A Comprehensive 3-Steps Methodology for Vibration-Based Fault Detection,Diagnosis and Localization in Rotating Machines

In any industry,it is the requirement to know whether the machine is healthy or not to operate machine further.If the machine is not healthy then what is the fault in the machine and then finally its location.The paper is proposing a 3-Steps methodology for the machine fault diagnosis to meet the industrial requirements to aid the maintenance activity.The Step-1 identifies whether machine is healthy or faulty,then Step-2 detect the type of defect and finally its location in Step-3.This method is extended further from the earlier study on the 2-Steps method for the rotor defects only to the 3-Steps methodology to both rotor and bearing defects.The method uses the optimised vibration parameters and a simple Artificial Neural Network(ANN)-based Machine Learning(ML)model from the earlier studies.The model is initially developed,tested and validated on an experimental rotating rig operating at a speed above 1st critical speed.The proposed method and model are then further validated at 2 different operating speeds,one below 1st critical speed and other above 2nd critical speed.The machine dynamics are expected to be significantly different at these speeds.This highlights the robustness of the proposed 3-Steps method.

Fault Detection and Diagnosis of a Gearbox in Marine Propulsion Systems Using Bispectrum Analysis and Artificial Neural Networks

A marine propulsion system is a very complicated system composed of many mechanical components.As a result,the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft.It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis.For this reason,a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems.To monitor the gear conditions,the bispectrum analysis was first employed to detect gear faults.The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique,which could be regarded as an index actualizing forepart gear faults diagnosis.Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox.The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum,and the ANN classification method has achieved high detection accuracy.Hence,the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases,and thus have application importance.

Gear Vibration Analysis and Gear Fault Diagnosis based on Mathematical Morphology

Gear vibration analysis and gear fault diagnosis are related to the multi-objective decision-making process of machinery equipment production, in which a large amount of data and information should be collected, and the relationship between supply/demand needs and available resources, between production and labor, and between enterprise benefit and social benefit should be balanced generally. Thus, the gear fault diagnosis technologies as well as the professional quality and technical quality are required to be very high. To conform to the forward development of mathematical modeling technology, it is urgent to implement safety product management with computer by using gear vibration analysis and gear fault diagnosis as methods for aiding the research and development of machinery gear fault diagnosis system. 7

A Diagnosis Method of Vibration Fault of a Steam Turbine Based on Information Entropy and Grey Correlation Analysis

The vibration fault, one of the common faults in the steam turbine generator unit, brings great damage to the production and the running process. It is well known that the information entropy is to describe the degree of indeterminacy of the system, so the information entropy can be used to measure Despite its efficiency, one kind of information entropy is just enabled to identify make up for this limitation, based on nalysis was studied for vibration fault the vibration condition of the unit. certain part of the faults. In order to the faulty signals collected from the rotor test platform, the grey correlation adiagnosis of steam turbine shafting in this paper. The reference faulty matrix and the calculation model of grey correlation degree was established based on three kinds of information entropy. The analysis shows that grey correlation analysis is a useful method for fault diagnosis of shafting and can be used as a quantitative index for fault diagnosis.

SIMULATION OF CRACK DIAGNOSIS OF ROTOR BASED ON MULTI-SCALE SINGUUR-SPECTRUM ANALYSIS

In the diagnosis of rotor crack based on wavelet analysis, it is a painful task to find out an adaptive mother wavelet as many of them can be chosen and the analytic results of different mother wavelets are yet not the same. For this limitation of wavelet analysis, a novel diagnostic approach of rotor crack based on multi-scale singular-spectrum analysis （MS-SSA） is proposed. Firstly, a Jeffcott model of a cracked rotor is developed and the forth-order Runge-Kutta method is used to solve the motion equations of this rotor to obtain its time response （signals）. Secondly, a comparatively simple approach of MS-SSA is presented and the empirical orthogonal functions of different orders in various scales are regarded as analyzing functions. At last, the signals of the cracked rotor and an uncracked rotor are analyzed using the proposed approach of MS-SSA, and the simulative results are compared. The results show that, the data-adaptive analyzing functions can capture many features of signals and the rotor crack can be identified and diagnosed effectively by comparing the analytic results of signals of the cracked rotor with those of the uncracked rotor using the analyzing functions of different orders.

