Local Gear Fault Research Articles

Joint suppression of normal gear mesh component and background noise for early local fault detection based on dynamic evolutionary digital filter

Signal denoising is one of the most essential steps in mechanical fault diagnosis, which can help improve the signal-to-noise ratio (SNR) and find faults as earlier as possible. Many denoising techniques have been developed to suppress both Gaussian white and non-Gaussian background noise. However, most of them achieved poor performance in terms of assessing local gear fault in the early stage. The key problem is that only one or several gear tooth’s stiffness or displacement excitation are aroused in the beginning of local faults and most of the gear teeth or gear pairs are healthy, especially for the multi-stage gearbox. And both the normal gear meshing signals and background noise lead to low SNR of fault characteristics. To overcome this problem, this paper proposes a dynamic evolutionary digital filter (Dyna-EDF) method to realize the optimal suppression of background noise and normal gear meshing component in the acquired signal aiming to enhance the SNR of early fault characteristics. The proposed method belongs to an adaptive noise cancellation (ANC) method, which can find and suppress the relevant component between the acquired signal and reference signal based on an iterative optimization of a digital filter. In the proposed method, a filter parameter population and ANC structure is initialized and established firstly. Then the filter parameters of the population are dynamically adjusted to find the optimal filter based on cloning and mating evolutionary strategies. Furthermore, “survival of the fittest” idea is employed for the optimal filter searching and applied during each iteration in Dyna-EDF method to prevent the premature problem that found in traditional EDF. Finally, the derived optimal filter is applied to suppress the relevant component between the acquired signal and reference signal using an ANC process. Results show that the proposed Dyna-EDF method successfully increase the SNR of fault characteristics, both normal gear mesh component and background noise are suppressed effectively. The proposed Dyna-EDF method seems achieve better denoising performance than traditional EDF method, its improved versions and the traditional denoising methods.

Sensor-integrating gears: wear detection by in-situ MEMS acceleration sensors

Gear tooth wear is a common phenomenon leading to malfunctions in machines. To detect wear and faults, gear condition monitoring by vibration is established. The problem is that the measurement data quality for detection of wear by vibration is not good enough with currently established measurement methods, caused by long signal paths of the commonly used housing mounted sensors. In-situ sensors directly at the gear achieve better data quality, but are not yet proved in wear detection. Further it is unknown what analysis methods are suited for in-situ sensor data. Existing gear condition metrics are mainly focused on localized gear tooth faults, and do not estimate wear related values. This contribution aims to improve wear detection by investigating in-situ sensors and advance gear condition metrics. Using a gear test rig to conduct an end of life test, the wear detection ability of an in-situ sensor system and reference sensors on the bearing block are compared through standard gear condition metrics. Furthermore, a machine-learned regression model is developed that maps multiple features related to gear dynamics to the gear mass loss. The standard gear metrics used on the in-situ sensor data are able to detect wear, but not significantly better compared to the other sensors. The regression model is able to estimate the actual wear with a high accuracy. Providing a wear related output improves the wear detection by better interpretability.

Weak Feature Extraction of Local Gear Damage Based on Underdamped Asymmetric Periodic Potential Stochastic Resonance

The enhancement of the detection of weak signals against a strong noise background is a key problem in local gear fault diagnosis. Because the periodic impact signal generated by local gear damage is often modulated by high-frequency components, fault information is submerged in its envelope signal when demodulating the fault signal. However, the traditional bistable stochastic resonance (BSR) system cannot accurately match the asymmetric characteristics of the envelope signal because of its symmetrical potential well, which weakens the detection performance for weak faults. In order to overcome this problem, a novel method based on underdamped asymmetric periodic potential stochastic resonance (UAPPSR) is proposed to enhance the weak feature extraction of the local gear damage. The main advantage of this method is that it can better match the characteristics of the envelope signal by using the asymmetry of its potential well in the UAPPSR system and it can effectively enhance the extraction effect of periodic impact signals. Furthermore, the proposed method enjoys a good anti-noise capability and robustness and can strengthen weak fault characteristics under different noise levels. Thirdly, by reasonably adjusting the system parameters of the UAPPSR, the effective detection of input signals with different frequencies can be realized. Numerical simulations and experimental tests are performed on a gear with a local root crack, and the vibration signals are analyzed to validate the effectiveness of the proposed method. The comparison results show that the proposed method possesses a better resonance output effect and is more suitable for weak fault feature extraction under a strong noise background.

Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems

Higher order spectra exhibit a powerful detection capability of low-energy fault-related signal components, buried in background random noise. This paper investigates the powerful nonlinear non-stationary instantaneous wavelet bicoherence for local gear fault detection. The new methodology of selecting frequency bands that are relevant for wavelet bicoherence fault detection is proposed and investigated. The capabilities of wavelet bicoherence are proven for early-stage fault detection in a gear pinion, in which natural pitting has developed in multiple pinion teeth in the course of endurance gearbox tests. The results of the WB-based fault detection are compared with a stereo optical fault evaluation. The reliability of WB-based fault detection is quantified based on the complete probability of correct identification. This paper is the first attempt to investigate instantaneous wavelet bicoherence technology for the detection of multiple natural early-stage local gear faults, based on comprehensive statistical evaluation of the industrially relevant detection effectiveness estimate—the complete probability of correct fault detection.

Electromechanical modeling of a motor–gearbox system for local gear tooth faults detection

Many of today’s industrial applications are powered by electromechanical drives. Wind turbines, fuel based and hydroelectric generators or electric drivetrains are examples of these applications. Developing accurate models of these systems has attracted a significant interest in recent years, because it can help enormously to enhance their reliability and develop efficient maintenance strategies to avoid prominent failure modes. In this paper, motor–gearbox systems are investigated in their both mechanical and electrical aspects. An electrical model of an induction machine is coupled with a lumped parameter model of a two-stage gear system to build an integrated model of these systems. The coupling between these sub-models is realized in a way that allows to consider transient regimes. Furthermore, an improved potential energy method is used to determine the mesh stiffness of gear pairs considering actual gear shapes and an original method is proposed to refine stiffness curves based on the correlation measure with experimental measurements. The developed model is validated under varying operating conditions using vibration and electrical measurements and simulation results were in good agreement with experiment. Moreover, the model was investigated for the detection of gear tooth faults using both vibration and motor current signature analysis in time and frequency domains, the model response under faulty condition was satisfactory compared with the observed response.

Novel Higher-Order Spectral Cross-Correlation Technologies for Vibration Sensor-Based Diagnosis of Gearboxes.

Novel vibration sensor-based diagnostic technologies, built on the higher order wavelet spectral cross-correlation (WSC), are proposed, investigated and applied to gearbox vibration diagnosis for the first time in worldwide terms. The proposed WSC-based technologies do not feature any constrains in selection of signal spectral components, relations between which are analysed. That is a radical improvement in comparison with the higher-order spectra (HOS). The WSC technologies are applied for an experimental diagnosis of a local gear tooth fault of a helical gearbox that is developed during a long duration gearbox endurance test. Differences between the applied technologies and advantages of the novel WSC approach over the classical HOS are explained in detail. Superiority of the WSC technologies is justified by high validity comprehensive experimental comparison with the HOS technologies: i.e., the wavelet bicoherence and the wavelet tricoherence.

Study on the Fault Diagnosis Method of Scraper Conveyor Gear under Time-Varying Load Condition

Vibration signal is often used in traditional gear fault diagnosis techniques. However, the working face of the scraper conveyor is narrow, harsh and easily explosive, so it is inconvenient to obtain vibration signals by installing sensors. Motor current signature analysis (MCSA) is a fault-diagnosis method without sensor installation, which is easier to realize in the mine. Therefore, a fault diagnosis method for local gear fault, which is based on bispectral analysis (BA) of analytical signal envelope obtained by processing a stator current under time-varying load condition, is proposed in our paper. In this method, the fault frequency component is enhanced by eliminating the interference of fundamental frequency and coal flow impact. Then, the enhanced fault frequency component is extracted by BA, and a quantitative analysis of the fault strength under time-varying load is carried out from the perspective of energy. Finally, the proposed method is verified on the number HB-kpl-75 scraper conveyor reducer, and the results show that this method can successfully diagnose the failure of the scraper conveyor gear under time-varying load conditions.

Automatic condition monitoring of electromechanical system based on MCSA, spectral kurtosis and SOM neural network

Condition monitoring and fault diagnosis play the most important role in industrial applications. The gearbox system is an essential component of mechanical system in fault identification and classification domains. In this paper, we propose a new technique which is based on the Fast-Kurtogram method and Self Organizing Map (SOM) neural network to automatically diagnose two localized gear tooth faults: a pitting and a crack. These faults could have very different diagnostics; however, the existing diagnostic techniques only indicate the presence of local tooth faults without being able to differentiate between a pitting and a crack. With the aim to automatically diagnose these two faults, a dynamic model of an electromechanical system which is a simple stage gearbox with and without defect driven by a three phase induction machine is proposed, which makes it possible to simulate the effect of pitting and crack faults on the induction stator current signal. The simulated motor current signal is then analyzed by using a Fast-Kurtogram method. Self-organizing map (SOM) neural network is subsequently used to develop an automatic diagnostic system. This method is suitable for differentiating between a pitting and a crack fault.

