional methods.
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