Complex signal analysis for wind turbine planetary gearbox fault diagnosis via iterative atomic decomposition thresholding

The vibration signals from complex structures such as wind turbine (WT) planetary gearboxes are intricate. Reliable analysis of such signals is the key to success in fault detection and diagnosis for complex structures. The recently proposed iterative atomic decomposition thresholding (IADT) method has shown to be effective in extracting true constituent components of complicated signals and in suppressing background noise interferences. In this study, such properties of the IADT are exploited to analyze and extract the target signal components from complex signals with a focus on WT planetary gearboxes under constant running conditions. Fault diagnosis for WT planetary gearboxes has been a very important yet challenging issue due to their harsh working conditions and complex structures. Planetary gearbox fault diagnosis relies on detecting the presence of gear characteristic frequencies or monitoring their magnitude changes. However, a planetary gearbox vibration signal is a mixture of multiple complex components due to the unique structure, complex kinetics and background noise. As such, the IADT is applied to enhance the gear characteristic frequencies of interest, and thereby diagnose gear faults. Considering the spectral properties of planetary gearbox vibration signals, we propose to use Fourier dictionary in the IADT so as to match the harmonic waves in frequency domain and pinpoint the gear fault characteristic frequency. To reduce computing time and better target at more relevant signal components, we also suggest a criterion to estimate the number of sparse components to be used by the IADT. The performance of the proposed approach in planetary gearbox fault diagnosis has been evaluated through analyzing the numerically simulated, lab experimental and on-site collected signals. The results show that both localized and distributed gear faults, both the sun and planet gear faults, can be diagnosed successfully.     

A time domain approach to diagnose gearbox fault based on measured vibration signals

Spectral analysis techniques to process vibration measurements have been widely studied to characterize the state of gearboxes. However, in practice, the modulated sidebands resulting from the local gear fault are often difficult to extract accurately from an ambiguous/blurred measured vibration spectrum due to the limited frequency resolution and small fluctuations in the operating speed of the machine that often occurs in an industrial environment. To address this issue, a new time-domain diagnostic algorithm is developed and presented herein for monitoring of gear faults, which shows an improved fault extraction capability from such measured vibration signals. This new time-domain fault detection method combines the fast dynamic time warping (Fast DTW) as well as the correlated kurtosis (CK) techniques to characterize the local gear fault, and identify the corresponding faulty gear and its position. Fast DTW is employed to extract the periodic impulse excitations caused from the faulty gear tooth using an estimated reference signal that has the same frequency as the nominal gear mesh harmonic and is built using vibration characteristics of the gearbox operation under presumed healthy conditions. This technique is beneficial in practical analysis to highlight sideband patterns in situations where data is often contaminated by process/measurement noises and small fluctuations in operating speeds that occur even at otherwise presumed steady-state conditions. The extracted signal is then resampled for subsequent diagnostic analysis using CK technique. CK takes advantages of the periodicity of the geared faults; it is used to identify the position of the local gear fault in the gearbox. Based on simulated gear vibration signals, the Fast DTW and CK based approach is shown to be useful for condition monitoring in both fixed axis as well as epicyclic gearboxes. Finally the effectiveness of the proposed method in fault detection of gears is validated using experimental signals from a planetary gearbox test rig. For fault detection in planetary gear-sets, a window function is introduced to account for the planet motion with respect to the fixed sensor, which is experimentally determined and is later employed for the estimation of reference signal used in Fast DTW algorithm.     

Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.     

Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring

Accessing difficulties and harsh environments require more advanced condition monitoring techniques to ensure the high availability of offshore wind turbines. Empirical mode decomposition (EMD) has been shown to be a promising technique for meeting this need. However, EMD was developed for one-dimensional signals, unable to carry out an information fusion function which is of importance to reach a reliable condition monitoring conclusion. Therefore, bivariate empirical mode decomposition (BEMD) is investigated in this paper to assess whether it could be a better solution for wind turbine condition monitoring. The effectiveness of the proposed technique in detecting machine incipient fault is compared with EMD and a recently developed wavelet-based ‘energy tracking’ technique. Experiments have shown that the proposed BEMD-based technique is more convenient than EMD for processing shaft vibration signals, and more powerful than EMD and wavelet-based techniques in terms of processing the non-stationary and nonlinear wind turbine condition monitoring signals and detecting incipient mechanical and electrical faults.     

