Gearbox Vibration Research Articles

Методика модельного исследования функционирования редуктора

An imitation model of a gear reducer is considered, taking into account its gear ratio, multi-mass, friction, and gearing errors. The structures of individual components and the general model of the reducer is presented. The spectrum of speed fluctuations of the output link of the reducer is obtained and a comparison is made with the spectrum of vibration speed obtained using a vibration analyzer on a real object. The approach proposed in the work makes it possible to establish the nature of the reasons causing the gearbox vibration.

Enhancing gearbox vibration signals under time-varying operating conditions by combining a whitening procedure and a synchronous processing method

Condition monitoring is very important to avoid unexpected breakdowns of expensive rotating machines found in the power generation and mining industries. The fault information in vibration signals is often buried in low energy frequency bands. In addition to this, time-varying operating conditions and the presence of impulsive components result in the signal to have time-varying spectral content, which impedes the condition inference process. The instantaneous power spectrum is a powerful method that for bearing and gear damage detection under time-varying operating conditions. In this work, we propose a new method of using the instantaneous power spectrum for improved fault diagnosis under time-varying conditions by enhancing the fault information in the vibration signals. It is shown on experimental datasets that the proposed method enhances the fault information in the signal, whereafter it can be used to obtain reliable condition indicators under time-varying operating conditions.

Multifractal detrended fluctuation analysis based on optimized empirical mode decomposition for complex signal analysis

Multifractal detrended fluctuation analysis (MFDFA) is a powerful tool to uncover nature of complex signals. However, MFDFA runs into difficulties in determining the type of the fitting polynomial trend and making the fitting polynomial trend continuous. To solve these problems, MFDFA based on empirical mode decomposition (MFDFAemd) is developed. Unfortunately, MFDFAemd suffers from negative frequency and difficulties in selecting fractal components. To overcome deficiencies of these traditional methods, this paper proposes a novel version of MFDFA based on optimized empirical mode decomposition (MFDFAoemd). In MFDFAoemd, instantaneous frequency of a signal component is estimated using normalized Hilbert transform and Teager energy operator and a criterion for distinguishing a fractal component from a truly noisy component is established. Moreover, the effectiveness of MFDFAoemd is compared with MFDFA and MFDFAemd by probing a multifractal signal generated by a multifractal cascade model. The comparison displays superiority of MFDFAoemd over MFDFA and MFDFAemd. Next, the performance of MFDFAoemd is further benchmarked against MFDFA and MFDFAemd by analyzing gearbox vibration signals containing different types of single-point fault and those containing different types of compound gear fault. The results show that MFDFAoemd can remedy the shortages of MFDFA and MFDFAemd and has an advantage in diagnosing both single-point gear faults and compound gear faults.

Effect of bolt constraint of ring gear on the vibration response of the planetary gearbox

The lumped parameter model (LPM) is a common modeling method for simulating the vibration signal of the planetary gearbox. Generally, the simulation results are not directly used for the subsequent analysis, because two factors need to be considered: the transmission paths of vibration signals and the amplitude modulation effect of the carrier. However, both of these factors are subjectively affected. In order to overcome this problem, a new method for generating the gearbox vibration signal is proposed. Based on LPM, the vibration displacement of each component is solved, and meshing forces of the internal gear pairs are calculated. Then, the ring gear with bolt constraints is simplified to the Euler-Bernoulli beam, and the vibration signal of any point on the ring gear is established by solving the vibration of the beam. Subsequently, based on the new signal model, the transmission mechanism, the amplitude modulation and the overlapping phenomenon of the vibration signal, and the effect of bolt constraint on the vibration of the gearbox are analyzed. Finally, the correctness of the new method and the effect of bolt constraint on the vibration signal are verified by experiments.

