Fault diagnosis techniques for planetary gearboxes under variable conditions: A review

This thesis proposes analytical models and monitoring methods based on these models to carry out fault diagnosis of planetary gearboxes and assist in developing robust fault alarm framework for early detection of gear failures. The proposed techniques can be applied not only to planetary wind turbine gearboxes but also to other general planetary gearboxes in varied engineering applications. The equally-spaced planetary gearbox is an important power-train component employed in many electro-mechanical engineering systems whose failures can result in significant capital losses and pose safety concerns. For example, a modern wind turbine is usually designed for a life span of over 20 years. Failure of its drive train components, such as the planetary gearbox and the generator, can leave the wind turbine out of service for long periods and therefore contribute to majority of the downtime and incurred maintenance costs over the entire lifetime of the wind turbine. Vibration based diagnostic techniques to detect gear tooth damage in gearboxes haven been widely studied. However, the complex nature of measured vibration spectra, resulting from rotating planet pinions with respect to the stationary sensor mounted on the gearbox housing, makes an effective implementation of these techniques as a challenge. The research presented in this thesis addresses the challenges of vibration based monitoring of planetary gearbox by (1) understanding the dynamic behavior of planetary gears and the amplitude modulation (AM) phenomena due to the relative motions of the planet pinions with respect to the stationary sensor through a lumped parameter modeling based approach, (2) identifying the location of the additional frequency components in measured vibration spectra resulting due to general gear faults, which are shown to depend on the geometry of the planetary gear-set and the gear carrying the local fault and (3) developing a fault alarm algorithm to diagnose incipient gear failure for industrial applications. The results are validated through dynamic simulations and experimental data from two planetary gearbox test rigs: a 4KW laboratory planetary gearbox test rig with the artificial seeded gear failure and a 750 kW tested gearbox for wind turbine application installed in a 2.5 MW dynamometer test facility at the National Renewable Energy Laboratory (NREL) at Golden, Colorado, US. The second facility is capable of providing static, highly accelerated life and model-in-the-loop tests. Both experiments collected vibration data through stationary accelerometers attached to the housing of the gearbox. The original contribution of the thesis include: (1) A mathematical AM-FM (amplitude modulated/frequency modulated) signal model developed to simulate the distinct spectrum of planetary gearboxes operating under healthy and faulty conditions; (2) Fourier series analysis based on this mathematical signal model that identifies the additional frequency components appearing due to local faults and describe the relationship between the location of these frequency components and the geometry of the planetary gear-set and (3) a new time domain approach that combines dynamic time warping and correlated kurtosis to detect gear faults and identify their location, thus assisting in the development of a robust fault alarm system.

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

Planetary gearboxes are widely used in the modern mechanical transmission equipment. The fault diagnosis of the planetary gearboxes is an important work. When the planetary gearbox fault occurs, its vibration signal has the characteristics of complex frequency components and amplitude modulation-frequency modulation. In view of these problems, a signal demodulation analysis method based on complementary ensemble empirical mode decomposition (CEEMD) and Teager energy operator is proposed to extract instantaneous amplitude and instantaneous frequency information of the fault signals. First, the CEEMD method is used to decompose the vibration signals of the complex multi component planetary gearbox into a series of single component intrinsic mode functions (IMFs). Then, the Teager energy operator is used to estimate the amplitude envelope and the instantaneous frequency of each single component signal. The intrinsic mode function is selected as the sensitive component according to the fluctuation characteristic of the instantaneous frequency. Finally, the Fourier transformation is applied in the obtained amplitude envelope and instantaneous frequency of the sensitive component to identify the gear fault characteristic frequencies, and achieve the accurate diagnosis of the planetary gearbox fault. The effectiveness and superiority of the method is verified through the analysis of the fault simulation experiment data of the planetary gearbox.

