Planetary Gearbox Research Articles

Fault Diagnosis Method of Planetary Gearbox Based on Compressed Sensing and Transfer Learning

This paper suggests a novel method for diagnosing planetary gearbox faults. It addresses the issue of network bandwidth limitation during wireless data transmission and the problem of relying on expert experience and insufficient training samples in traditional fault diagnosis. The continuous wavelet transform was combined with the AlexNet convolutional neural network using transfer learning and the compressed theory of sense. The original vibration signal was compressed and reconstructed using the compressed sampling orthogonal matching pursuit reconstruction algorithm. A continuous wavelet transform was used to convert the compressed signal into a time–frequency image. The pretrained AlexNet model was selected as the migration object, the network model was fine-tuned and retrained, and the trained AlexNet model was used to diagnose the fault using the model-based migration method. It was demonstrated by the experimental results when the compression ratio CR = 0.5. Compared to other network models, the classification accuracy rate is 97.78%. This method has specific reference value and application prospects and good feature extraction and fault classification capabilities.

Evidence Theory based Uncertainty Design Optimization for Planetary Gearbox in Wind Turbine

The planetary gearbox is an important part of the wind turbine. There are many random uncertain factors in the process of design, production, installation, and use, and these uncertain factors greatly influence the service life and reliability of the planetary gearbox. Therefore, the influence of uncertain factors needs to be considered in the design process to reduce the risk of failure. In this paper, an uncertainty design optimization method based on evidence theory is proposed, which can consider both interval variables and random variables in the optimization process. Then the megawatt wind turbine planetary gearbox is taken as the research object to analyze its uncertainty sources. Finally, the planetary gearbox is optimized by the proposed method. By comparing the results, the design scheme obtained by the method proposed in this paper is more reliable.

A novel adaptive bandwidth selection method for Vold–Kalman filtering and its application in wind turbine planetary gearbox diagnostics

The planetary gearbox transmission system in wind turbines has complex structures and generally operates under non-stationary conditions. Thus its measured responses are of high complexity and nonlinearity, which brings a great challenge in the development of reliable condition monitoring techniques for the planetary gearbox transmission system. As a prevalent and effective tool for analyzing the non-stationary vibration signal with strong nonlinearity, the Vold–Kalman filtration technique has excellent capabilities of tracking the targeted harmonic components of vibrations, which can significantly benefit planetary gearbox fault diagnostics. However, the tracking accuracy is heavily enslaved to the selection of the rational bandwidth for the Vold–Kalman filter. An inappropriate bandwidth could impair the characteristics of the targeted harmonic responses, and as a consequence, the monitoring process becomes no longer reliable. To address this issue, a novel bandwidth selection methodology for the Vold–Kalman filter is developed in this paper. Through comprehensively depicting the targeted harmonic response using features in multiple domains, the rational bandwidth can be selected for Vold–Kalman filtering, and then, a reliable monitoring process can be ensured. Additionally, a tacho-less speed estimation procedure is utilized in this paper to acquire the instantaneous rotational speed from the vibration signal directly. With the rational bandwidth and the estimated rotational speed, the desired harmonic components of vibrations can be adaptively extracted and tracked through the Vold–Kalman filter with high accuracy, and at the same time, the irrelevant or unwanted components are excluded completely. The effectiveness and superiority of the proposed adaptive Vold–Kalman filtration for wind turbine planetary gearbox diagnostics are demonstrated and validated experimentally.

