Planetary Gearbox Research Articles

Scaling operator demodulation spectrum-based planetary gearbox fault diagnosis method under variable speed conditions

It is a challenging task to detect planetary gearbox fault type under time-varying speeds. Firstly, under time-varying speeds, the fault-induced frequency harmonics of the planetary gearbox are time-varying and close-spaced, and the fault feature is difficult to detect from the spectrum or traditional time-frequency analysis (TFA) results. Secondly, the interference component in the vibration signal resulting from the complicated gearbox configuration is another obstacle for fault detection. As such, the scaling operator demodulation spectrum (SODS) technique with high readability is developed for planetary gearbox fault diagnosis under variable speed conditions. The developed technique consists of two terms: scaling demodulation operator term for transforming all time-varying fault-related frequency harmonics; and spectral selection criterion term for obtaining fault-related frequency harmonics and removing the other smeared frequency components. With the proposed technique, the time-varying and close-spaced fault-induced frequency harmonics can be quantitatively characterized by the spectral peaks. The performance of the SODS-based planetary gearbox fault diagnosis technique is validated by a simulated signal and two experimental signals collected from different test rigs. Furthermore, comparison results with the traditional TFA methods show that the proposed technique has much better ability to identify the fault-related frequency harmonics of the planetary gearbox.

Fault diagnosis of planetary gears based on intrinsic feature extraction and deep transfer learning

The planetary gearbox is a key transmission apparatus used to change speed and torque. The planetary gear is one of the most failure-prone components in a planetary gearbox. Due to the complexity of working environments, collected vibration signals contain a lot of noise and interference; fault characteristic frequencies are usually submerged or even lost. Thus, feature extraction from the vibration signal is beneficial to subsequent fault diagnosis. As a fault identification approach that has been increasingly popular in the field of fault diagnosis, deep learning requires a large number of samples to train the model. Insufficient samples lead to low diagnostic accuracy for deep learning models. This paper proposes a novel fault diagnosis approach for planetary gears based on intrinsic feature extraction and deep transfer learning. The original vibration signals are decomposed into a series of band-limited intrinsic mode functions (BLIMFs) by variational mode decomposition. BLIMF with the most apparent fault characteristics is selected to generate two-dimensional time-frequency maps by continuous wavelet transform. The preprocessed time-frequency maps are adopted as the input of the pretrained VGG16 model. The bottom layers are frozen, and the top layers are fine-tuned to achieve fault diagnosis for planetary gears. Applications to planetary gear datasets verify the superiority of the proposed method.

Impulse feature extraction via combining a novel voting index and a variational model penalized by center frequency constraint

A key step in rotating machinery fault diagnosis is extracting the subcomponent that best characterizes the fault features. To this end, a fault feature extraction methodology based on an adaptive spectrum segmentation method, a new voting index, and a new variational model is constructed in this paper. More specifically, the spectrum is divided into a series of sub-bands through a fast iteration filter by changing the width of the window. Then, by considering the amplitudes and locations of the fault feature spectral lines in squared envelope spectrum, a novel index based on voting mechanism is constructed to evaluate the periodic impulses in each sub-band. Finally, a new variational model penalized by a center frequency penalty item is proposed to extract the fault characteristics from the raw vibration signal by maximining the proposed index iteratively. The proposed methodology is applied to extract the fault characteristics from rolling bearing and planetary gearbox signals. The comparative results indicate the proposed method is superior to fast kurtogram, fast entrogram, and the proposed fault feature extraction methods based on other classical indices.