Planetary gearboxes are widely used in many sorts of machinery, and data-driven intelligent fault diagnosis is one of the important development directions for planetary gearbox maintenance. However, the existing data-driven methods suffer from limitations such as weak fault information, insufficient training data, and uneven feature distribution in common industrial diagnosis scenarios. Although transfer learning-based approaches are anticipated to resolve the above issues, it remains challenging for the fault diagnosis of gearboxes with small datasets. Thus, we propose a multi-feature representation distribution adaptation (MFRDA) method for the cross-condition fault diagnosis of planetary gearboxes under time-varying conditions. We dimensionally reorganize the time-frequency feature matrices of multiple sensor signals under the same condition during signal preprocessing, thus extracting the feature information of multiple signals simultaneously for data fusion. The issue of weak fault feature loss within single signals is addressed by data enhancement of the fault-related features. We adopt a multi-feature representation hybrid structure to extract multiple fault-related feature representations for feature learning, thus alleviating the issue of network overfitting caused by insufficient training data. Moreover, a distribution assessment algorithm is presented in the hybrid structure to assess the feature distribution between source and target tasks, which minimizes the feature differences among different tasks by the distribution adaptation corresponding to different feature representations. This method is validated experimentally. Analysis results indicate that the diagnostic accuracy and training robustness of this method outperform the residual network and other adaptation methods.

Planetary gear transmission structures, consisting of sun gear, planet gears, ring gear, carrier etc., are more complex than fixed-axis gear transmission structures. In planetary gearboxes, the vibration responses of multiple gear pair meshing couple with each other, and the vibration transmission paths from gear meshing points to the fixed transducers are time-varying. Therefore, fault diagnosis of the planetary gearboxes is more difficult compared to that of fixed-axis gearboxes, in which the vibration simulation models are very important. By analyzing the transmission paths of each meshing vibration and synthetizing the phase differences between different meshing vibrations, vibration simulation models of the planetary gearboxes are constructed and the vibration signals based on the models are obtained. By comparing the signal characteristics of the faulty condition with the healthy one, faults of the planetary gearboxes can be diagnosed. The constructed models are characterized by considering multiple gear pair meshing and their phase relations, time-varying vibration transmission paths, modulations due to gear faults, etc. To test the proposed models, the vibration signals are measured from a planetary gearbox test rig under the healthy condition, the sun gear with a cracked tooth and a planet gear with a cracked tooth, respectively. The fault characteristics of the planetary gearboxes are summarized based on the comparisons of the vibration signals between the models and the experiments.

Investigation on planetary gearbox fault mechanism under variable speed conditions based on rigid-flexible coupling dynamics model


 
 
 Planetary gearboxes are widely used in the drivetrain of helicopters and wind turbines. Any planetary gearbox failure could lead to breakdown of the whole drivetrain and major loss of helicopters and wind turbines. Therefore, planetary gearbox fault diagnosis is an important topic in prognostics and health management (PHM). Planetary gearbox fault diagnosis has been done mostly through vibration analysis over the past years. Vibration signals theoretically have 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 is complex. It is difficult to diagnose planetary gearbox faults via vibration analysis. Strain sensor signals on the other hand have less amplitude modulation effect. Thus, it is potentially easy and effective to diagnose planetary gearbox faults via stain sensor signal analysis. In this paper, a research investigation on planetary gearbox fault diagnosis via strain sensor signal analysis is reported. The investigation involves using time synchronous average technique to process signals acquired from a single piezoelectric strain sensor mounted on the housing of a planetary gearbox and extracting condition indicators for fault diagnosis. The reported investigation includes analysis results on a set of seeded fault tests performed on a planetary gearbox test rig in a laboratory. The results have showed a satisfactory planetary gearbox fault diagnostic performance using strain sensor signal analysis.
 