Linking the modulation of gear mesh vibration in planetary gearboxes to manufacturing deviations

The gear mesh vibration caused by planetary geartrains is modulated. Small planetary gearboxes, commonly used in servo drives, are examined experimentally and numerically. The goal is to link the modulation of the gearbox vibration to the deviations from the ideal geometry caused by manufacturing and assembly. Operational modal analysis shows that, while the dynamical behavior of nominally identical gearboxes is very similar, their modulation is not. The modulation also strongly depends on the operating conditions, such as speed and torque. Therefore, the modulation is aggregated to a vibration signature. In the vibration signature, distinct features are present. These can be linked to the eccentricity of the gears using numerical multi-body simulation and correlation analysis.

Analytical vibration signal model and signature analysis in resonance region for planetary gearbox fault diagnosis

Complex components and multi-modulations characterize planetary gearbox vibration signals. As the vibration signals become time-varying under nonstationary conditions, it is difficult to extract fault features around meshing frequency harmonics, since they are all dependent on speed, and change under time-varying speed conditions. To address this issue, we turn to the resonance region and exploit the time-invariability of resonance frequency and the sideband symmetry around resonance frequency for planetary gearbox fault diagnosis under both constant and time-varying speed conditions. Gear fault generates impulses and thereby excites resonance of the planetary gearbox and even the measurement system. As such, gear fault information can be explored in the resonance region. To thoroughly understand gear fault vibration features in resonance region, the vibration signal is modeled as an amplitude modulation and frequency modulation (AM-FM) process, considering the multi-modulations due to gear fault, time-varying vibration transmission path, and time-varying angle between mesh line of action and measurement axis of vibration sensor. Furthermore, explicit time-varying Fourier spectra under nonstationary conditions are derived. Sideband symmetry features around resonance frequency can be utilized to detect gear fault. This is a major progress and contribution in contrast to reported researches that focus on meshing frequency or its harmonics only. Resonance frequency identification is the key to gear fault feature extraction in resonance region. An on-line resonance frequency identification method under time-varying speeds is proposed by exploiting the nature of resonance independence of running conditions. To effectively pinpoint the time-varying sidebands in the time-frequency domain, the iterative generalized demodulation (IGD) method is used to achieve high time-frequency resolution and to avoid both outer and inner interferences at the same time. The theoretical derivations and proposed method are validated through numerical simulation and lab experiments. Gear fault features are extracted in the resonance region of the planetary gearbox and the accelerometer under time-varying speed conditions.

Wind Turbine Drivetrain Gearbox Fault Diagnosis Using Information Fusion on Vibration and Current Signals

To improve the reliability of the conventional vibration-based wind turbine drivetrain gearbox fault diagnosis system, this article proposes a novel fault diagnosis method by fusing the information from gearbox vibration and generator current signals. First, the fault features contained in the gearbox vibration signals and the generator current signals are analyzed, respectively. Second, a multiclass support vector machine (SVM) model with probabilistic output is proposed to design two classifiers which output the probabilities of different gearbox fault types according to the input fault features extracted from the vibration signals and the current signals separately. Then, a nontrainable combiner and a trainable combiner are designed based on the Dempster-Shafer theory and the softmax regression technique, respectively, to fuse the information from the vibration and current SVM classifiers at decision level. The output of each combiner is the final diagnosis result. The proposed method is validated by experimental results obtained from a test gearbox with different types of faults. The validation results show that the proposed method can increase the fault diagnostic accuracy and is more robust than the conventional fault diagnosis systems that only use one type of signals.

Gearbox Vibration and Noise Analysis of Plastic Gear

Gearbox Vibration and Noise Analysis of Plastic Gear

Feature Extraction of Gearbox Vibration Signals Based on Multi-Channels Weighted Convolutional Neural Network

Feature Extraction of Gearbox Vibration Signals Based on Multi-Channels Weighted Convolutional Neural Network

New degradation feature extraction method of planetary gearbox based on alpha stable distribution

The planetary transmission system has been widely used in industry because of its various advantages. And the study on degradation feature extraction method of planetary gearbox is of major significance for mechanical system prognostics and health management (PHM). In this paper, the alpha stable distribution characteristics of planetary gearbox vibration signals in performance degradation process are verified. By observing the change of alpha stable distribution for planetary gearbox degradation experiment data, a new degradation feature extraction method based on alpha stable distribution is proposed, which is called the height of probability distribution (HPD). Through comparative analysis, it is determined that HPD has better linearity and less fluctuation compared with conventional degradation features in planetary gearbox accelerated degradation stage. Moreover, in the accelerated degradation stage, the degradation trend prediction result based on HPD is closer to the actual data than conventional degradation features no matter using Wiener-based or LSSVM-based prediction method. These conclusions indicate that the newly proposed HPD works well and gives accurate estimates for condition monitoring and degradation trend prediction of planetary gearbox.

