In order to solve the difficult problem of early fault feature extraction of planetary gearbox and consider that the planetary gearbox vibration signal is coupling and nonlinear，and the signal has ...multiple transmission paths，a planetary gearbox fault diagnosis method based on Local Mean Decomposition（LMD） and Sample Entropy and Extreme Learning Machine（ELM） is proposed.Firstly，the vibration signal is adaptively decomposed into a plurality of PF components by LMD，and the first four PF components including the main fault information are selected in combination with the correlation coefficient and the variance contribution rate.Secondly，the Sample Entropy of the signal is calculated to form a feature vector.Finally，the feature vector is input into ELM for fault classification.Experiments are carried out on the planetary gearbox test bench，compared with the probabilistic neural network classification algorithm，and compared with the feature vector based on Singular Value Decomposition （SVD）.The results verify the
Tanh is a sigmoidal activation function that suffers from vanishing gradient problem, so researchers have proposed some alternative functions including rectified linear unit (ReLU), however those ...vanishing-proof functions bring some other problem such as bias shift problem and noise-sensitiveness as well. Mainly for overcoming vanishing gradient problem as well as avoiding to introduce other problems, we propose a new activation function named Rectified Linear Tanh (ReLTanh) by improving traditional Tanh. ReLTanh is constructed by replacing Tanh’s saturated waveforms in positive and negative inactive regions with two straight lines, and the slopes of the lines are calculated by the Tanh’s derivatives at two learnable thresholds. The middle Tanh waveform provides ReLTanh with the ability of nonlinear fitting, and the linear parts contribute to the relief of vanishing gradient problem. Besides, thresholds of ReLTanh that determines the slopes of line parts are learnable, so it can tolerate the variation of inputs and help to minimize the cost function and maximize the data fitting performance. Theoretical proofs by mathematical derivations demonstrate that ReLTanh is available to diminish vanishing gradient problem and feasible to train thresholds. For verifying the practical feasibility and effectiveness of ReLTanh, fault diagnosis experiments for planetary gearboxes and rolling bearings are conducted by stacked autoencoder-based deep neural network (SAE-based DNNs). ReLTanh alleviates successfully vanishing gradient problem and the it learns faster, more steadily and precisely than Tanh, which is consistent with the theoretical analysis. Additionally, ReLTanh surpasses other popular activation functions such as ReLU family, Hexpo and Swish, which shows that ReLTanh has certain applying potential and researching value.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Abstract
Planetary gearboxes have various merits in mechanical transmission, but their complex structure and intricate operation modes bring large challenges in terms of fault diagnosis. Deep ...learning has attracted increasing attention in intelligent fault diagnosis and has been successfully adopted for planetary gearbox fault diagnosis, avoiding the difficulty in manually analyzing complex fault features with signal processing methods. This paper presents a comprehensive review of deep learning-based planetary gearbox health state recognition. First, the challenges caused by the complex vibration characteristics of planetary gearboxes in fault diagnosis are analyzed. Second, according to the popularity of deep learning in planetary gearbox fault diagnosis, we briefly introduce six mainstream algorithms, i.e. autoencoder, deep Boltzmann machine, convolutional neural network, transformer, generative adversarial network, and graph neural network, and some variants of them. Then, the applications of these methods to planetary gearbox fault diagnosis are reviewed. Finally, the research prospects and challenges in this research are discussed. According to the challenges, a dataset is introduced in this paper to facilitate future investigations. We expect that this paper can provide new graduate students, institutions and companies with a preliminary understanding of methods used in this field. The dataset can be downloaded from
https://github.com/Liudd-BJUT/WT-planetary-gearbox-dataset
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•Stacked wavelet auto-encoder with Morlet wavelet function is constructed.•A flexible weighted assignment of decision fusion strategy is designed.•Collaborative fault diagnosis framework driven by ...multisensory fusion is proposed.