Nonlinear dynamic modeling and analysis of a helicopter planetary gear set for tooth crack diagnosis

Planetary gearset is a vital dynamic component in helicopter transmission. In the planetary gearbox, there are many nonlinear dynamic factors, such as time-varying meshing stiffness, bearing support stiffness, comprehensive transmission error and tooth backlash. These nonlinear factors will deeply affect the vibration response characteristics of the gearbox system, and bring new challenges to the crack fault detection and diagnosis. To unveil the nonlinear properties of planetary gear set, a 21 degrees of freedom translational-torsional model of the planetary gear set is established by integrating above-mentioned nonlinear factors. Analysis of the fault influence on natural frequency and statistical indicators of simulation responses, the optimal indicators for fault detection are found. Then, nonlinear dynamic characteristics of gear system under fault state are conducted, and crack fault symptoms are pointed out using time-domain waveform analysis and power spectrum analysis of response signals, which actively illustrates the system fault mechanism and achieves fault isolation. Finally, the effectiveness of the model for planetary gear set with tooth crack is verified by experiments.

Reducing noise and vibrations in wind power plants with DFIG and planetary gearbox through improved rotor current control

Planetary gears in wind power plants transmit force excitations to the mechanical structure due to alternating tooth meshes. Structure-borne sound is generated and proceeds to tower and nacelle where it is radiated to the environment and may be a noise pollution to residents. In addition, unwanted vibrations can decrease the durability of the gearbox. So far, structural vibrations are being avoided by constructive measures or active absorbers. In contrast to this, our approach is to reduce gear mesh excitations only with the existing electric generator. Therefore, the rotor current control of the machine is enhanced to inject high frequency oscillations so that the wind power generator produces a specific oscillating torque. This anti-noise torque ripple is transmitted via the shaft to the gear stage and minimizes the gear mesh orders so that less force excitations are exposed to the mechanical structure at the bearings. In our knowledge this anti-noise torque approach using the electric generator and its current control is completely new in the field of wind power plants. Simulation is performed using a multi-body-model of the drivetrain to identify an optimal torque oscillation. Afterwards a control circuit model of the generator and the power converter for the rotor circuit is shown. Hence, it is possible to calculate the behavior of the system in high level of detail on the mechanical and the electrical side. Resulting bearing forces are analyzed to prove that excitation can be reduced significantly. No additional hardware, especially no additional actor is needed.

Informative singular value decomposition and its application in fault detection of planetary gearbox

The fault features of planetary gearboxes are modulated complexly and are submerged by other signal components, for its vibration signal has the characteristics of multi-source and multi transmission path. A fault detection method of planetary gearboxes based on informative singular value decomposition and envelope spectrum analysis (ISVD-ESA) is proposed in this paper. In this method, the advantage of blind source separation of singular value decomposition (SVD) method is combined with the ability of negentropy and cyclic autocorrelation (CA) in non-Gaussian characteristics recognition. The fast SVD is firstly performed to decompose the vibration signal into a series of singular value decomposition component signals (SVCSs). Secondly, the detector of negentropy combined with CA is applied to estimate the fault informativeness of each SVCS. The SVCSs are amplified by the fault informativeness and reconstructed to the out signal of ISVD. Finally, the fault features can be extracted by the ESA from the output signal of ISVD. The performance of the proposed method is verified by simulation and experimental studies. Results show that the proposed ISVD-ESA strategy can enhance the weak features of multi-modulation and accurately extract the faults of tooth tip pitting and misalignment of sun gear of the planetary gearbox.

Adaptive high-resolution order spectrum for complex signal analysis of rotating machinery: Principle and applications