A Methodology to Handle Spectral Smearing in Gearboxes Using Adaptive Mode Decomposition and Dynamic Time Warping

Tacho-less order tracking is an effective tool for fault detection in gearboxes operating under speed fluctuations. This technique's performance depends on extracting instantaneous shaft speed information from a complex multicomponent gearbox signal, which is first preprocessed by a bandpass filter. However, spectral overlap resulting from the time-varying frequency components makes it challenging to isolate the shaft speed harmonic mono-component using this approach. In order to overcome such issues, an adaptive tacho-less order-tracking (OT) method combining the variational mode decomposition (VMD) and the fast dynamic time warping (FDTW) is proposed in this article. The proposed method first decomposes the measured gearbox vibration signal using VMD to estimate the instantaneous shaft speed profile to construct the shaft vibration signal. The gearbox vibration signal is then resampled based on the optimal warping path obtained by FDTW, which performs an “elastic” stretching and compression along the time axis of the extracted shaft vibration signal with respect to a sinusoidal reference signal with a constant shaft rotational frequency. Finally, the presence of gear fault is detected by constructing the order spectrum of the resampled vibration signal. The effectiveness of the proposed algorithm is demonstrated using both simulation analysis and experimental validation using measurements from two different wind-turbine gearboxes collected under test and field conditions, respectively.

Multiscale Dynamic Fusion Global Sparse Network for Gearbox Fault Diagnosis

One-dimensional vibration signals are widely used for gearbox fault diagnosis to perform maintenance timely and then reduce various losses. The fault diagnosis accuracy of those classifiers is determined by the features extracted from the vibration signals. These typical deep neural networks (DNNs), e.g., convolutional neural network (CNN) and convolutional autoencoder (CAE) have been applied in machinery fault diagnosis. However, the feature learning of DNNs on the nonlinear vibration signals is still a big challenge for gearbox fault diagnosis. In this article, a new CNN, multiscale fusion global sparse network (MFGSNet) is proposed for feature extraction from vibration signals and gearbox fault diagnosis. First, a novel kernel dynamic fusion method based on multiscale convolution is proposed to extract defect features of vibration signals; second, a global dense connection method is proposed to generate features from shallow and deep layers; finally, a spare feature selection layer is embedded in the deep network to perform feature selection and dimension reduction on the shallow and deep features. The experimental results show that MFGSNet has good fault feature extraction performance on gearbox vibration signals. It performs much better on gearbox fault diagnosis than these typical DNNs, e.g., residual network (ResNet) and dense connection network (DenseNet).

Fault severity detection of a worm gearbox based on several feature extraction methods through a developed condition monitoring system

This study presents the severity detection of pitting faults on worm gearbox through the assessment of fault features extracted from the gearbox vibration data. Fault severity assessment on worm gearbox is conducted by the developed condition monitoring instrument with observing not only traditional but also multidisciplinary features. It is well known that the sliding motion between the worm gear and wheel gear causes difficulties about fault detection on worm gearboxes. Therefore, continuous monitoring and observation of different types of fault features are very important, especially for worm gearboxes. Therefore, in this study, time-domain statistics, the features of evaluated vibration analysis method and Poincaré plot are examined for fault severity detection on worm gearbox. The most reliable features for fault detection on worm gearbox are determined via the parallel coordinate plot. The abnormality detection during worm gearbox operation with the developed system is performed successfully by means of a decision tree.

