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Stator current modeling for defective planetary gearboxes based on magnetic equivalent circuits (MEC) is conducted in this article. A lumped parameter torsional model of the motor-planetary gearbox coupling system is established to obtain the nonlinear time-variable torsional response of the rotor numerically. An MEC model of an induction motor is established by connecting the equivalent flux tubes with nodes. After considering the time-variable rotor torsional response, the nonlinear magnetic field intensity of the iron material, and magnetic saturation, an iterative numerical integration method is presented to solve the MEC model. Both a finite element analysis and dynamic tests on a planetary gearbox driven by an induction motor are carried out for verification. Based on these, the fault characteristic components in the stator current spectra are, respectively, identified when the tooth chipping defect appears at sun/planet/ring gears. Fault-related frequencies are essentially represented by the combinations between the meshing frequency, sun/planet/ring gear fault passing frequency, its harmonics, and the power supply frequency. Owing to the electromechanical coupling effect, high-order harmonics of the power supply frequency also participate in the fault-related frequencies of the planet and ring gears. By quantitatively analyzing the influence of chipping size on the amplitudes of fault characteristic frequencies, suitable spectra for planetary gearbox condition monitoring are recommended. [ABSTRACT FROM AUTHOR]

Wavelet transform, an effective tool to decompose signals into a series of frequency bands, has been widely used for vibration-based fault diagnosis in machinery. Likewise, the use of deep learning algorithms is becoming popular to automatically learn discriminative features from input data for the sake of improving diagnostic performance. However, the fact that no general consensus has been reached as to which wavelet basis functions are useful for diagnosis motivated this investigation of methods to fuse multiple wavelet transforms into deep learning algorithms. In this paper, two methods—i.e., multiple wavelet coefficients fusion in deep residual networks by concatenation and multiple wavelet coefficients fusion in deep residual networks by maximization—were developed to capture discriminative information from diverse sets of wavelet coefficients for fault diagnosis. The efficacy of the developed methods was verified by applying them to planetary gearbox fault diagnosis. [ABSTRACT FROM AUTHOR]

Time–frequency analysis (TFA) is considered as a useful tool to extract the time-variant features of the nonstationary signal. In this paper, a new method called demodulated high-order synchrosqueezing transform (DHST) is proposed. The DHST introduces a two-step algorithm, namely, demodulated transform and high-order synchrosqueezing method to achieve a compact time–frequency representation (TFR) while enabling the reconstruction of the signal from TFR. The performance of the proposed DHST method in this paper is validated by both the simulated and experimental signals including bat echolocation and a vibration signal. The results show that the proposed TFA method is more effective in processing the nonstationary signals with fast varying instantaneous frequency. [ABSTRACT FROM AUTHOR]

Vibration-based fault diagnosis of a planetary gearbox is challenging due to the revolving planet gears inducing modulation of vibration signals. For accurate fault diagnosis, researchers have suggested that vibration signals should be extracted by window function when the planet gears are positioned under the sensor. However, vibration modulation characteristics can be affected by operational and geometrical uncertainties. Thus, fault-related features can be inadvertently discarded when the window function is employed. Alternatively, periodicity analysis of anomalies can be employed with the entire vibration signal without the use of window function. However, it is challenging in a planetary gearbox because the fault-related features are also modulated. This paper proposes a health data map for toothwise fault identification in a planetary gearbox. In the proposed approach, samplewise health data are aligned in the domains of a pair of gear teeth of the planetary gearbox. The proposed approach represents the health state of every pair of gear teeth, while making it possible to isolate the location of any faulty teeth in the gears. For demonstration of the proposed method, two case studies are presented: an analytical model and a 2-kW testbed. The results suggest that the proposed method performs well even under unexpected vibration modulation characteristics. [ABSTRACT FROM PUBLISHER]

One of the significant tasks in data-driven fault diagnosis methods is to configure a good feature set involving statistical parameters. However, statistical parameters are often incapable of representing the dynamic behavior of planetary gearboxes under variable operating conditions. Although the use of deep learning algorithms to find a good set of features for fault diagnosis has somewhat improved diagnostic performance, the lack of domain knowledge incorporated into deep learning algorithms has limited further improvement. Accordingly, this paper developed a variant of deep residual networks (DRNs), the so-called deep residual networks with dynamically weighted wavelet coefficients (DRN+DWWC) to improve diagnostic performance, which takes a series of sets of wavelet packet coefficients on various frequency bands as an input. Further, the fact that no general consensus has been reached as to which frequency band contains the most intrinsic information about a planetary gearbox's health status calls for “dynamic weighting layers” in the DRN+DWWC and the role of the layers is to dynamically adjust a weight applied to each set of wavelet packet coefficients to find a discriminative set of features that will be further used for planetary gearbox fault diagnosis. [ABSTRACT FROM PUBLISHER]

Planetary gearboxes (PGBs) are widely used in the drivetrain of wind turbines. Any PGB failure could lead to breakdown of the whole drivetrain and major loss of wind turbines. Therefore, PGB fault diagnosis is important in reducing the downtime and maintenance cost and improving the reliability and lifespan of wind turbines. PGB fault diagnosis has been done mostly through vibration analysis over the past years. Vibration signals theoretically have an amplitude modulation (AM) 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. Strain sensor signals, on the other hand, are closely correlated to torsional vibration, which is less sensitive to the AM effect caused by rotating vibration transfer path. Thus, it is potentially easy and effective to diagnose PGB faults via stain sensor signal analysis. In this paper, a new method using a single piezoelectric strain sensor for PGB fault diagnosis is presented. The method is validated on a set of seeded localized faults on all gears, namely, sun gear, planetary gear, and ring gear. The validation results have shown a satisfactory PGB fault diagnostic performance using strain sensor signal analysis. [ABSTRACT FROM PUBLISHER]

Motor current signal analysis (MCSA) provides an alternative nonintrusive approach to detect mechanical faults by using the fault signature transmitted along the torsional direction through the rotor. In existing fault detection methods based on MCSA, the gearbox health condition is monitored through the amplitude of the fault-related sidebands in the lower frequency range of the motor current spectra. However, their practical implementation is challenged by the harmonics resulting from the structural properties of the electrical machines and the inherent system imperfections. This effect is even more severe in case of a drivetrain containing planetary gearboxes due to its more complex assembly. In this paper, the resonance residual technique, which investigates the spectrum region around the resonance frequency where rich fault information may occur, is applied for the first time to MCSA to detect planetary gearbox faults. This proposed approach is verified through both simulation and experiments. A lumped parameter model for an electromechanical drive train with an annulus gear tooth crack is simulated to investigate its effect on the stator current. For experimental verification, a similar 4-kW motor-planetary gearbox–generator test rig is used. The robustness of the proposed method is demonstrated through simulations of a nonlinear finite-element model and experiments under different operating conditions. Furthermore, the effectiveness of the proposed method to extract fault information over the existing methods is also shown. [ABSTRACT FROM PUBLISHER]