A Fault Detection Method for Electrohydraulic Switch Machine Based on Oil-Pressure-Signal-Sectionalized Feature Extraction

A turnout switch machine is key equipment in a railway, and its fault condition has an enormous impact on the safety of train operation. Electrohydraulic switch machines are increasingly used in high-speed railways, and how to extract effective fault features from their working condition monitoring signal is a difficult problem. This paper focuses on the sectionalized feature extraction method of the oil pressure signal of the electrohydraulic switch machine and realizes the fault detection of the switch machine based on this method. First, the oil pressure signal is divided into three stages according to the working principle and action process of the switch machine, and multiple features of each stage are extracted. Then the max-relevance and min-redundancy (mRMR) algorithm is applied to select the effective features. Finally, the mini batch k-means method is used to achieve unsupervised fault diagnosis. Through experimental verification, this method can not only derive the best sectionalization mode and feature types of the oil pressure signal, but also achieve the fault diagnosis and the prediction of the status of the electrohydraulic switch machine.     

A multiscale noise tuning stochastic resonance for fault diagnosis in rolling element bearings

Condition monitoring of rotating machinery is important to extend the mechanical system's reliability and operational life. However, in many cases, useful information is often overwhelmed by strong background noise and the defect frequency is difficult to be extracted. Stochastic resonance (SR) is used as a noise-assisted tool to amplify weak signals in nonlinear systems, which can detect weak signals of interest submerged in the noise. The multiscale noise tuning SR (MSTSR), which is originally based on discrete wavelet transform (DWT), has been applied to identify the fault characteristics and has also increased the signal-to-noise ratio (SNR) improvement of SR. Therefore, a novel tri-stable SR method with multiscale noise tuning (MST) is proposed to extract fault signatures for fault diagnosis of rotating machinery. The wavelet packets transform (WPT) based MST can obtain better denoising effect and higher SNR of resonance output compared with the traditional SR method. Thus the proposed method is well-suited for enhancement of rotating machine fault identification, whose effectiveness has been verified by means of practical vibration signals carrying fault information from bearings. Finally, it can be concluded that the proposed method has practical value in engineering.     

Fault Diagnosis of a Wind Turbine Gearbox Based on Improved Variational Mode Algorithm and Information Entropy

The working environment of wind turbine gearboxes is complex, complicating the effective monitoring of their running state. In this paper, a new gearbox fault diagnosis method based on improved variational mode decomposition (IVMD), combined with time-shift multi-scale sample entropy (TSMSE) and a sparrow search algorithm-based support vector machine (SSA-SVM), is proposed. Firstly, a novel algorithm, IVMD, is presented for solving the problem where VMD parameters (K and α) need to be selected in advance, which mainly contains two steps: the maximum kurtosis index is employed to preliminarily determine a series of local optimal decomposition parameters (K and α), then from the local parameters, the global optimum parameters are selected based on the minimum energy loss coefficient (ELC). After decomposition by IVMD, the raw signal is divided into K intrinsic mode functions (IMFs), the optimal IMF(s) with abundant fault information is (are) chosen based on the minimum envelopment entropy criterion. Secondly, the time-shift technique is introduced to information entropy, the time-shift multi-scale sample entropy algorithm is applied for the analysis of the complexity of the chosen optimal IMF and extract fault feature vectors. Finally, the sparrow search algorithm, which takes the classification error rate of SVM as the fitness function, is used to adaptively optimize the SVM parameters. Next, the extracted TSMSEs are input into the SSA-SVM model as the feature vector to identify the gear signal types under different conditions. The simulation and experimental results confirm that the proposed method is feasible and superior in gearbox fault diagnosis when compared with other methods.     

