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.     

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.     

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.     

Roller bearing acoustic signature extraction by wavelet packet transform,applications in fault detection and size estimation

Continuous online monitoring of rotating machines is necessary to assess real-time health conditions so as to enable early detection of operation problems and thus reduce the possibility of downtime. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings, comprises four main stages which are, statistical analysis, fault diagnostics, defect size calculation, and prognostics. In this paper the effect of defect size, operating speed, and loading conditions on statistical parameters of acoustic emission (AE) signals, using design of experiment method (DOE), have been investigated to select the most sensitive parameters for diagnosing incipient faults and defect growth on rolling element bearings. A modified and effective signal processing algorithm is designed to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transform (WPT) and envelope detection. Results show the superiority of the developed algorithm and its effectiveness in extracting bearing characteristic frequencies from the raw acoustic emission signals masked by background noise under different operating conditions. To experimentally measure the defect size on rolling element bearings using acoustic emission technique, the proposed method along with spectrum of squared Hilbert transform are performed under different rotating speeds, loading conditions, and defect sizes to measure the time difference between the double AE impulses. Measurement results show the power of the proposed method for experimentally measuring size of different fault shapes using acoustic emission signals.     

Constrained adaptive lifting and the CAL4 metric for helicopter transmission diagnostics

This paper presents a methodology for detecting and diagnosing gear faults in the planetary stage of a helicopter transmission. This diagnostic technique is based on the constrained adaptive lifting (CAL) algorithm, an adaptive manifestation of the lifting scheme. Lifting is a time domain, prediction-error realization of the wavelet transform that allows for greater flexibility in the construction of wavelet bases. Adaptivity is desirable for gear diagnostics as it allows the technique to tailor itself to a specific transmission by selecting a set of wavelets that best represent vibration signals obtained while the gearbox is operating under healthy-state conditions. However, constraints on certain basis characteristics are necessary to enhance the detection of local wave-form changes caused by certain types of gear damage. The proposed methodology analyzes individual tooth-mesh waveforms from a healthy-state gearbox vibration signal that was generated using the vibration separation synchronous signal-averaging algorithm. Each waveform is separated into analysis domains using zeros of its slope and curvature. The bases selected in each analysis domain are chosen to minimize the prediction error, and constrained to have approximately the same-sign local slope and curvature as the original signal. The resulting set of bases is used to analyze future-state vibration signals and the lifting prediction error is inspected. The constraints allow the transform to effectively adapt to global amplitude changes, yielding small prediction errors. However, local waveform changes associated with certain types of gear damage are poorly adapted, causing a significant change in the prediction error. A diagnostic metric based on the lifting prediction error vector termed CAL4 is developed. The CAL diagnostic algorithm is validated using data collected from the University of Maryland Transmission Test Rig and the CAL4 metric is compared with the classic metric FM4.     

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.     

Vibro-acoustic propagation of gear dynamics in a gear-bearing-housing system

This work developed a computational process to predict noise radiation from gearboxes. It developed a system-level vibro-acoustic model of an actual gearbox, including gears, bearings, shafts, and housing structure, and compared the results to experiments. The meshing action of gear teeth causes vibrations to propagate through shafts and bearings to the housing radiating noise. The vibration excitation from the gear mesh and the system response were predicted using finite element and lumped-parameter models. From these results, the radiated noise was calculated using a boundary element model of the housing. Experimental vibration and noise measurements from the gearbox confirmed the computational predictions. The developed tool was used to investigate the influence of standard rolling element and modified journal bearings on gearbox radiated noise.     

Subway Gearbox Fault Diagnosis Algorithm Based on Adaptive Spline Impact Suppression

In the signal processing of real subway vehicles, impacts between wheelsets and rail joint gaps have significant negative effects on the spectrum. This introduces great difficulties for the fault diagnosis of gearboxes. To solve this problem, this paper proposes an adaptive time-domain signal segmentation method that envelopes the original signal using a cubic spline interpolation. The peak values of the rail joint gap impacts are extracted to realize the adaptive segmentation of gearbox fault signals when the vehicle was moving at a uniform speed. A long-time and unsteady signal affected by wheel–rail impacts is segmented into multiple short-term, steady-state signals, which can suppress the high amplitude of the shock response signal. Finally, on this basis, multiple short-term sample signals are analyzed by time- and frequency-domain analyses and compared with the nonfaulty results. The results showed that the method can efficiently suppress the high-amplitude components of subway gearbox vibration signals and effectively extract the characteristics of weak faults due to uniform wear of the gearbox in the time and frequency domains. This provides reference value for the gearbox fault diagnosis in engineering practice.     