SVM Algorithm for Vibration Fault Diagnosis in Centrifugal Pump

Vibration failure in the pumping system is a significant issue for indus-tries that rely on the pump as a critical device which requires regular maintenance.To save energy and money,a new automated system must be developed that can detect anomalies at an early stage.This paper presents a case study of a machine learning(ML)-based computational technique for automatic fault detection in a cascade pumping system based on variable frequency drive(VFD).Since the intensity of the vibrational effect depends on which axis has the most significant effect,a three-axis accelerometer is used to measure it in the pumping system.The emphasis is on determining the vibration effect on different axes.For experiment,various ML algorithms are investigated on collected vibratory data through Matlab software in x,y,z axes and performances of the algorithms are compared based on accuracy rate,prediction speed and training time.Based on the proposed research results,the multiclass support vector machine(MSVM)is found to be the best suitable algorithm compared to other algorithms.It has been demonstrated that ML algorithms can detect faults automatically rather than conventional meth-ods.MSVM is used for the proposed work because it is less complex and pro-duces better results with a limited data set.

Study of Fault Diagnosis in Bend Axis Piston Pump

On the basis of theoretical analysis and experimental rerearck, the vibration characteristics of the ZB1-107 bend axis piston pump that is wldely ed in mining machinery is studied in the paper, and the study provides the basis for pump fault diagnesis. The vibration signals of the rault-rree pump and tbe faulty pump have been compared in frequency domaln and it is round that tbere is obvious differeuce in their vibration frequency spectra. The experimentol results demonstrate that the raults, such as port plate wear and tear and the looseness or ball joint or the conuecting rod, can be effectively detected through vibration analysis.

Testing and Analysis of Induction Motor Electrical Faults Using Current Signature Analysis

The proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) to diagnosis the stator faults of Induction Motors. The performance of the proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) and the Zero-Sequence Voltage Component (ZSVC) to diagnose the stator faults of Induction Motors. The unalleviated study of the robustness of the industrial appliances is obligatory to verdict the fault of the machines at precipitate stages and thwart the machine from brutal damage. For all kinds of industry, a machine failure escorts to a diminution in production and cost increases. The Motor Current Signature Analysis (MCSA) is referred as the most predominant way to diagnose the faults of electrical machines. Since the detailed analysis of the current spectrum, the method will portray the typical fault state. This paper aims to present dissimilar stator faults which are classified under electrical faults using MCSA and the comparison of simulation and hardware results. The magnitude of these fault harmonics analyzes in detail by means of Finite-Element Method (FEM). The anticipated method can effectively perceive the trivial changes too during the operation of the motor and it shows in the results.

Fault Diagnosis of Cylindrical Grinding Machine

Based on experiment modal analysis(EMA) and operation modal analysis(OMA), the dynamic characteristics of cylindrical grinding machine were measured and provided a basis for further failure analysis.The influences of grinding parameters on dynamic characteristics were studied by analyzing the diagnostic signals extracted from racing and grinding experiments.The significant frequency of 38 Hz related to grinding wheel spindle speed of 2 307 r/min showed that the wheel spindle system was in a state of imbalan...

Brake Fault Diagnosis Through Machine Learning Approaches–A Review

Diagnosis is the recognition of the nature and cause of a certain phenomenon.It is generally used to determine cause and effect of a problem. Machine fault diagnosis isa field of finding faults arising in machines. To identify the most probable faults leadingto failure, many methods are used for data collection, including vibration monitoring,thermal imaging, oil particle analysis, etc. Then these data are processed using methodslike spectral analysis, wavelet analysis, wavelet transform, short-term Fourier transform,high-resolution spectral analysis, waveform analysis, etc. The results of this analysis areused in a root cause failure analysis in order to determine the original cause of the fault.This paper presents a brief review about one such application known as machine learningfor the brake fault diagnosis problems.

Fault diagnosis of tractor auxiliary gearbox using vibration analysis and random forest classifier

Accurate detection of mechanical components faults is an essential step for reduction of repair cost,human injury probability and loss of production.Using intelligent fault diagno-sis systems in tractor could prevent secondary damage,thereby avoiding heavy conse-quences.In this study,fault diagnosis of tractor auxiliary gearbox is presented.Vibration signals of healthy and faulty pinions gear under three different operational conditions(Rotational speeds of 600 RPM,1350 RPM and 2000 RPM)were collected,and discrete wave-let transform(DWT)was used as signal processing.Useful statistical features were calcu-lated from collected signals.Correlation-based feature selection(CFS)method was used to find the best features.Random forest(RF)and multilayer perceptron(MLP)neural net-works were employed to classify the data.The overall accuracy of RF classifier without using feature selection were 86.25%,at 600 RPM.The corresponding values of RF trained with the optimal 6 features by using CFS was 92.5%.The best results obtained at 1350 RPM,since the detection accuracy was 95%.The results of this study demonstrated the effectiveness and feasibility of the proposed method for fault diagnosis of tractor auxiliary gearbox.