基于极限学习机与规则推理的NPC三电平逆变器二级故障诊断方法

针对NPC三电平逆变器故障诊断问题,提出一种基于极限学习机与规则推理的二级故障诊断方法。分析了依据输出电流诊断故障的可区分性,以及故障模式的分类。然后对输出电流提取故障特征,并采用极限学习机完成故障初级分类。对于初级分类结果为电流不可区分故障情况,再根据桥臂电压信息运用规则推理法实现故障二级精确诊断。诊断实验表明,该方法能够实现NPC三电平的多模式故障诊断,且故障诊断方法简单、定位精确、快速、鲁棒性强。     

基于紫外光谱检测的GIS内多类故障早期预警

气体绝缘开关(GIS)故障预警强调异常识别的实效性和现场适用性,是精确诊断的基础。研究GIS内局部放电(PD)及过热故障的统一预警方法具有重要意义。提出将SO₂作为GIS内部两类故障公共的特征分解物,并应用紫外光谱(UV)测量SO₂实现多故障早期预警。一阶导数法和Savitzky-Golay滤波被用于快速基线校正和光谱平滑,290～310 nm被选为特征区进行SO₂检测及定量。UV检测可避免诸如SF₆及SOF₂、SO₂F₂等分解物的干扰,便于从复杂的SF₆分解物组分中识别微量SO₂特征,测量系统结构简单、维护成本低、适于现场检测。通过模拟SF₆气体在两类PD缺陷及200～400 ℃过热条件下的分解实验,证实所提UV预警方法的有效性：两类GIS故障条件下都会稳定的生成SO₂。对UV预警后的故障诊断,借助傅里叶变换红外光谱仪及气相色谱仪,确认PD及过热故障下的分解物组分存在差异,并据此简要分析了故障类型辨识方法。PD分解物以SOF₂和SO₂F₂为主,SO₂含量远小于SOF₂,放电涉及环氧树脂时含碳分解物含量上升。过热材质为不锈钢时,SF₆约在300 ℃发生分解,300～400 ℃内的过热分解物主要为SO₂和SOF₂,350 ℃以上SO₂生成速率明显快于PD时。随着GIS内故障持续恶化,SO₂含量均呈现稳定上升趋势。在UV预警后,可利用获得的SO₂生成特性初步辨识故障类型。     

Adaptive autoregressive modeling of non-stationary vibration signals under distinct gear states. Part 2: experimental analysis

Parametric time-frequency representation based on parametric models is more desirable for presenting highly precise time-frequency domain information due to its high-resolution property. However, the sensitivity and robustness of parametric models, in particular the parametric models on the basis of advanced adaptive filtering algorithms, has never been investigated for on-line condition monitoring of rotating machinery. Part 1 of this study proposed three adaptive parametric models based on three advanced adaptive filtering algorithms. Part 2 of this study is concerned with the effectiveness of the proposed models under distinct gear states, especially the highly non-stationary conditions accrued from advanced gear faults. Four gear states are considered: healthy state, adjacent gear tooth failure, non-adjacent gear tooth failure and distributed gear tooth failure. The vibration signals used in this study include the time-domain synchronous averaging signal and gear motion residual signal for each considered gear state. The test results demonstrate that the optimum filter behavior can readily be attained and the white Gaussian assumption of innovations can relatively be easily guaranteed for the NAKF-based model under distinct gear states and a wide variety of model initializations. On the other hand, the EKF- and MEKF-based models are capable of generating more accurate time-frequency representations than the NAKF-based model, but in general the optimality condition for white Gaussian assumption cannot be guaranteed for these two advanced models. Therefore, the NAKF-based model is preferred for automatic condition monitoring due to its appealing robustness to distinct gear states and arbitrary model initializations, whereas the EKF- and MEKF-based models are desirable when accurate time-frequency representation is concerned.     

Vibration signal models for fault diagnosis of planetary gearboxes

A thorough understanding of the spectral structure of planetary gear system vibration signals is helpful to fault diagnosis of planetary gearboxes. Considering both the amplitude modulation and the frequency modulation effects due to gear damage and periodically time variant working condition, as well as the effect of vibration transfer path, signal models of gear damage for fault diagnosis of planetary gearboxes are given and the spectral characteristics are summarized in closed form. Meanwhile, explicit equations for calculating the characteristic frequency of local and distributed gear fault are deduced. The theoretical derivations are validated using both experimental and industrial signals. According to the theoretical basis derived, manually created local gear damage of different levels and naturally developed gear damage in a planetary gearbox can be detected and located.     