Wind turbine planetary gearbox fault diagnosis using circular pitch cyclic vector and a bidirectional gated recurrent unit

A planetary gearbox with large transmission ratio is a critical component of a wind turbine (WT). When the planetary gearbox suffers from failure, the regular variation of circular pitch vibration energy contains fault pattern information. Although deep learning-based fault diagnosis models have the capability for automatic feature extraction, they rarely consider emphasizing the energy variation characteristics related to the planetary transmission structure in the data representation stage, which degrades the fault diagnosis performance. To solve this problem, this paper describes a novel data representation method, circular pitch cyclic vector (CPCV) and, on this basis, proposes a WT planetary gearbox fault diagnosis method using CPCV and a bidirectional gated recurrent unit (BGRU). Firstly, this paper utilizes root mean square value as the energy evaluation metric for calculating the CPCV. Then, the mapping relationship between circular pitch energy and the ring gear mesh phase is captured using the BGRU through its bidirectional learning capability. Finally, a fully connected layer with softmax is used for fault classification. The proposed method is evaluated by analyzing the in-service WT vibration data collected from a wind farm in eastern China. The experimental results and comparison analysis illustrate the superiority of the proposed method.

Research on fault diagnosis method of planetary gearbox based on dynamic simulation and deep transfer learning

To address the issue of not having enough labeled fault data for planetary gearboxes in actual production, this research develops a simulation data-driven deep transfer learning fault diagnosis method that applies fault diagnosis knowledge from a dynamic simulation model to an actual planetary gearbox. Massive amounts of different fault simulation data are collected by creating a dynamic simulation model of a planetary gearbox. A fresh deep transfer learning network model is built by fusing one-dimensional convolutional neural networks, attention mechanisms, and domain adaptation methods. The network model is used to learn domain invariant features from simulated data, thereby enabling fault diagnosis on real data. The fault diagnosis experiment is verified by using the Drivetrain Diagnostics Simulator test bench. The validity of the proposed means is evaluated by comparing the diagnostic accuracy of various means on various diagnostic tasks.

Intermediate Distribution Alignment and Its Application Into Mechanical Fault Transfer Diagnosis

Domain adaptation has been widely used for knowledge transfer. However, the aligning targets of the existing domain adaptation mechanisms dynamically vary during the training, which leads to the loss oscillation, slow convergence, and poor robustness. To overcome this main problem, a novel domain adaptation mechanism named intermediate distribution alignment (IDA) is proposed. For implementing the end-to-end diagnostic tasks, a feature extractor based on deep convolutional neural network with wide first-layer kernel is first built to fit the posterior distributions of source and target domains. Then through the KL divergence, IDA maps the learned features from the source and target domains into a specific intermediate distribution. It is proved theoretically that IDA can align the prior distributions of two domains. The proposed IDA mechanism is successfully applied to the fault transfer diagnosis of planetary gearboxes without labeled target-domain samples. The comparative results show that the proposed IDA mechanism has higher diagnostic performance than the typical domain adaptation mechanisms.

Parallel adversarial feature learning and enhancement of feature discriminability for fault diagnosis of a planetary gearbox under time-varying speed conditions

The intelligent fault diagnosis of a planetary gearbox under variable speed is still a challenging topic. Due to the similar spectrum structure, overlapping features occur and result in decreasing diagnosis accuracy. Autoencoder-based methods can extract features adaptively but few studies have proposed approaches to enhance the discriminability of features from different classes under variable speeds. Besides, the adverse variability of encoder weights may result in an adverse effect on the decoder. Adversarially learned inference (ALI) trains the encoder and decoder independently, but it is time-consuming to reach Nash equilibrium. To address the issues, a parallel adversarial learning inference (PALI) model is proposed, which aims at validating the parallel training of encoder and decoder and enhancing the discriminability of features. Specifically, time-frequency analysis is utilized to reveal the time-varying characteristics of raw signals and obtain time-frequency images as input for the encoder. Then, an explicit multi-dimensional uniform distribution is used for the merit of a simple probability density function to construct visualized and well-classified samples as input for the decoder. After that, a parallel adversarial game is explored to train the encoder and decoder simultaneously and independently, which will reduce computing interference and make the extracted features similar to the well-classified samples and reconstruct the raw signals. Finally, a Softmax classifier is trained and tested by the features. This method and its generability are validated via a planetary gearbox data set and a public bearing data under variable speed. The results indicate that the proposed parallel adversarial game is valid for training encoder and decoder independently, and that PALI works as well as the adversarial autoencoder (AAE) and outperforms ALI, the variational autoencoder (VAE) in obtaining well-clustered features over different training data. Compared to Wigner–Ville distribution (WVD) and continuous wavelet transform (CWT), PALI based on short-time Fourier transformation (STFT) works better over different training data.