Timedelay based all-frequency correction method for discrete spectrum

Many spectrum correction methods have been developed, but their performance degrades significantly when they are applied to the correction of low frequency component （ LFC ）. It owns to that the model underlying the conventional approaches neglects the interference of the negative frequency in the real signal. A new approach for the correction of the LFC is proposed, which suits all kinds of symmetrical windows. It divides a signal into three sections and makes use of the first spectrum line of each section. Then this approach is modified so that it is also applicable to the correction of the high frequency component. Thus a timedelay-based all-frequency correction method is proposed. The simulation results show that this method is simple and feasible. By this method, the accurate frequency, amplitude and phase of the spectral line can be obtained whether it is close to or far from OHz.

基于定子电流法的绕线转子无刷双馈发电机偏心故障研究

绕线转子无刷双馈电机在船舶轴带发电以及风力发电等领域中具有良好的应用前景。转子偏心是一种常见的机械故障,威胁着现代化工业体系的稳定运转,因此实现其状态检测和故障诊断非常关键。由于无刷双馈电机具有特殊的定、转子绕组结构以及复杂的气隙磁场,因此传统的故障检测方法无法直接用于该类电机。该文通过定子电流法提取了功率绕组电流的特征频率分量作为故障指标,实现对静偏心、动偏心以及混合偏心的无创诊断。建立故障电机的时步有限元模型,并通过详细的故障机理分析,得出不同类型的偏心故障可能导致的特征频率信号。最后,以一台2/4对极无刷双馈电机为例,并通过有限元仿真、样机试验验证解析法的结论,为绕线转子无刷双馈电机偏心故障的精准检测提供了理论依据。

基于多特征提取和麻雀搜索算法优化XGBoost的变压器绕组松动诊断方法

针对使用单一特征量诊断变压器绕组松动,在不同负载条件下存在交叠和抗干扰能力不足的问题,提出一种基于核主成分分析(KPCA)和改进麻雀搜索算法(SSA)优化极端梯度提升(XGBoost)的变压器绕组松动振动诊断方法。首先,从时域、频域和熵值3个维度提取适用于变压器多传感器振动信号的多种特征量;其次,通过网格搜索优化的KPCA对特征量进行降维;最后,构建基于XGBoost的故障诊断模型,并采用改进麻雀搜索算法调参,实现不同电流大小下变压器绕组松动故障准确识别。以某110 kV变压器为对象进行实验验证,诊断结果表明,所提取的特征量能够准确反映故障特征,抗干扰能力更强,诊断模型故障诊断准确率为99.00%,相比于其他诊断算法准确率和稳定性更高,在不同负载情况下均有良好的识别效果。

联合TVF-EMD和SSA降噪的轴承故障特征提取

针对滚动轴承早期故障信号微弱、故障特征难以提取的问题,文章提出了一种基于时变滤波经验模态分解(time-varying filtering based empirical mode decomposition,TVF-EMD)模态分量自适应融合与奇异谱分析(singular spectrum analysis,SSA)降噪的滚动轴承早期故障特征提取方法。首先,为了降低故障信号的非线性和非平稳性,通过TVF-EMD将轴承信号分解为一系列内蕴模态函数(IMF)。其次,为了克服TVF-EMD分解后IMF分量过多的不足,利用IMF的峭度、复杂度和分形维数构造了复合敏感模态判定因子(composite sensitive mode determination factor,CSMDF),通过CSMDF对IMF分量进行降序排列,并依据复合敏感模态判定因子递增原则对IMF分量依次进行融合,直至找到最优融合分量。最后,通过SSA对最优融合分量降噪,对降噪后分量进行Hilbert包络谱分析,实现轴承故障的特征提取。通过仿真故障信号以及两个实测故障信号对所提方法的性能进行了试验分析,试验结果表明,该方法具有良好的敏感特征筛选融合能力和降噪能力,能更准确地提取出轴承早期故障特征,实现噪声环境下轴承故障类型的准确识别。

基于多带加权包络谱的轴箱轴承故障诊断

为增强复杂噪声干扰下轴箱轴承故障检测的鲁棒性,基于循环谱分析并考虑轴承故障信息分布差异和阈值降噪,对轴箱轴承故障诊断的包络谱构造方法进行了研究.首先,提出频域信噪比作为轴承故障信息量化的新测度,用于评估谱相干中不同谱频带内的轴承故障相关信息;其次,构造以谱频率为变量的故障特征信息分布函数,并自适应确定信息阈值来辨识谱相干中故障信息丰富和干扰噪声主导的谱频率分量,进一步基于故障特征信息分布函数和信息阈值设计权重函数;最后,由谱相干和权重函数生成融合多带信息的多带加权包络谱,通过分析谱中的轴承故障特征频率来检测轴箱轴承的不同故障.铁路轴箱轴承实验数据的分析结果表明:相比于基于谱相干的典型包络谱方法,多带加权包络谱能够在复杂噪声干扰下准确识别轴箱轴承的外圈、滚动体和内圈故障,并能取得更高的性能量化指标(频域信噪比和负熵).