Investigation of the characteristics of the component of vibration of gas turbine engine gearbox that causes fatigue failures of its structural elements in case of tooth flank wear

The paper presents an analysis of the component of vibration of the NK-12MP turboprop engine differential gearbox that is generated by the wear of the flanks of the teeth of the sun gear planet gear assembly pair and at certain values of its intensity may cause fatigue breakdown of the engines structural elements. A complex of diagnostic indicators is determined on the basis of this component. Its intensity is shown to be maximal in steady-state operation of the engine with the greatest run time. The data obtained by the spectrum of maxima are shown to have higher information content as compared to the autospectrum data. The complex of diagnostic indicators proposed on the basis of the component under consideration makes it possible to successfully control the technical condition of the differential gearbox by the defects of gear tooth flank wear.

Research on a Pattern Recognition Method of Cyclic GMM-FCM Based on Joint Time-Domain Features

The safety and reliability of the mechanical system in the industrial process determines the quality of products. Whether the fault can be identified and classified in time is the key to ensure the safe operation of the system and arrange the appropriate maintenance plan to restrain the deterioration of the fault. However, with the rapid development of manufacturing digitization, how to process large amounts of data quickly and accurately is faced with many problems. In this paper, a pattern recognition method of cyclic GMM-FCM (CGF) based on joint time-domain features is proposed. Firstly, the concept of joint time-domain features based on Vold-Kalman filter (VKF) is proposed. It retains the integrity of the signal components and avoids the problem of dimension disaster caused by anomaly detection, which laid a foundation for the accurate classification of sensitive feature sets. Secondly, a pattern recognition method of cyclic GMM-FCM is proposed. It can eliminate global and local outliers in sensitive feature sets and determine the number of FCM categories adaptively. It makes the classification result more reasonable and accurate. Finally, the effectiveness and superiority of the pattern recognition algorithm are verified by the gearbox vibration experiments in various states. The result shows that the method is feasible in engineering practice.

A practical methodology for enhancement and detection of transient faults in a gearbox without prior fault feature information

Gear fault diagnosis has been the focus of research in both academia and industry in the past. Due to the complicated structures of gearboxes, the vibration signals collected from the gearbox casings are comprised of multiple sources, in which the fault-related components may be easily masked by the Gaussian and non-Gaussian noises (e.g. random external shocks and gear meshing harmonics). Moreover, fault feature frequencies of interest cannot always be obtained in advance in practical applications. Therefore, it is necessary to choose an appropriate signal processing method for gear fault diagnosis. In this study, a new practical methodology is proposed to extract the transient fault features from a gearbox vibration signal corrupted by complicated interference without prior fault feature information. In the proposed method, a modified self-adaptive noise cancellation (MSANC) algorithm is first developed for removing the interferences of gear meshing harmonics adaptively, which can overcome the shortcomings of the traditional SANC in parameter selection and convergence performance. Based on the de-noised signal, a cyclic spectral analysis tool called the fast spectral correlation and assisted by the multipoint optimal minimum entropy deconvolution adjusted algorithm is then applied to enhance and detect the certain and potential gear faults. The effectiveness of the proposed method is demonstrated by a numerical simulation and two experimental scenarios that are compared to an advanced blind optimal demodulation band selection technique. The results reveal that the proposed methodology has good capability to detect mono- and multiple gear faults under complicated interferences, and can be regarded as an efficient method for practical gear fault diagnosis, especially for applications where the fault feature frequencies are unknown in advance.