Enhanced order spectrum analysis based on iterative adaptive crucial mode decomposition for planetary gearbox fault diagnosis under large speed variations

Variable speed conditions are omnipresent in the operation of rotating machinery. Tacholess order tracking (TOT) technique based on time-frequency representation (TFR) has been attracting much attention in recent years, but the challenges of low time-frequency resolution, parameter selection and computational consumption limit its practical applications. To this end, this paper presents a novel time-frequency analysis (TFA) method named iterative adaptive crucial mode decomposition (IACMD), which accurately extracts the time-varying component in the signal through the instantaneous frequency refinement strategy and the adaptive bandwidth parameter update rule. Then, an enhanced order spectrum analysis scheme based on IACMD is further proposed, which highlights the fault characteristic order by eliminating the amplitude modulation effect and is completely independent of the tachometers. Both simulation analysis and test-rig experiments have demonstrated the effectiveness of the proposed strategy in fault diagnosis of planetary gearboxes under various large speed variations. Compared with traditional TFA methods, the results show that the IACMD provides higher accuracy and efficiency even in the case of large time-varying and strong noise.

Improved vibration AM-FM sideband phenomenon models of planetary gear set with distributed faults and fault-induced speed fluctuation

Vibration modulation features are the important indicators for the planetary gear set fault diagnosis. However, the modulation sidebands caused by the distributed faults of planetary gear set are complicated, especially the FM components related to fault-induced periodic speed fluctuation, which have not been clearly revealed. In this paper, the transfer path function, gear meshing force and measuring-direction projection function with FM components are combined to develop a series of phenomenon models when considering the distributed gear fault-induced speed fluctuation, which can theoretically deduce the vibration modulation sidebands. Field test of the wind turbine planetary gearbox and the fault simulation of experiment platform verify the high consistency of the vibration AM-FM sidebands obtained from the phenomenon models and the practical planetary gear sets. The proposed models contribute a clear explanation for the generation mechanism of vibration AM-FM sidebands even the sidebands with small amplitudes caused by the distributed faults of sun and planet gear, and provide the important and effective frequency features to confirm the condition of planetary gear set.

Unknown fault diagnosis of planetary gearbox based on optimal rank nonnegative matrix factorization and improved stochastic resonance of bistable system

Unknown fault diagnosis of planetary gearbox based on optimal rank nonnegative matrix factorization and improved stochastic resonance of bistable system

Vibration based fault diagnostics in a wind turbine planetary gearbox using machine learning

To reduce wind turbine operations and maintenance costs, we present a machine learning framework for early damage detection in gearboxes based on the cyclostationary and kurtogram analysis of sensor data. The application focus is fault diagnostics in gearboxes under varying load conditions, particularly turbulent wind. Faults in the gearbox rotating components can leave their signatures in vibrations signals measured by accelerometers. We analyze data stemming from a simulated vibration response of a 5 MW multibody wind turbine model in a healthy and damaged scenarios and under different wind conditions. With cyclostationary and kurtogram analysis applied on acquired sensor data, we generate two types of 2D maps that highlight signatures related to the fault damage. Using these maps, convolutional neural networks are trained to identify faults, including those of small magnitude, in test data with a high accuracy. Benchmark test cases inspired by an NREL study are tested and faults successfully detected.