Identification of multiple transient faults based on the adaptive spectral kurtosis method

This paper proposes a multi-fault detection method based on the adaptive spectral kurtosis (ASK) analysis of the vibration signal from single sensor. A theoretical model of multiple bearing faults is established in this paper. Compared with the kurtogram and protrugram techniques, the proposed method can more effectively extract signatures of multiple bearing faults even in the presence of strong background noise. The performance of the proposed method in fault detection of the rolling element bearings is validated using simulation data and experimental signals from a bearing with multiple faults and two faulty bearings.     

滚动轴承声信号特征提取和诊断试验研究

为解决振动检测方法不能有效识别低速旋转机械滚动轴承故障问题,利用声发射检测方法,建立了滚动轴承低速声发射信号采集试验装置,对模拟人工缺陷滚动轴承声发射信号进行了采集,进而对滚动轴承声发射信号进行总体平均经验模式分解,结合能量矩及相关系数法综合判断分解后各模态分量的真伪,据此提取出特征信号并做出其局部Hilbert边际谱,最后对滚动轴承各种故障模式进行诊断。试验结果表明该诊断方法能准确识别滚动轴承声发射信号故障频率,依据特征频率及幅值大小可对低速滚动轴承故障进行有效诊断。     

Crack level estimation approach for planetary gearbox based on simulation signal and GRA

The planetary gearbox is a critical mechanism in helicopter transmission systems. A crack level estimation methodology for planetary gearbox has been devised by integrating a physical model for simulation signal generation, a statistic algorithm for feature selection and a grey relational analysis (GRA) algorithm for damage level estimation. The physical model is used to generate simulation datasets for developing and evaluating the diagnostic scheme that will be further calibrated with real world test data during application. The proposed method was calibrated with historical test data and then validated with real-time test data. The estimation results coincide with the actual test records, showing the effectiveness and accuracy of this method in providing a novel way for more accurate health monitoring and condition prediction.     

Reference-free impedance-based crack detection in plates

Impedance-based damage detection techniques gained popularity among structural health monitoring (SHM) and nondestructive testing (NDT) communities due to their sensitivity to local damage and applicability to complex structures. In general, conventional impedance-based techniques identify damage by comparing “current” impedance signals with “baseline” ones obtained from the pristine condition of a structure. However, in-situ structures are often subject to changing temperature and loading conditions that can adversely affect measured impedance signals and cause false-alarms. In this paper, a “reference-free” impedance method, which does not require direct comparison of the current impedance signals with the previously obtained baseline impedance signals, is developed for crack detection in a plate-like structure. The proposed technique utilizes a single pair of PZTs collocated on the opposite surfaces of a structure to extract mode conversion produced by crack formation. Then, a reference-free damage classifier is developed and performed on the extracted mode conversion for instantaneous damage diagnosis. Numerical simulations and experimental tests have been conducted explicitly considering varying temperature and loading conditions to demonstrate the robustness of the proposed damage detection technique under varying operational and environmental conditions.     

Influence of the temporal response of the detection system on time-resolved laser-induced incandescence signal evolutions

Time-resolved LII (TIRE-LII) measurements are performed simultaneously at two different wavelengths in a sooting, premixed, flat acetylene flame under atmospheric pressure conditions. The influence of temporal response of the detection system on the measured evolution of the LII signal is discussed. The effect of the temporal response on the determination of particle size distributions is quantified for data evaluation starting some nanoseconds after the maximum particle ensemble temperature. Furthermore, it is investigated how the temporal response of a slow detection system affects the determination of accommodation parameters, e.g. thermal accommodation coefficients, and evaporation coefficients, if TIRE-LII signals are modelled including particle heating as well as particle cooling, and if deconvolution techniques are not applied to the measured LII signal. PACS 85.60.Gz     

频域盲源分离与波束形成结合抑制方向性强干扰方法

针对方向性强干扰严重影响无源声呐弱目标检测的问题,提出了频域盲源分离与波束形成结合的干扰抑制方法:以子带分解的方法实现宽带干扰抑制。对每个子带进行频域盲源分离,并估计出各分离信号的方位,将与给定强干扰方位匹配的分离信号置零,利用估计的解混矩阵和处理后的分离信号重构回阵元域信号并进行波束形成实现目标方位估计。声呐模拟器数据与海试数据验证结果表明,相对于传统零陷常规波束形成与零陷最小方差无失真响应波束形成方法有2 dB以上的增益,约6 dB的背景级降低,证明该方法在抑制方向性强干扰方面是有效的。      

Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults

Wind turbine gearboxes operate in harsh environments; therefore, the resulting gear vibration signal has characteristics of strong nonlinearity, is non-stationary, and has a low signal-to-noise ratio, which indicates that it is difficult to identify wind turbine gearbox faults effectively by the traditional methods. To solve this problem, this paper proposes a new fault diagnosis method for wind turbine gearboxes based on generalized composite multiscale Lempel–Ziv complexity (GCMLZC). Within the proposed method, an effective technique named multiscale morphological-hat convolution operator (MHCO) is firstly presented to remove the noise interference information of the original gear vibration signal. Then, the GCMLZC of the filtered signal was calculated to extract gear fault features. Finally, the extracted fault features were input into softmax classifier for automatically identifying different health conditions of wind turbine gearboxes. The effectiveness of the proposed method was validated by the experimental and engineering data analysis. The results of the analysis indicate that the proposed method can identify accurately different gear health conditions. Moreover, the identification accuracy of the proposed method is higher than that of traditional multiscale Lempel–Ziv complexity (MLZC) and several representative multiscale entropies (e.g., multiscale dispersion entropy (MDE), multiscale permutation entropy (MPE) and multiscale sample entropy (MSE)).     

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.     

基于声发射的光学元件损伤监测方法研究

光学元件损伤是限制激光通量水平提高的重要因素之一。为快速、准确地检测光学元件损伤是否产生,支撑光学元件循环修复策略的使用,研究并提出了基于声发射技术的光学元件损伤检测方法,通过研究光学元件损伤产生的声发射信号特征,判断光学元件是否发生损伤,使用一种基于二次相关和相关峰精确插值（FICP）的时延估计算法,通过仿真验证了该算法的可行性,结合时差定位原理建立了损伤位置求解方法,并通过实验进行了验证。研究结果表明:该方法能从监测信号中快速地获得损伤的位置估计,其平均定位误差为8.61 mm,计算时间为0.143 s/次,对大口径光学元件的损伤在线监测具有应用潜力。     

Integrated modulation intensity distribution as a practical tool for condition monitoring

Modulations present in vibration signals generated by rotating machinery might carry a lot of useful information about objects’ technical condition. It has been proven that both gearboxes and rolling element bearing (REB) faults manifest themselves as modulations. The paper describes a technique for detection of modulations in vibroacoustic signals, called modulation intensity distribution (MID), which is a function that combines multiple spectral correlation densities in one way or another, depending on the application. Additionally, the paper describes a functional obtained by integrating an MID (denoted by IMID) that has the advantage of being a function of only one frequency variable instead of two. The paper investigates the utility of the MID as an indicator for detection of the presence of rolling element bearing faults in high noise environments. For the purpose of testing, a wind turbine that suffered both advanced gearbox fault and early stage of bearing fault was chosen. Additionally, the paper undertakes the problem of application of the proposed tool in an industrial condition-monitoring system. In order to show the behavior of cyclic components generated by the turbine under study over a long period of time, the set of MIDs integrated over full range of potential carrier signals was presented as a cascade plot.     

Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal

Rolling bearing faults are one of the major reasons for breakdown of industrial machinery and bearing diagnosing is one of the most important topics in machine condition monitoring.The main problem in industrial application of bearing vibration diagnostics is the masking of informative bearing signal by machine noise. The vibration signal of the rolling bearing is often covered or concealed by other structural vibrations sources, such as gears. Although a number of vibration diagnostic techniques have been developed over the last several years, in many cases these methods are quite complicated in use or only effective at later stages of damage development. This paper presents an EMD-based rolling bearing diagnosing method that shows potential for bearing damage detection at a much earlier stage of damage development.By using EMD a raw vibration signal is decomposed into a number of Intrinsic Mode Functions (IMFs). Then, a new method of IMFs aggregation into three Combined Mode Functions (CMFs) is applied and finally the vibration signal is divided into three parts of signal: noise-only part, signal-only part and trend-only part. To further bearing fault-related feature extraction from resultant signals, the spectral analysis of the empirically determined local amplitude is used. To validate the proposed method, raw vibration signals generated by complex mechanical systems employed in the industry (driving units of belt conveyors), including normal and fault bearing vibration data, are used in two case studies. The results show that the proposed rolling bearing diagnosing method can identify bearing faults at early stages of their development.     

Condition monitoring of a single-stage gearbox with artificially induced gear cracks utilizing on-line vibration and acoustic emission measurements

The condition monitoring of a lab-scale, single stage, gearbox using different non-destructive inspection methodologies and the processing of the acquired waveforms with advanced signal processing techniques is the aim of the present work. Acoustic emission (AE) and vibration measurements were utilized for this purpose. The experimental setup and the instrumentation of each monitoring methodology are presented in detail. Emphasis is given on the signal processing of the acquired vibration and acoustic emission signals in order to extract conventional as well as novel parameters-features of potential diagnostic value from the monitored waveforms. Innovative wavelet-based parameters-features are proposed utilizing the discrete wavelet transform. The evolution of selected parameters/features versus test time is provided, evaluated and the parameters with the most interesting diagnostic behaviour are highlighted. The differences in the parameters evolution of each NDT technique are discussed and the superiority of AE over vibration recordings for the early diagnosis of natural wear in gear systems is concluded.