Order spectrum analysis is commonly used in rotating machinery vibration signal analysis under nonstationary conditions, because of its capability in detecting the rotating frequency and harmonics. Nevertheless, the conventional order spectrum suffers from some inherent drawbacks. For example, its application is limited by the inconvenience of rotary encoder or tachometer installation on running machines. Meanwhile, it is subjected to disturbances by speed-irrelative components, such as resonances and background noise, and angular resampling errors as well. More importantly, it is obtained by Fourier transforming the angular resampled signal, but the amplitude envelope after angular resampling is still nonstationary under time-varying conditions, leading to spectral blur of frequency orders and low resolution. To address the above issues, we propose an adaptive high-resolution order spectrum method, by exploiting the capability of multiple order probabilities approach (MOPA) in estimating rotating speed and that of adaptive iterative generalized demodulation (AIGD) in identifying true signal components of interest respectively. Firstly, the MOPA is utilized to extract the rotating speed from vibration signals, instead of using rotary encoders or tachometers. Then, the AIGD is adopted to identify true speed-related mono-components adaptively, thus eliminating interferences by speed-irrelative components. Finally, the order spectrum is constructed based on the average amplitude envelope and frequency order of each true component, instead of through angular resampling and Fourier transform. By doing so, the proposed order spectrum can adaptively identify rotating frequency orders in high resolution. The adaptability lies in that it does not need rotary encoders or tachometers, but adaptively estimates speed and identify rotating frequency harmonics, and therefore is more convenient in applications. The high resolution lies in that the energy highly concentrates at true frequency order, because it does not involve Fourier transform and angular resampling, thus being free of spectral blur, interferences by speed-irrelative components and resampling errors. The proposed method is illustrated and validated by analyses of numerical simulation and typical rotating machinery signals, including lab experiment and in-situ measurement of planetary gearboxes, and on-site measurement of a hydraulic turbine.

Vibration AM-FM sidebands mechanism of planetary gearbox with tooth root cracked planet gear

The planet gear crack induced speed fluctuation is fully considered in the three effect factors of vibration signal like path weighting function, meshing forces and the measured-direction projection function. A series of phenomenon models of the vibration AM-FM signals of planet gear with tooth root crack are then developed by introducing three effect factors. The vibration AM-FM sidebands of cracked planet gear are finally obtained from the models, which are classified into two clusters that respectively locate at whole frequency range and harmonics of the meshing frequency. Harmonics of the fault feature frequency in the whole frequency range are modulated by the rotation frequency of carrier, which is obvious in the resonance ranges. Besides, harmonics of the meshing frequency are modulated by the fault feature frequency to form fault modulation sidebands, which are further modulated by the rotation frequency of carrier. The experimental results agree well with those obtained from the phenomenon models. The vibration signal models considering the cracked fault induced speed fluctuation perfect the models only containing the fault AM effect. The improved models clearly explain the sources of complicated vibration modulation sidebands even with small amplitudes, which provide a reliable theoretical basis for the detection of planet gear with tooth root crack.

Design and Testing of a Novel, High-Performance Two DoF Prosthetic Wrist

Extrinsically powered prosthetic wrists have the potential to offer significant improvements to the functionality and dexterity of a prosthetic hand. They can also reduce a user’s overreliance on their intact limb and help prevent injury from overuse of upper limb (both intact and residual) and trunk joints. Despite these potential advantages, there are very few prosthetic wrist options that are commercially available and these devices are not commonly used by prosthetic hand users due to several factors including inadequate performance specifications. In this paper, we first seek to establish the target specifications for a prosthetic wrist suitable for both median men and women. We then complete a comprehensive review of the state-of-the-art in extrinsically powered prosthetic wrists in the research, commercial, and patent literature. This review determines that no existing prosthetic wrist meets the target specifications due to the presence of actuators and transmissions that do not offer sufficient torque density, power density, and specific power. In order to address this challenge and produce a prosthesis that achieves target specifications, we next review the performance of existing actuators and transmissions and determine that Brushless DC motors with planetary gearboxes and ball screws offer the best potential to achieve the target specifications. We then present the design of a novel two Degree of Freedom parallel kinematic prosthetic wrist that incorporates this actuator-transmission combination. This first iteration of the proposed prosthetic wrist meets the target torque, speed, and weight but does not meet the target dimensions or range of motion yet. We propose design improvements in subsequent iterations that could lead to a prosthetic wrist that meets all the target specifications of torque, speed, weight, and volume.