A new autocorrelation-based strategy for multiple fault feature extraction from gearbox vibration signals

A gearbox plays a key role in rotating machines, and its fault diagnosis is of great significance to reduce maintenance costs, unnecessary casualties and property losses. However, due to the concurrence of heavy Gaussian and non-Gaussian background noises, the multi-fault diagnosis of gearboxes becomes a challenging task in practice. Besides, the components of multiple faults may be easily coupled with each other, thus making the weak fault unnoticeable or even undetectable. To address these issues, a new strategy for multi-fault detection of gearboxes called LEASgram, along with the cyclic feature index (CFI) which is a quantitative indicator for evaluation of the impulsive fault components in the log envelope’s auto-spectrum (LEAS) of a signal, is proposed in this study. In the proposed method, a series of frequency sub-bands are required to be divided and the one with the largest CFI value would be selected as the optimal frequency band for further demodulation analysis. Considering the merits of autocorrelation, log envelope, and the moving average process, the main advantage of the proposed LEASgram is that it has significant capability of multi-fault identification and separation under Gaussian and non-Gaussian interferences, whether the fault-related resonance frequency bands are overlapped or not. Two simulated vibration signals and two experimental scenarios were applied for testing the performance of the proposed method. The results reveal that the proposed strategy is superior to other existing methods in separating and extracting the impulsive fault features, thus demonstrating its effectiveness for gearbox multi-fault diagnosis.

Transmission error investigation of gearbox using rigid-flexible coupling dynamic model: Theoretical analysis and experiments

The transmission error (TE) is one of the most important excitations of gearbox vibration. In previous studies, simplified models composed of meshing gears alone were built to analyze the TE. However, the flexibility of other system components was ignored. In this study, a system-level TE investigation of gearbox is carried out by proposing a rigid-flexible coupling dynamic model. In this model, the meshing pair, flexible shaft, and bearing are established by a lumped mass element, beam element, and spring element, respectively. Housing flexibility is introduced into the analysis through model condensation, and a modal test is launched to verify its effectiveness. Moreover, a TE test rig is set up to compare the theoretical and experimental results. These results show that the TE increases with increasing load. Furthermore, the TE decreases when the housing flexibility is considered, especially when the gearbox is running at higher speed and when the housing is a thin-walled structure. Appropriately decreasing the stiffness of the housing can decrease the TE. Additionally, increasing the inner radius of hollow shaft simultaneously reduces the shaft weight and decreases the system TE.

Adaptive Densely Connected Convolutional Auto-Encoder-Based Feature Learning of Gearbox Vibration Signals

Vibration signals are widely utilized for machinery fault diagnosis. Typical deep neural networks (DNNs), e.g., convolutional neural networks (CNNs), perform well in feature learning from vibration signals and have been applied in gearbox fault diagnosis. However, the supervised-learning-based DNNs depend on a large amount of labeled data, and the extracted features consist of much noise and redundant information. In this article, a new DNN, adaptive densely connected convolutional auto-encoder (ADCAE), is proposed for feature extraction from 1-D vibration signals directly in an unsupervised-learning way. First, adaptive attention mechanism (AAM) is proposed for feature filtering. Second, a multiscale convolution based on AAM is proposed for fusion of multiscale information. Moreover, a new unsupervised-learning network, densely connected convolutional auto-encoder (DCAE), is further developed to improve the information flow between encoder and decoder, which significantly improves the performance of feature learning and fault classification. The results on two cases show that ADCAE has good feature extraction performance on gearbox vibration signals. It performs quite better on gearbox fault diagnosis than typical DNNs, e.g., residual CNN (ResNet), densely connected CNN (DenseNet).

의사-레이블기법을 이용한 기계학습기반 설비상태 모니터링과 진단

This paper deals with a monitoring problem of the hot strip roughing mill that consists of lots of mechanical and electrical units. In fault diagnosis, various and complex units could be failed with unknown reasons and the main issue is to how to detect efficiently the abnormal or failed condition of the equipments. In this paper, pseudonormal and pseudo-abnormal data sets were defined and labeled to apply a classification analysis algorithm to unlabeled equipment vibration data. The Lagrangian perspective is used to compare the initial baseline pseudonormal dataset with the pseudo-abnormal dataset determined over time to monitor the classification evaluation scale for the status of the equipment. We use the support vector machine (SVM), K-NN, and discriminant analysis together to diagnose equipment fault and give alarm to maintain the facility properly applied classification algorithms and a procedure are proposed to generate alarms through test of correlation coefficient, cluster performance, and mean difference between clusters. A case study is done based on the experimental field gear box vibration data.