基于动态核主元分析的大功率LED阵列动态光源在线状态观测与故障诊断

大功率LED阵列动态光源工作过程中光电热参数具有不确定性和时变时滞非线性特点,利用动态核主元分析方法（DKPCA）对大功率LED阵列动态光源进行在线状态观测与故障诊断能有效地捕捉观测数据的非线性和相关性特征,根据历史数据的主元特征计算出的统计量阈值和在线数据的统计特征实现故障检测,利用重构贡献图法实现故障的分离。仿真实验表明,对大功率LED阵列动态光源典型的传感器和执行器故障进行有效监测和诊断相对于核主元分析方法对故障更为敏感,故障检测率最高提高了7.5%,误检率下降了4.2%。     

Simulation and analysis of machinery fault signals

Machinery condition monitoring is rapidly finding applications in all branches of industry. In particular, vibration monitoring is playing an increasingly important role as a tool for assisting with predictive and preventive maintenance and for improving operation efficiency of plant. Condition monitoring systems are used for the detection of incipient failure and the diagnosis of the nature of faults in operating machinery. However, for these systems to be reliable an improved understanding is required of the vibration signatures produced by machinery failure mechanisms and of methods for the interpretation of these signals. Many types of fault produce vibration signals which are impulsive in nature and which may be buried in background noise. A method is described for simulating this type of signal and modelling the various stages of incipient failure. Statistical and spectral analysis are used to describe the fault development. The influence of machinery frequency response characteristics on signal transmission from the damaged are to the measurement point are also considered.     

Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis

This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate.     

A fault diagnosis scheme of rolling element bearing based on near-field acoustic holography and gray level co-occurrence matrix

Vibration signal analysis is the most widely used technique in condition monitoring or fault diagnosis, whereas in some cases vibration-based diagnosis is restrained because of its contact measurement. Acoustic-based diagnosis (ABD) with non-contact measurement has received little attention, although sound field may contain abundant information related to fault pattern. A new scheme of ABD for rolling element bearing fault diagnosis based on near-field acoustic holography (NAH) and gray level co-occurrence matrix (GLCM) is presented in this paper. It focuses on applying the distribution information of sound field to bearing fault diagnosis. A series of rolling element bearings with different types of fault are experimentally studied. Sound fields and corresponding acoustic images in different bearing conditions are obtained by fast Fourier transform (FFT) based NAH. GLCM features are extracted for capturing fault pattern information underlying sound fields. The optimal feature subset selected by improved F-score is fed into multi-class support vector machine (SVM) for fault pattern identification. The feasibility and effectiveness of our proposed scheme is demonstrated on the good experimental results and the comparison with the traditional ABD method. Considering test cost, the quantized level and the number of GLCM features for each characteristic frequency is suggested to be 4 and 32, respectively, with the satisfactory accuracy rate 97.5%.     

Gearbox vibration monitoring using extended Kalman filters and hypothesis tests

The efficiency of many maintenance programs is heavily dependent on the detection accuracy of the condition monitoring system. Condition indicators that are sensitive to environmental or operational variables of no interest will inevitably reflect irrelevant fluctuations and thus mislead the subsequent analysis. In consideration of this phenomenon, a fully automatic and robust vibration monitoring system for gearboxes is proposed in this study. The primary objective here is on how to exclude the effects of variable load conditions. The proposed technique features a number of appealing advantages, which include extended Kalman filter-based time-varying autoregressive modeling, automatic autoregressive model order selection with the aid of a non-paired two-sample Satterthwaite's t′-test, a highly effective and robust condition indicator (the means of one-sample Kolmogorov-Smirnov goodness-of-fit test), and an automatic alert generating mechanism for incipient gear faults with the aid of a Wilcoxon rank-sum test. Two sets of entire lifetime gearbox vibration monitoring data with distinct variable load conditions were used for experimental validation. The proposed condition indicator was compared with other well-known and/or recently proposed condition indicators. The results demonstrate excellent performance of the proposed technique in four aspects: the effectiveness of identifying the optimum model order, a minimum number of false alerts, constant behavior under variable load conditions, and to some extent an early alert for incipient gear faults. Furthermore, the proposed condition indicator can be directly employed by condition-based maintenance programs as a condition covariate for operational maintenance decision analysis. It provides a quantitative and more efficient means for exchanging condition information with maintenance programs in comparison with the widely used non-parametric time-frequency techniques such as wavelets, which rely on visual inspection.     