Modal sensitivity of three-dimensional planetary geared rotor systems to planet gear parameters

A parameter study is presented to determine effects of planet gear design parameters on the global modal behaviour of planetary geared rotor systems. The modal sensitivity analysis is conducted using a three-dimensional dynamic model of a planetary geared rotor system for the number of planet gears, planet mistuning, mass of planet gears, gear mesh stiffness and planet gear speed. These parameters have varying impacts on both natural frequencies and mode shapes, therefore the sensitivity of the planetary geared rotor vibration modes to the planet gear parameters is determined by computing the frequency shifts and comparing the mode shapes. The results show that the mass and mesh stiffness of planet gears have a larger influence on the global dynamic response. Torsional modes and coupled torsional-axial modes are more sensitive to gear mesh stiffness whereas lateral vibration modes are more sensitive to gearbox mass. Planet mistuning results in coupling between lateral and torsional vibrations. The planetary gearbox becomes more rigid in the torsional-axial modes and more flexible in the lateral modes with an increase in the number of planet gears. Planet gears are also found to be having significant gyroscopic effects inside the planetary gearbox. The main original findings in this study can be directly used as initial guidelines for planetary geared rotor design.

Ramanujan Fourier Mode Decomposition and Its Application in Gear Fault Diagnosis

As an important part of rotating machinery, gear is easy to appear some unexpected fault states, and its fault diagnosis is very important. Fourier decomposition method (FDM) is a common method for gear fault diagnosis, but the noise robustness, period recognition, and extraction capabilities of FDM are unsatisfactory. Based on this, in this article, Ramanujan Fourier mode decomposition (RFMD) method is proposed. The RFMD not only has a complete mathematical theory foundation but also has an excellent ability to identify and extract periodic components. Emulational and experimental results of planetary gearbox show that the RFMD method has good noise robustness and can accurately extract gear fault characteristic information. Thus, it is an effective gear fault diagnosis method.

INVESTIGATION OF OPERATION OF THE HYDROVOLUMIC MANUAL CONTROL SYSTEM ON THE BASIS OF A DOSING PUMP WITH AXIAL SPOILER WITH ACCIDENTAL LOAD

One of the factors ensuring the stable development of agriculture is high-quality and reliable equipment. Today, the development of hydraulic drives has largely led to its widespread use in agricultural machinery and tractors. One of the systems fully hydrogenated in heavy agricultural machinery is the steering system. Hydrovolume steering system has the following advantages - compactness, freedom in the layout of the machine, high efficiency, accuracy, the ability to transfer significant effort to the steered wheels with minimal effort of the operator. Disadvantages include certain restrictions when the associated load on the steered wheels when turning. This parameter is one of the indicators of the quality of hydrovolume steering systems and the minimum effort at which the process of assembling the wheels begins, is regulated by international standards. The article performs mathematical modeling of the hydrovolume steering system. The mathematical model consists of the equations of continuity of flows of the working fluid, the equations of moments and the Lagrange equation of the 2nd kind, which describes the operation of the planetary gearbox. The search for solutions of this model was carried out using the original software, built on the basis of the Runge-Kutta-Feldberg method with an automatic integration step. As a result of the analysis of the received transient processes areas of steady work were constructed that allowed to receive a ratio of parameters of system at which its steady work is provided. The proposed design solution allowed to increase the limit value of the associated load in excess of which there is a spontaneous movement of the steered wheels. The analysis of the workload at the associated load had a significant impact on the values of the overlap of the working edges on any work of the system under the action of the associated load, and the correct selection of these floors can achieve significant quality improvement.

Intelligent fault warning method of rotating machinery with intraclass and interclass infographic embedding

Rotating machinery is widely used in industrial production facilities, and once a failure occurs, it can be catastrophic. Alerting to potential defects in time to prevent further equipment degradation is a challenging task. In this paper, a novel two-stage fault warning framework is proposed for early fault warning of rotating machinery. Specifically, a new method based on intra-class and inter-class neighborhood information graph embedding orthogonal discriminant projection is firstly adopted in this framework to extract the global distribution feature information and local geometric structure information of the data so that the homogeneous distance is compressed and the heterogeneous distance is distanced. Secondly, the minimum quantization error between the sample to be measured and the optimal winning neuron weight vector is calculated by self-organizing map to characterize the health state change, and combined with the Beta distribution self-learning technique to establish the fault warning threshold to circumvent the defects brought by the traditional fixation and it. Finally, the effectiveness of the proposed method is verified in the bearing and planetary gearbox test cases, and exciting conclusions are obtained under different working conditions in the gearbox case.