Extraction of weak fault transients using variational mode decomposition for fault diagnosis of gearbox under varying speed

Non-stationary vibration signals of a gearbox under varying speed display complicated modulations, which lead to intense sidebands thereby resulting in difficulty to identify the presence of a fault. Variational mode decomposition (VMD) being highly adaptive, effective in attenuating mode-mixing problem, low computational time requirement and therefore it is suitable to decompose a modulated multi-component non-stationary gearbox vibration signal. This research work thus utilizes the merits of VMD for demodulation and to diagnose localized gear tooth faults under real-time speed variation. In the present study, the vibration signals of a gearbox for different faults were acquired under complete speed variation (i.e., run-up, random fluctuation, and coast-down). To identify the symptoms confirming the presence of faults, the vibration signals were decomposed by VMD thereby demodulating the raw vibration signal. The decomposed VMFs exhibited the presence of transients due to faults and were analyzed statistically. To state the effectiveness of VMD in exhibiting fault by extracting fault transients, the performance of VMD was compared with the performance of recently developed wavelet based empirical wavelet transform (EWT) and flexible analytic wavelet transform (FAWT). The fault detection performance of VMD outperforms EWT as demonstrated by simulated signal and validated by the experimental investigations. FAWT was found ineffective in decomposing the vibration signal under varying speed.

Vibration separation technique based localized tooth fault detection of planetary gear sets: A tutorial

Abstract Due to the epicyclic motion of planet gears revolving around the sun gear in a planetary gear set, the vibration transfer paths between the gear-meshing positions and the outer ring gear mounted sensor are time-varying. This makes conventional localized gear faults detection approaches, such as the synchronous averaging and the narrowband demodulation, not usable for planetary gearboxes directly. The vibration separation technique was proposed more than 20 years ago to address the issue from the time-varying vibration transfer path, which allows further exploring the conventional gear faults detection approaches for the localized tooth fault detection of planetary gear sets. This tutorial is dedicated to study the vibration separation technique and showcase its application to the localized tooth fault detection of planetary gear sets. To guide readers’ understanding of the localized tooth fault detection of planetary gear sets comprehensively, the conventional gear fault detection methods, synchronous averaging and narrowband demodulation, are reviewed briefly at first. Subsequently, the features of planetary gear sets for vibration analysis are introduced. Furthermore, the conventional vibration separation technique is surveyed and an improvement on synthesizing the artificial gear vibration in the vibration separation is proposed. Then, a tooth-root crack detection scheme for planet and sun gears in a planetary gear set is designed by combining the vibration separation with the synchronous averaging and the narrowband demodulation. Experiments on a planetary gearbox test rig with the tooth-root crack of a planet gear and the sun gear have been carried out. The vibration analysis results demonstrate the effectiveness of the vibration separation technique for localized fault detection of planetary gear sets. It is hoped that this tutorial would provide a helpful reference for the readers in this field.

Weighted sparse representation based on failure dynamics simulation for planetary gearbox fault diagnosis

The most common and effective way of rotating machinery diagnostics is to extract fault impact features from the vibration signals and to carry out further processing. As for planetary gear sets, because of the simultaneous mesh of multiple gears and the effect of the carrier rotation, the fault features are submerged in the strong harmonic signals and other noise. Due to the lack of prior information on the faults, the conventional fault diagnostic methods often fail to achieve satisfactory results. To address this problem, we give the prior information of a chipped planetary gear set by dynamics simulation, and utilize the information in the time domain to improve the diagnosis performance. Firstly, a pure torsional lumped parameter model is used to simulate the system vibration response with different degrees of failure. Through statistical analysis, the margin factor, the most sensitive indicator, is selected as prior information to reflect the local gear fault among several indexes. Finally, a weighted sparse representation method based on the prior information provided above is proposed to extract the impact features. Moreover, it is found that the extracted components have a strong–weak cyclic impact feature in the chipped planetary gear sets. The features and effectiveness of the method are verified by an experiment on a planetary gearbox test rig. To validate the superiority of the proposed method, comparisons are made among several state-of-the-art feature extraction methods.