Collaborative fault diagnosis can be facilitated by multisensory fusion technologies, as these can give more reliable results with a more complete data set. Although deep learning approaches have been developed to overcome the problem of relying on subjective experience in conventional fault diagnosis, there are two remaining obstacles to collaborative efficiency: integration of multisensory data and fusion of maintenance strategies. To overcome these obstacles, we propose a novel two-part approach: a stacked wavelet auto-encoder structure with a Morlet wavelet function for multisensory data fusion and a flexible weighted assignment of fusion strategies. Taking a planetary gearbox as an example, we use noisy vibration signals from multisensors to test the diagnosis performance of the proposed approach. The results demonstrate that it can provide more accurate and reliable fault diagnosis results than other approaches.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A new diagnostic model named SDAE-GAN is proposed.•The model combines Generative Adversarial Networks and Stacked Denoising Autoencoders.•The performance of the model is investigated in the ...planetary gearbox experiment platform.•Results suggest that SDAE-GAN is better than SDAE and other common diagnostic models in classification precision.
Planetary gearbox has complex structures and works under various non-stationary operating conditions. The vibration signals of planetary gearbox are complicated and usually polluted by noise and interference. It is difficult to extract effective features of early faults. In addition, there are only a small number of fault samples for planetary gearbox fault diagnosis. All of these increase the difficulty of planetary gearbox fault diagnosis. Aiming at these problems, a novel fault diagnostic method is proposed which combines Generative Adversarial Networks (GAN) and Stacked Denoising Autoencoders (SDAE). The generator of GAN can generate new samples which has similar distribution with original samples from planetary gearbox vibration signals. Then, generated samples are transformed to the discriminator together with original samples which expand the sample size. SDAE is used as the discriminator of GAN which can automatically extract effective fault features from input samples and discriminate their authenticity and fault categories. Through novel adversarial machine learning mechanism, the generator and discriminator are concurrently optimized to enhance the quality of generation samples and the ability of fault mode classification. The experimental results show that the developed SDAE-GAN method for planetary gearbox has good anti-noise ability and achieve better fault diagnosis performance in the case of small samples.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•A multi-sensor fusion method that combines vibration and rotational speed signals.•A novel deep learning algorithm (BiConvLSTM) to classifying planetary gear faults.•Superior to existing deep ...learning algorithms such as CNN, LSTM, and CNN-LSTM.
Gearbox fault diagnosis is expected to significantly improve the reliability, safety and efficiency of power transmission systems. However, planetary gearbox fault diagnosis remains a challenge due to complex responses caused by multiple planetary gears. Model-based gearbox fault diagnosis techniques extract hand-crafted features from sensor data based on underlying physics and statistical analysis, which are not effective in extracting spatial and temporal features automatically. While deep learning methods such as convolutional neural network (CNN) enable automatic feature extraction from multiple sensor sources, they are not capable of extracting spatial and temporal features simultaneously without losing critical feature information. To address this issue, we introduce a novel deep neural network based on bidirectional-convolutional long short-term memory (BiConvLSTM) networks to determine the type, location, and direction of planetary gearbox faults by extracting spatial and temporal features from both vibration and rotational speed measurements automatically and simultaneously. In particular, a CNN determines spatial correlations between two measurements within one time step automatically by combining signals collected from three accelerometers and one tachometer. Long short-term memory (LSTM) networks identify temporal dependencies between two adjacent time steps. By replacing input-to-state and state-to-state operations in the LSTM cell with convolutional operations, the BiConvLSTM can learn spatial correlations and temporal dependencies without losing critical features. Experimental results have shown that the BiConvLSTM network can detect the type, location, and direction of gearbox faults with higher accuracy than conventional deep learning approaches such as CNN, LSTM, and CNN-LSTM.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•We explain the root cause of observed asymmetry in planet gear fault signals.•Sequential mesh phasing of the planet gears is responsible.•Indicator proposed based on timing of fault symptoms ...relative to gearmesh phase.•Indicator can locate a faulty planet gear if a phase reference is available.•The first time mesh phasing relationships have been used for diagnostics.
It is a challenge to determine by vibration analysis which planet gear carries a fault due to the complex layout of planetary gearboxes. In this study, we explain for the first time the possibility of using mesh phasing for distinguishing between faults on different planet gears. It has been found that, due to mesh phasing relationships, the vibration signals recorded on a planetary gearbox test rig exhibit different characteristics depending on the position of the faulty gear. These tests used localised seeded spalls giving impulsive signals, and it was found that due to the sequential mesh phasing arrangement, the timing of the fault-related impulse responses, as measured relative to the phase of the gearmesh component, depends on which planet is faulty. These timing differences can also give rise to different levels of asymmetry in the signal.