Vibration-modelling-based fault feature analysis for incipient damage identification of sun gear

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.

Data Augmentation in 2D Feature Space for Intelligent Weak Fault Diagnosis of Planetary Gearbox Bearing

Quickly detecting and accurately diagnosing early bearing faults is the key to ensuring the stable operation of high-precision equipment. In actual industrial applications, it is common to face the issues of big data and poor fault identification accuracy. To accurately and automatically realize the diagnostics of rolling bearings, a convolutional neural network algorithm and fault feature enhancement method is proposed. A two-dimensional space feature extraction method based on the Cyclostationary theory and wavelet transform shows good results in noise suppression. Firstly, the cyclic demodulation of wavelet transform coefficients is performed on bearing vibration signals to convert one-dimensional vibration data into a two-dimensional spectrogram for enhancing the weak fault feature. Secondly, the image segmentation theory is introduced, which can obtain more data and improve the calculation accuracy and efficiency on the basis of data dimension reduction. Finally, the augmented 2D spectrograms are inputted into a convolutional neural network. Through the analysis of the actual planetary gearbox bearing data, and compared with other mainstream intelligence algorithms, the effectiveness and superiority of this method are verified.

SRMANet: Toward an Interpretable Neural Network with Multi-Attention Mechanism for Gearbox Fault Diagnosis

Deep neural network (DNN), with the capacity for feature inference and nonlinear mapping, has demonstrated its effectiveness in end-to-end fault diagnosis. However, the intermediate learning process of the DNN architecture is invisible, making it an uninterpretable black-box model. In this paper, a stacked residual multi-attention network (SRMANet) is proposed as a means of feature extraction of vibration signals, and visualizing the model training process, designing Squeeze-excitation residual (SE-Res) blocks to obtain additive features with minimal redundancy and sparsity. This study recommends the use of the attention fusion unit to ensure the interpretability of the model and ultimately to obtain representative features. By feeding the output gradient of the attention layer back to the original signal, the key feature components in the time domain signal can be effectively captured. Finally, the interpretability, identification accuracy and adaptability of the model under different operating conditions are verified on 12 different fault tasks in the planetary gearbox.

Enhanced hierarchical symbolic sample entropy: Efficient tool for fault diagnosis of rotating machinery

Intelligent fault diagnosis of rotating machinery is a key topic for industrial equipment maintenance and fault prevention. In this study, an intelligent diagnosis approach of rotating machinery via enhanced hierarchical symbolic sample entropy (EHSSE) is proposed. Firstly, a novel indicator termed symbolic sample entropy (SSE) is proposed for complexity measure and representation of fault information. By using symbolic dynamic filtering, the raw continuous time-series will be discretized into symbolic data, and analysis of symbolic data is less sensitive to measurement noise, resulting in superior robustness. Secondly, SSE is combined with enhanced hierarchical analysis to further extract fault characteristics hidden in both low- and high-frequency components. To study the performance of SSE and EHSSE, multiple simulated signals and experimental studies are constructed and three widely used entropy methods are employed to present a comprehensive comparison. The comparison results show that EHSSE performs best in diagnosing various faults of planetary gearbox and rotor system with highest identification accuracy compared with other entropy-based approaches.

Hybrid 3K Compound Planetary Reduction Gearbox With a Roller Transmission Mechanism

Reduction gears are used in the joints of the robots. While precision and efficiency are required for industrial robot gearboxes, humanoid robots are expected to become more popular in the future. Flexibility must be considered in humanoid robots, and high back drivability shall be required. In this study, we aimed at realizing high efficiency, and high-precision reduction gears with high reduction ratios by maximizing the efficiency equation, hybridizing a 3K compound planetary reduction gearbox and traction drive, experimentally determining the efficiency, backlash, and angular velocity variation, and demonstrating the effectiveness of the proposed mechanism from a control perspective. In the experiments, we achieved a maximum forward driving efficiency of 90.90%, backward driving efficiency of 90.58%, and a minimum backlash of 1.18 arcmin.