基于VMD⁃ESA和IPOA⁃XGBOOST相结合的异步电机故障诊断

为了提高异步电机故障诊断的准确度,提出一种结合变分模态分解(VMD)、包络谱分析法(ESA)和改进的鹈鹕优化算法优化的极限梯度提升模型(IPOA‐XGBOOST)的智能诊断方法。首先,对实测的异步电机振动信号进行VMD分解,并用ESA计算VMD分解得到的本征模态分量(IMFs)的瞬时能量矩阵;然后用奇异值分解法(SVD)对得到的瞬时能量矩阵进行特征提取;最后,使用提取到的特征向量训练IPOA‐XGBOOST模型,得到异步电机的故障诊断准确率。另外,为了解决鹈鹕优化算法容易陷入局部最优解、寻优速度慢等问题,使用Circle映射改进鹈鹕优化算法。将改进的鹈鹕优化算法、遗传算法(GA)和鹈鹕优化算法进行寻优分析,实验结果表明,改进的鹈鹕优化算法的寻优效果最好。

基于ES-ALPF的行星齿轮箱故障特征提取方法研究

针对行星齿轮箱故障初期特征提取困难的问题,提出了一种基于聚合经验模态分解(Ensemble Empirical Mode Decomposition, EEMD)、奇异谱分析(Singular Spectrum Analysis, SSA)与自适应线性预测滤波(Adaptive Linear Prediction Filtering, ALPF)结合的故障初期特征自适应提取方法。首先,利用EEMD预处理采集的振动信号,得到本征模态函数(Intrinsic Module Function, IMF)分量。其次,根据能量比选取IMFs重构信号。然后,利用SSA增强重构信号的非线性特征。之后,利用基于谱估计的自适应线性预测方法对非线性最突出的频段进行自适应线性滤波。最后,提取滤波后的信号包络,进行傅里叶变换,提取得到故障初期的微弱特征。在行星齿轮箱故障诊断试验台上进行了试验,所提方法与传统的基于EEMD-SSA的包络分析进行了对比,结果验证了该方法的有效性。

基于振动信号的控制棒驱动机构滚轮早期故障诊断研究

针对现有研究未充分关注控制棒驱动机构(control rod drive mechanism,CRDM)的早期故障诊断问题、很难将故障特征定位至具体部件以及人工引入的故障样本与装备实际故障特征存在差异等不足,提出了一种基于振动信号的CRDM滚轮早期故障诊断方法:首先,利用寿命考核试验时机采集了某密封磁阻马达式CRDM的滚轮全寿命振动信号,基于经验模态分解(empirical mode decomposition,EMD)和Hilbert变换方法进行解调分析,获得与滚轮退化状态相关的模态成分;然后,采用时、频域分析方法获得了11个能够直接表征CRDM滚轮磨损状态的特征量,并根据退化趋势提取出与实际故障特征高度吻合的早期故障样本;最后,分别基于BP神经网络和支持向量机两种方法实现了CRDM滚轮早期故障的多特征智能诊断。结果表明:提取的滚轮早期磨损故障样本与实际运行过程保持了较好的一致性,证明所提CRDM滚轮早期故障诊断方法具有较强的工程应用价值。

基于泵送频率的往复泵活塞故障诊断方法

为了确保往复式钻井泵的高质量运行,实施在线故障监测至关重要。从BW-250型钻井泵液力端的易损件入手,设计了试验探究钻井泵的振动信号的成分;通过对频率成分的分析,揭示了泵送频率幅值与活塞刺漏故障之间的关系,并依据机理提出了以泵送频率幅值作为诊断指标的钻井泵活塞故障检测方法,进一步结合最大相关峭度解卷积滤波及包络谱分析等方法从泵送频率处能量变化的角度,对活塞刺漏这一故障进行了诊断;结合实验室以及钻井现场采集的数据对该方法进行了验证,并将诊断结果同其他振动指标做了对比。结果表明,该方法对于往复泵活塞故障诊断的准确率为91.1%,相较于均方根诊断准确率提升了3.6%、相较于脉冲因子、裕度因子和峭度3种统计指标诊断准确率提升了9%以上。该方法取得了良好的结果,为往复泵活塞组件的故障诊断提出了一种较好的解决思路。