Integrated Vibro-Acoustic Analysis and Empirical Mode Decomposition for Fault Diagnosis of Gears in a Wind Turbine

Over the last few years, wind turbine technology has experienced a rapid growth among the other renewable power developing technologies with respect to market share, size and technological design. Generally, wind turbines are subjected to harsh operating conditions which further yields to damage of the critical components. Hence, health monitoring of key components is a vital task which predicts the damage severity and gives the flexibility to plan the maintenance tasks. Wind turbine condition monitoring is a major area of interest in recent years aiming to improve the life of the machine components simultaneously reducing the operational and maintenance cost. Gearbox in wind turbines has the largest share of downtime among all other components affecting directly the cost of operation and maintenance.In this current investigation, an attempt has been made to diagnose the gear faults by using Empirical Mode Decomposition (EMD) methodology. Two condition monitoring techniques such as vibration analysis and acoustic signal analysis are integrated and the experiments are performed on a laboratory scaled three-stage gearbox having the speed ratio of 48:1. Local gear faults such as tooth chip and tooth root crack are seeded and the response is recorded in the form of vibration and acoustic signals. EMD analysis is implemented and the statistical features are extracted from the acquired data. The representative features are identified using a decision tree algorithm and these are classified using pattern recognition techniques – Support Vector Machine (SVM) to distinguish between the healthy and faulty classes. The challenges and the potential advantages are also discussed in this paper to establish the focus of integrated condition monitoring systems.

FPGA BASED BEVEL GEARBOX FAULT DIAGNOSIS APPROACH

This paper addresses the issue of classifying local gear faults that depends on vibration signal measurements. Most of the early gears fault detection and diagnosis methods result insufficient results when dealing either with time or frequency domain characteristic. In order to overcome these obstacles, a TimeFrequency based approach implemented on Field Programming Gate Array (FPGA) is proposed. The presented approach combines Bagged Trees Classifier (BTC) with Complex Analytic Wavelet Transform (CAWT) analysis. Moreover, the presented approach benefits from the superior realization nature of FPGA. Extensive simulations and experiments have been conducted in order to demonstrate the efficiency of the proposed approach. Experiments are performed on bevel gearbox with both normal and one missing tooth. The obtained results clarify that the presented approach has a superior classification accuracy rate over the other comparative approaches

Time–frequency analysis based on Vold-Kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions

Planetary gearbox fault diagnosis under nonstationary conditions is important for many engineering applications in general and for wind turbines in particular because of their time-varying operating conditions. This paper focuses on the identification of time-varying characteristic frequencies from complex nonstationary vibration signals for fault diagnosis of wind turbines under nonstationary conditions. We propose a time–frequency analysis method based on the Vold-Kalman filter and higher order energy separation (HOES) to extract fault symptoms. The Vold-Kalman filter is improved such that it is encoders/tachometers-free. It can decompose an arbitrarily complex signal into mono-components without resorting to speed inputs, thus satisfying the mono-component requirement by the HOES algorithm. The HOES is then used to accurately estimate the instantaneous frequency because of its high adaptability to local signal changes. The derived time–frequency distribution features fine resolution without cross-term interferences and thus facilitates extracting time-varying frequency components from highly complex and nonstationary signals. The method is illustrated and validated by analyzing simulated and experimental signals of a planetary gearbox in a wind turbine test rig under nonstationary running conditions. The results have shown that the method is effective in detecting both distributed (wear on every tooth) and localized (chipping on one tooth) gear faults.

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

Abstract 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.

CQFB and PBP in Diagnosis of Local Gear Fault

The vibration signal of local gear fault is mainly composed of two components. One is the resonant signal and noise signal and the other one is the transient impulse signal including fault information. The quality factors corresponding to the two components are different. Hence, a method to diagnose local gear fault based on composite quality factor basis and parallel basis pursuit is proposed. First, two different quality factors bases are established using wavelet transform of variable quality factors to obtain the decomposition coefficient. Next, the parallel basis pursuit is adopted for the optimization of the decomposition coefficient. With the derived optimal decomposition coefficient, the resonant components with different quality factors can be reconstructed. By discussing the sparsity of signals treated with different quality factors bases, the suitable composite quality factor basis is selected to perform sparse decomposition on the signal. Besides, the obtained resonant component with low quality factor is subject to demodulation analysis, so as to derive the fault information. The feasibility and validity of the algorithm are shown by the results from simulation signal and practical application of local gear faults.