 

As an essential component in the power transmission system of rotating machinery, planetary gearboxes are often subjected to extreme operating conditions, including heavy loads and low speeds, which can cause key components to fail over time. Early planetary gearbox fault features are weak, coupled with the impact of strong noise in the industrial field, which brings great challenges to the accurate identification and diagnosis of planetary gearbox faults. To address these issues, we propose a novel spiking graph attention network (Spiking-GAT) for intelligent fault diagnosis of planetary gearboxes, which realizes synchronous extraction of temporal and spatial features of the original signal, to accurately identify the fault category and severity of planetary gearbox. Firstly, a graph data construction method based on chaos theory is proposed. A multi-phase coupled chaotic oscillator array based on Duffing (MP-COAD) is established to reconstruct the original signal, and the K-nearest neighbor classifier (KNN) is used to construct the reconstructed signal as graph data. Secondly, a novel Spiking GAT intelligent fault diagnosis framework has been developed, which provides an adaptive spiking coding approach for graph data and deep mining and extraction of spatial-temporal features to accurately identify the health status of planetary gearboxes. Finally, the experimental and industrial fault simulation test rigs of the planetary gearbox are designed, and three cases are studied to verify the accuracy and stability of Spiking-GAT. The results show that the classification accuracy of Spiking-GAT reaches 100% on the datasets of the two test rigs, and has excellent noise robustness.

Planetary gearboxes are very prone to failure when they are used in low-speed and heavy-load conditions for a long time. Due to strong background noise in the industrial field and the signal decay in the process of fault signal transmission, the weak fault characteristics of planetary gearboxes are submerged by noise and difficult to extract and identify. Aiming at this problem, an improved chaos detection method for weak signal frequency recognition is proposed. In this paper, the maximum weighted kurtosis is selected as the fitness function, and the Aquila optimizer algorithm is used to find the optimal modal number K and penalty factor α of variational modal decomposition. According to the grey relational degree, an inherent modal function (IMF) containing rich fault features is selected to reduce the noise of the original signal. Further, a double-coupled Duffing oscillator equation is constructed, and a quantitative criterion for chaotic oscillator phase transition based on the standard deviation weighted-average Euclidean distance is proposed. Based on the reverse detection method, the sideband growth of the most sensitive IMF after standardization is detected to determine the health state of the planetary gearbox. Detecting the fault characteristic frequency in the most sensitive IMF after normalization and Hilbert transform processing is based on the forward detection method in order to determine the fault type of the planetary gearbox. The effectiveness of the proposed method is verified by simulation and experiment. The results show that the proposed method successfully identifies the weak fault characteristics of the planetary gearbox, which fully shows that this method has an excellent diagnostic effect for planetary gearbox faults with rich frequency fault characteristics, and provides a new method for the diagnosis and identification of weak faults in planetary gearboxes in engineering practice.

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 vibration signals have strong modulation features due to the amplitude modulation and frequency modulation (AM-FM) effect of gear faults, as well as the amplitude modulation (AM) effect of time-varying vibration transfer paths, on gear meshing vibrations. This results in an involute sidebands structure in Fourier spectrum, possibly misleading fault diagnosis. The modulating frequency of both amplitude modulation (AM) and frequency modulation (FM) parts is closely related to the gear fault characteristic frequency. This inspires the idea of joint amplitude and frequency demodulation analysis, thus addressing the complex sidebands issue inherent in Fourier spectrum. Demodulation analysis requires mono-component signals for accurate estimation of instantaneous frequency, and proper selection of an AM-FM component sensitive to gear fault. To this end, we firstly decompose the complex signal into intrinsic mode functions (IMFs) via variational mode decomposition (VMD), by exploiting its capability in decomposing complex modulated signal into constituent AM-FM components. For effective application of VMD in complex planetary gearbox signal analysis, we propose a method to determine a key parameter in VMD, i.e. the number of IMFs to be separated. For accurate instantaneous frequency estimation, we decompose IMFs via empirical AM-FM decomposition, to remove the influence of AM on instantaneous frequency estimation. Then, we select the sensitive IMF that contains the main gear fault information for further demodulation analysis. In order to properly select the sensitive IMF, we propose a criterion based on the gear vibration characteristics and the VMD properties. Finally, we obtain the amplitude and frequency demodulated spectra by applying Fourier transform to the amplitude envelope and instantaneous frequency of the selected sensitive IMF. According to the characteristics exhibited in the demodulated spectra, we can detect planetary gearbox fault. The proposed method is illustrated via a numerical simulated planetary gearbox vibration signal, and is further validated using lab experimental vibration signals of a planetary gearbox. Faults on all the three types of gear (sun, planet and ring) are successfully identified.