This paper proposes a phase-based approach to differentiate and locate the faulty planet gear position using vibration response signals, and an indicator is developed for this purpose. With the assistance of absolute phase information, acquired through a tachometer on the planet carrier, the phase indicator is able to locate the position of the faulty gear. The approach has been applied to recorded experimental signals and it is found that the method can diagnose the position of the faulty gear effectively.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A one-dimensional convolutional neural network model is proposed to Diagnostic fault with the original vibration signal.•The parameter optimization selection of the 1-DCNN model is ...analyzed.•Compared to three traditional methods, both tests of 1-DCNN achieved an accuracy of about 99.3%.
Fault diagnosis of rotating machinery plays a significant role in the reliability and safety of modern industrial systems. The traditional fault diagnosis methods usually need manually extracting the features from raw sensor data before classifying them with pattern recognition models. This requires much professional knowledge and complex feature extraction, only to cause results in a poor flexibility of the model, which only applies to the diagnosis of a fault in particular equipment. In recent years, deep learning has developed rapidly, and great achievements have been made in image analysis, speech recognition and natural language processing. However, its application in fault diagnosis of rotating machinery is still at the initial stage. In order to solve the problem of end-to-end fault diagnosis, this paper focuses on developing a convolutional neural network to learn features directly from the original vibration signals and then diagnose faults. The effectiveness of the proposed method is validated through PHM (Prognostics and Health Management) 2009 gearbox challenge data and a planetary gearbox test rig. Compared with the other three traditional methods, the results show that the one-dimensional convolutional neural network (1-DCNN) model has higher accuracy for fixed-shaft gearbox and planetary gearbox fault diagnosis than that of the traditional diagnostic ones.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Review vibration based fault diagnosis methods of wind turbine planetary gearbox.•Fundamental analysis of the planetary gearbox vibration response are reviewed.•Fault enhancement and operation ...pattern effect elimination are reviewed.•Feature extraction and fault diagnostics strategy are reviewed.•Prospects of vibration based fault diagnosis methods are discussed.
As one of the most immensely growing renewable energies, the wind power industry also experiences a high failure rate and operation & maintenance cost. Therefore, the condition monitoring and fault diagnosis of a wind turbine (WT) generator set are highly needed. Among different components of a WT generator set, WT planetary gearbox plays a crucial role in transmission and leads to relatively higher failure rate and longer downtime. Towards this, a number of studies have been reported in both the academic journals and conference proceedings. This paper provides a systemic and pertinent state-of-art review on WT planetary gearbox condition monitoring techniques on the topics of fundamental analysis, signal processing, feature extraction, and fault detection. Moreover, a few valuable open issues are pointed out and potential research directions are suggested.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•The HSWT method can effectively improve the TF energy concentration and obtain a high accuracy reconstruction for signals with fast varying IF.•The performance of the HSWT method has been ...theoretically proved.•The effectiveness of the HSWT method has been verified by a simulated signal and experimental signals.
The synchrosqueezing transform (SST) is a powerful tool for time-frequency analysis of signals with slowly varying instantaneous frequency (IF). However, the SST and its extensions provide poor time-frequency resolution for signals with wide frequency range and fast varying IF. In this paper, a new SST method called high-order synchrosqueezing wavelet transform is proposed to achieve a highly energy-concentrated time-frequency representation (TFR) for nonstationary signals with wide frequency range and fast varying IF. This method uses high-order group delay and chirp rate operators to obtain the accurate estimation of instantaneous frequency. The proposed method can effectively improve the energy concentration of the TFR and remain invertible simultaneously. The numerical simulations investigate the performance and noise robustness of the proposed method when analyzing a typical amplitude-modulated and frequency-modulated (AM-FM) multicomponent signal. Finally, the application of planetary gearbox fault diagnosis in the variable operating condition verifies the effectiveness of the proposed method.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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