Two-level variational chirp component decomposition for capturing intrinsic frequency modulation modes of planetary gearboxes

Due to the advantages of high transmission ratios and large carrying capacity, planetary gearboxes have been widely used in various industrial machinery. Vibration signals caused by planetary gearbox faults usually have intrinsic frequency modulation (FM) modes. Since the intrinsic FM modes have fast time-varying and oscillating characteristics, extraction of the intrinsic FM modes is still a challenging task. In this paper, a two-level variational chirp component decomposition (VCCD) approach is proposed to capture the intrinsic FM modes from vibration signals caused by planetary gearbox faults. Specifically, to avoid complicated sideband analysis, Level 1 of the VCCD is designed to extract gear fault characteristics from an amplitude modulation (AM) signal obtained by the Hilbert transform and a preliminary fault detection conclusion can be drawn to locate gear faults. Level 2 of the VCCD is employed to accurately extract the oscillation FM mode of a FM signal obtained by removing the AM signal from an original vibration signal. Results of Level 2 of the VCCD can not only verify the initial conclusion previously drawn from Level 1 but also explicitly and comprehensively reveal fault features of planetary gearboxes. The potential and effectiveness of the proposed two-level VCCD method are verified by two simulated cases related to planet gear faults under variable speed conditions and two experiments respectively related to a broken ring gear under constant speed conditions and a worn sun gear under variable speed conditions.

Condition-Based Monitoring on High-Precision Gearbox for Robotic Applications

This work presents a theoretical and experimental study regarding defect detection in a robotic gearbox using vibration signals in both cyclostationary and noncyclostationary conditions. The existing work focuses on inferring the health of the robot during operation with little regard toward the defective element of the components. This article illustrates the detection of specific element damage of a robotic gearbox during a robotic cycle based on domain knowledge and presents a novel data-driven method for asset health. This starts by studying the robotic gearbox, specifically its kinematics as a planetary 2-stage reduction gearbox to acquire the knowledge of the rotations of each component. The signals acquired from a test bench with four sensors undergo different acquisition methods and signal processing techniques to correlate the elements’ frequencies. The work shows the detection of the artificially created defects from the acquired vibration data, verifying the kinematic methodology and identifying the root cause of failure of such gearboxes. A novel resampling method, Binning, is presented and compared with the traditional signal processing techniques. Binning combined with Principal Component Analysis (PCA) as a data-driven method to infer the state of the gearbox is presented, tested, and validated. This work presents methods as a step toward automatized predictive maintenance on robots in industrial applications.

РАБОТА КОЛЕСНОГО ДИФФЕРЕНЦИАЛА В ПРОЦЕССЕ РАЗГОНА ПАХОТНОГО АГРЕГАТА

The use of ballast on wheeled tractors leads to over-compaction of the soil, including sub-arable horizons. When overclocking arable units with buried working bodies at operating speed, the engine will inevitably enter the overload mode. This leads to a decrease in productivity, fuel overspending, and deterioration of environmental indicators. The authors proposed for the first time to equip the driving wheels with built-in differentials. The basis of the wheel differential development concept is the eccentric application of external loads and the driving torque to the wheel. The purpose of the research is to identify structural and operational factors affecting the efficiency of the wheel differential, to establish the nature of the relationships between them. The operation of the driving wheel tire occurs under conditions of a deformed support surface. Acceleration of a mobile power vehicle with a built-in differential in the wheels is presented as a two-stage process. The first stage of acceleration corresponds to the operation of the gearbox in the planetary gearbox mode, the second stage - in the differential gearbox mode. The translational movement of the wheel, its rotation along the bearing surface is possible if the torque on the wheel rim is provided by the moment of friction force in the contact spot. The rotation of the bearing gear along the epicyclical gear, the formation of the tilting and lever moments in the wheel reducer contribute to the advancement of the pressure center along the length of the contact spot in the oncoming sector of the wheel, an increase in the friction force and tangential force, ensuring smooth starting of the tractor unit. After overcoming the moment of inertia of the rotating and translationally moving masses of the unit, the value of the required moment decreases. The driving bearing gear, remaining in the lower position, rotates the wheel. The gearbox has switched to differential operation. At the same time, the driver performs angular oscillations, the magnitude of which depends on the conditions of movement of the wheel and the traction resistance of the unit. The amount of torque required depends on the design parameters of the built-in differential, operating conditions, and gravity applied to the bearing drive gear of the planetary gearbox