Fault diagnosis of planetary gearboxes via torsional vibration signal analysis

Torsional vibration signals are theoretically free from the amplitude modulation effect caused by time variant vibration transfer paths due to the rotation of planet carrier and sun gear, and therefore their spectral structure are simpler than transverse vibration signals. Thus, it is potentially easy and effective to diagnose planetary gearbox faults via torsional vibration signal analysis. We give explicit equations to model torsional vibration signals, considering both distributed gear faults (like manufacturing or assembly errors) and local gear faults (like pitting, crack or breakage of one tooth), and derive the characteristics of both the traditional Fourier spectrum and the proposed demodulated spectra of amplitude envelope and instantaneous frequency. These derivations are not only effective to diagnose single gear fault of planetary gearboxes, but can also be generalized to detect and locate multiple gear faults. We validate experimentally the signal models, as well as the Fourier spectral analysis and demodulation analysis methods.

Application of multi-scale chirplet path pursuit and fractional Fourier transform for gear fault detection in speed up and speed-down processes

Demodulation is very important for gear fault detection. However, the demodulation is substantially complicated by the non-stationary nature of the signal during the speed-up and speed-down processes. As such, we propose a new technique to detect gear faults under such conditions based on the multi-scale chirplet path pursuit (MSCPP) algorithm and the fractional Fourier transform (FrFT) method. With the MSCPP algorithm, the instantaneous frequency of the signal component with the largest energy in the multi-components gear vibration signal can be estimated. Then according to the estimated instantaneous frequency, the vibration signal segment whose instantaneous frequency curve approximated as either an ascending or descending linear segment can be obtained from the original gear vibration signal. In other words, the vibration signal segment that can be regarded as a multi-component linear frequency modulated (LFM) signal is extracted. As the FrFT is suitable for multi-component LFM signal analysis, it is then applied to demodulating this vibration signal segment and hence detecting local gear faults based on the revealed modulation phenomenon. The effectiveness of the proposed method has been demonstrated by both simulation and experimental data.

Envelope Analysis of Gear Faults Based on Spectral Kurtosis

An improved envelope analysis approach based on spectral kurtosis (SK) and complex shifted Morlet wavelet in the diagnostics of local gear faults is proposed in this paper. For enhancing impact signals of gear faults, a prewhitting process is performed to the original signals by autoregressive (AR) models firstly in the approach. Then, the envelopes of signals are extracted by Morlet wavelet filter family and the optimal center frequency and bandwidth of the band-pass filters is determined by SK. In the improved approach, not only the optimal parameters of the band-pass filters in envelope analysis can be obtained adaptively by, but also the number of the required band-pass filters can be reduced dramatically. Simulation results verified the feasibility of the present scheme positively.

Differential diagnosis of spall vs. cracks in the gear tooth fillet region: Experimental validation

This paper presents a technique to differentially diagnose two types of localized gear tooth faults: a spall and a crack in the gear tooth fillet region. These faults could have very different prognoses, but existing diagnostic techniques only detect the presence of localized tooth faults without being able to differentiate between a spall and a crack. The effects of spalls and cracks on the behaviour of gear assemblies were studied using static and dynamic simulation models. Changes in the kinematics of a pair of meshing gears due to a gear tooth fillet crack (TFC) and a tooth flank spall were compared using a static analysis model. The difference in the variation of the transmission error (TE) caused by the two faults reveals their characteristics. The effect of a tooth crack depends on the change in stiffness of the tooth while the effect of a spall is dominantly determined by the geometry of the fault. A technique has previously been proposed to detect spalls [M. EL Badaoui, J. Antoni, F. Guillet, J. Daniere, Use of the moving cepstrum integral to detect and localize tooth spalls in gears, Mechanical System and Signal Processing, 15 (5) (2001) 873–885; M. EL Badaoui, V. Cahouet, F. Guillet, J. Daniere P. Velex, Modelling and detection of localized tooth defects in geared systems, Transaction of ASME, 123 (2001) 422–430], using the cepstrum to detect a negative echo in the signal (from entry into and exit from the spall) and successfully performed differential diagnosis on the simulated vibration signals. While the result of the experimental study showed some differences from the result of the simulation study, the differential diagnosis was successfully performed based on the technique presented in this paper. Further investigation revealed non-linear gearmesh behaviour which was causing differences in the experimental and simulation model results.