Predicting vibration signals of automobile engine using chaos theory

Condition monitoring and life prediction of the vehicle engine is an important and urgent problem during the vehicle development process. The vibration signals that are closely associated with the engine running condition and its development trend are complex and nonlinear. The chaos theory is used to treat the nonlinear dynamical system recently. A novel chaos method in conjunction with SVD (singular value decomposition) denoising skill are used to predict the vibration time series. Two types of time series and their prediction errors are provided to illustrate the practical utility of the method.     

Predicting vibration signals of automobile engine using chaos theory

Condition monitoring and life prediction of the vehicle engine is an important and urgent problem during the vehicle development process. The vibration signals that are closely associated with the engine running condition and its development trend are complex and nonlinear. The chaos theory is used to treat the nonlinear dynamical system recently. A novel chaos method in conjunction with SVD (singular value decomposition)denoising skill are used to predict the vibration time series. Two types of time series and their prediction errors are provided to illustrate the practical utility of the method.     

Analysis and compression of six-dimensional gyrokinetic datasets using higher order singular value decomposition

Higher order singular value decomposition (HOSVD) is explored as a tool for analyzing and compressing gyrokinetic data. An efficient numerical implementation of an HOSVD algorithm is described. HOSVD is used to analyze the full six-dimensional (three spatial, two velocity space, and time dimensions) gyrocenter distribution function from gyrokinetic simulations of ion temperature gradient, electron temperature gradient, and trapped electron mode driven turbulence. The HOSVD eigenvalues for the velocity space coordinates decay very rapidly, indicating that only a few structures in velocity space can capture the most important dynamics. In almost all of the cases studied, HOSVD extracts parallel velocity space structures which are very similar to orthogonal polynomials. HOSVD is also used to compress gyrokinetic datasets, an application in which it is shown to significantly outperform the more commonly used singular value decomposition. It is shown that the effectiveness of the HOSVD compression improves as the dimensionality of the dataset increases.     

基于SVD的超精密工件自寻位加工算法能力评价

为进一步改善超精密表面修形的最终精度、效率与成本,优化超精密自寻位加工的工艺方向与工艺决策过程, 开展了对超精密工件的自寻位加工算法点云融合过程的定量评价研究,提出了基于SVD的自寻位加工算法能力评价方法。首先基于运动学方法建立了点云融合的矩阵表示,分别对平动、转动、复合运动等情况建立了自寻位结果的转换矩阵表示,获得自寻位点云融合转换矩阵;进而对转换矩阵进行奇异值分解得到转换矩阵的奇异值列表;最后将列表中最大奇异值用以表征自寻位加工算法的能力。通过对某型超精密叶片在平动、转动和复合运动、共计1078组自由放置状态进行分析,发现所提出的评价指标在独立平动和独立转动两种任意放置情况下能够正确地表征自寻位加工算法的工艺能力。对于独立平动情况,自寻位加工算法能够正常定位加工,其最大奇异值也与预设偏差较小;对于独立转动情况,当旋转角度小于45°时,均能够正确地进行自寻位加工,最大奇异值差值也趋近于零,旋转角度超过45°时,算法的自寻位加工能力恶化,这一特性能够被所提指标正确捕捉。对于由平动和转动构成的复合运动而言,所提指标显示约35%的情况能够正确进行自寻位加工,其余情况无法进行正确的自寻位加工。结果表明本文所提方法建立的指标能够正确表征自寻位加工算法能力。