Different types of incipient damage to gears need to be distinguished for the reliable and safe operation of mechanical equipment. However, the identification of various types of incipient gear damage is still a challenge for the fault diagnosis of planetary gearboxes. To identify the various types of incipient damage in a sun gear, fault feature analysis is conducted for various damage conditions in a planetary gearbox. First, the internal excitation aroused by gear damage is computed and the effects of the typical damage modes of a sun gear on the internal excitation are demonstrated. Then, the internal excitation of the damaged sun gear is applied into the dynamic model of the planetary gear train to simulate distributed damage or local damage. After that, with the vibration responses obtained from the dynamic model, a vibration signal model is proposed to simulate the vibration signals of the planetary gearbox. Furthermore, the vibration signals are analysed over the time domain and frequency domain. The fault features of the planetary gearbox are characterized by using residual signals and variational mode decomposition signals, which are useful for identifying the various incipient damage types of a sun gear. Finally, the simulated results are validated by experimental tests.

Planetary gearboxes are widely used in aerospace, marine and other important equipment for their unique advantages, and their health directly affects whether the equipment can work normally. The vibration signal generated when the fault occurs is extremely complicated, making it difficult to perform an effective diagnosis. In order to solve this problem, a planetary gearbox fault diagnosis method based on autocorrelation noise reduction combined with an improved convolutional neural network is proposed. The method firstly performs autocorrelation noise reduction on the fault signal. Secondly, the noise-reduced signal is used as the input of CNN for automatic feature extraction. The classifier is used to finally complete the intelligent diagnosis of the planetary gearbox.

As a type of clean and renewable energy source, wind power is growing fast as more and more countries lay emphasis on it. At the end of 2011, the global wind energy capacity reached 238 GW, with a cumulative growth of more than 20% per year, which is certainly a respectable figure for any industry. There is an exigent need to reduce the costs of operating and maintaining wind turbines while they became one of the fastest growing sources of power production in the world today. Gearbox is a critical component in the transmission system of wind turbine generator. Wind turbine gearbox operates in the extreme conditions of heavy duty, low speed and non-stationary load and speed, etc., which makes it one of the components that have high failure rate. To detect the fault of gearbox, many methods have been developed, including vibration analysis, acoustic emission, oil analysis, temperature monitoring, and performance monitoring and so on. Vibration analysis is widely used in fault diagnosis process and many efforts have been made in this area. However, there are many challenging problems in detecting the failure of wind turbine gearbox. The gearbox transforms low-speed revolutions from the rotor to high-speed revolutions, for example, from 20 rpm to 1500 rpm or higher. Usually one or more planetary gear stages are adopted in a gearbox design because the load can be shared by several planet gears and the transmission ratio can get higher. One disadvantage with the planetary gear stage is that a more complex design makes the detection and specification of gearbox failure difficult. The existing fault diagnosis theory and technology for fixed-shaft gearbox cannot solve the issues in the fault diagnosis of planetary gearbox. The planetary stage of wind turbine gearbox consists of sun gear, ring gear and several planet gears. The planet gears not only rotate around their own centers but also revolve around the sun gear center, and the distance between each planet gear to the sensor varies all the time. This adds complexity to vibration signals and results in difficulty in finding the fault-related features. The paths through which the vibration propagates from its origin to the sensors are complex, and the gears of other stage vibrate at the same time. This makes fault features be buried in noises. Further, the extreme conditions of heavy duty, low speed, and non-stationary workload lead to evidently non-stationary phenomena in the collected vibration. Methods to assess fault severity of a gearbox should be developed so as to realize fault prognosis and estimate of the remaining useful life of gearbox. Finally, other issues like signal analysis based on multi-sensor data fusion are also considered. This paper gives a comprehensive investigation on the state-of-the-art development in the wind turbine gearbox condition monitoring and health evaluation. The general situation of wind energy industry is discussed, and the research progresses in each aspects of wind turbine gearbox are reviewed. The existing problems in the current research are summarized in the end.

Deep balanced domain adaptation neural networks for fault diagnosis of planetary gearboxes with limited labeled data