Intelligent Fault Diagnosis for Planetary Gearbox Using Time-Frequency Representation and Deep Reinforcement Learning

Accurately and intelligently identifying faults of the planetary gearbox is essential in the safe and reliable operation and maintenance of the mechanical drive system. Recently, fault diagnosis of planetary gearbox has acquired tremendous progress, especially with the rising popularity of deep learning (DL). However, most methods are standard supervised learning where the input is directly mapped to a fault type, and with strong feedback. Also, their learning ways are static and unlike human learning that gradually acquires knowledge by interaction with the environment. To a certain extent, these deficiencies reduce the generalization and intelligence level of DL-based fault diagnosis methods. Besides, due to harsh working conditions, signals acquired often have strong noise and nonlinear features, leading to relatively low accuracy if raw signals are used as the input directly. Thus, this article proposes a new fault diagnosis method based on time-frequency representation and deep reinforcement learning (DRL). We first define fault diagnosis as a sequential decision-making problem in the classification Markov decision process. Next, the vibration signals are converted to uniform-sized TF maps by synchro-extracting transform to enhance the robustness of feature representation. Finally, a diagnosis agent is built and trained in the framework of DRL to learn the optimal classification policy automatically. Experimental results show that this method not only achieves better generalization and stability with an overall accuracy of over 99.5% in single-speed load cases but also outperforms others in multiwork conditions.

Design of Active Continuous Variable Transmission Control System with Planetary Gear

Planetary gearboxes have been employed in various applications due to their advantages of maintaining a high speed-reduction ratio, such as in wind power applications which have now become a prevailing green energy resource. A traditional power transmission machine of the wind turbine has a fixed gear-ratio mechanical gearbox for speed-increasing transmission. The purpose of this study is to propose an active continuous variable transmission (ACVT) control system with a planetary gear to apply to the wind turbine. The planetary gear holds three terminals, i.e., the ring gear, the planet carrier, and sun gear, and the motions of three terminals can be controlled purposely by the servomotors to achieve ACVT. The three different transmission types of the proposed ACVT can be operated. The dynamic characteristic of the planetary gear is expressed in block diagram form, and a pseudo derivative feedback feed-forward controller of the velocity control loop is designed for the required performance. The results can be used to verify the effect of the proposed ACVT with the planetary gear.

Gear Fault Detection in a Planetary Gearbox Using Deep Belief Network

Traditional prognostics and health management (PHM) methods for fault detection require complex signal processing and manual fault feature extraction, and the accuracy is low. To address these problems, a fault diagnosis method of planetary gearbox based on deep belief networks (DBNs) is proposed. Firstly, the vibration signals of the planetary gearbox are collected and analyzed in the time domain and the frequency domain. Then, the DBN model and optimal parameters are determined to meet the task requirements. Finally, the vibration data is divided into training set and test set and input into the DBN model, which can realize the automatic feature extraction and fault recognition of vibration signals. The results show that the identification accuracy reaches 97% under five working conditions of planetary gearbox.

Deterioration state diagnosis and wear evolution evaluation of planetary gearbox using vibration and wear debris analysis

The deterioration state of planetary gearboxes with pitting faults cannot be diagnosed and evaluated effectively with a single type of monitoring signal and traditional features. In this study, a new diagnose method was proposed based on the tribology and vibration signal of planetary gear systems. A new feature extraction approach was exploited to construct a multi-feature fusion relevance vector machine (MFFRVM) model for diagnosing the deterioration state of planetary gearboxes. Meanwhile, a novel index for online wear debris monitoring (ADIDC) was extracted to evaluate the wear evolution of planetary gears. Condition monitoring experiments were conducted on a planetary gearbox. Experimental results show that the diagnostic method combining the MFFRVM model and ADIDC index achieves better accuracy than other methods. This study provides guidance for the wear evolution evaluation of planetary gears with pitting faults and the early fault recognition of planetary gearboxes.

Refined time-shift multiscale diversity entropy: a novel feature extraction algorithm for fault diagnosis of planetary gearbox

Abstract Planetary gearboxes play a critical role in aerospace and heavy industry fields, such as wind turbines, heavy vehicles and construction machines. Intelligent fault diagnosis is significant for safe operation and fault prevention of planetary gearboxes. Recently, multiscale diversity entropy and related entropy methods are proposed to extract features of time series and applied for the fault diagnosis. However, there are still some limitations in fault feature representation and stability for multiscale diversity entropy. To solve the problem, in this paper, a novel planetary gearboxes fault diagnosis method via refined time-shift multiscale diversity entropy (RTSMDE) is proposed. First, a novel entropy algorithm called RTSMDE is proposed to measure the complexity of time series and extract fault features of the vibration signals, which is robust and efficient in performance. Then, the obtained features are utilized to fulfil automatically the fault pattern identifications using support vector machine. To confirm the superiority of the RTSMDE-based fault diagnosis method, simulated signals and experimental studies are constructed and three used widely methods are employed to present a comprehensive comparison. The results indicate that RTSMDE performs best and obtains the highest accuracy.

Unsupervised adversarial domain adaptive for fault detection based on minimum domain spacing

The deep learning model has gradually matured in the detection of mechanical faults. However, due to the changes in the mechanical operating environment and the application of new sensors in real work, the effect of the training model is not ideal in field applications. The key of this problem is the deviation of feature space mapping between training source domain and application target domain. This paper proposes an unsupervised adversarial domain adaptive fault diagnosis transfer learning model based on the minimum domain spacing to reduce the deviation. In adversarial network training, by training the weight parameters of the classifier, some features extracted by the composed classifier are added to the feature distribution of the target domain through weight changes, which reduces the feature distribution difference between the source domain and the target domain. It is reflected on the reduction of the maximum mean difference distance (MMD) between the two domains, and the fitting features of the data distribution are improved. Finally, through two experimental platforms of rolling bearing and planetary gearbox dataset, the results of six diagnostic tasks show that the new model reduces the amount of parameters by 33.66% and keeps accuracy more than 99% compared with the DANN model under the condition.

Substantiations of the beginnings of a unified theory of planetary gears

Multi-satellite planetary mechanisms are analyzed. A new approach to the creation of planetary gearboxes on the principle of L. V. Assur's mechanisms is proposed, according to which the satellites add a group of links of zero mobility to the kinematic chain. Schemes of patented multi-satellite planetary gears are shown. The basis for the development of a new theory of design and manufacture of planetary gearboxes is presented. Keywords: planetary gear, satellite, multi-satellite transmission, uniform load distribution, gear transmission. tmmiok@yandex.ru, tmmiok@yandex.ru

Cyclic symplectic component decomposition with application in planetary gearbox fault diagnosis

Due to the structural complexity of planetary gearbox, it is difficult to extract the fault information in the vibration signal of planetary gearbox, which seriously affects the accuracy of fault diagnosis. Although the existing fault diagnosis methods, such as local characteristic-scale decomposition (LCD) and ensemble empirical mode decomposition (EEMD), are widely used in the field of fault diagnosis, they are difficult to decompose complex signals effectively. Therefore, a new signal processing method, called cyclic symplectic component decomposition (CSCD) based on the Toeplitz matrix and the cyclic matrix, is proposed in this paper. In CSCD, to obtain the closed-loop solutions, each row is the right cyclic shift of the previous row, so as to avoid information leakage. Meanwhile, the combination of sample covariance and symplectic geometry similarity transformation is constructed to further protect the status information and weaken the influence of noise. In addition, the reconstruction of the same mode is completed by using the extreme difference similarity (EDS), which can realize the adaptive decomposition of the signal. The research results of simulation signal and actual planetary gearbox fault signal show that the proposed CSCD method has superior decomposition performance.