Gear Fault Vibration Signal Research Articles

Composite fault feature extraction for gears based on MCKD-EWT adaptive wavelet threshold noise reduction

For the strong noise gear fault vibration signal is relatively weak, and the transmission path is complex and variable, in the case of composite faults, the modulation of different fault characteristics of the frequency, coupling, resulting in the actual acquisition of the fault characteristics are difficult to extract and separate. Aiming at fault feature extraction and separation, an adaptive threshold denoising fault detection method based on Maximum correlated kurtosis deconvolution (MCKD) and Empirical wavelet transform (EWT) is proposed. Firstly, envelope entropy and information entropy are used as fitness functions, and the parameters of the MCKD algorithm are optimized by the improved particle swarm algorithm, then the empirical wavelet decomposition is carried out on the signals, and finally adaptive wavelet threshold denoising is carried out on the decomposed Intrinsic mode functions (IMFs) components. The results of experimental data analysis show that compared with the feature extraction methods such as spatial scale threshold EWT-MCKD and Complete Ensemble Empirical Mode Decomposition (CEEMDAN)-MCKD, the proposed method is more suitable for the diagnosis of gear composite faults in a strong background noise environment, the noise interference is effectively suppressed, and the extraction effect of gear composite fault features is more obvious.

Fault feature extraction method of gear based on optimized minimum entropy deconvolution and accugram

Gear fault vibration signals are commonly non-stationary, and useful fault information is often buried in heavy noise, which makes it difficult to extract gear fault features. How to select the suitable fault frequency bands is the key to gear fault diagnosis. To address the above problems, a method combining the improved minimum entropy deconvolution (MED) and accugram, named IMEDA, is proposed for extracting gear fault features. Firstly, a selection index based on permutation entropy (PE) and correlation coefficient is defined. Then, the optimal filter length can be effectively selected by the step-length searching method using the proposed index as objective function, and the improved MED is employed to preprocess the gear vibration signals. Finally, the accugram analysis is performed for the preprocessed signals to obtain the optimal frequency band, and the fault characteristic frequencies are extracted from the square envelope spectrum of the signals in the optimal band. The method is validated by gear experimental data with gear wear-out failure. The analysis results demonstrate that the proposed method owns superior effect by comparing with the fast kurtogram (FK), MED combined with FK (MED-FK), accugram and infogram.

Gear Fault Diagnosis Based on Genetic Mutation Particle Swarm Optimization VMD and Probabilistic Neural Network Algorithm

The decomposition number $K$ and penalty factor $\alpha $ in the variational mode decomposition (VMD) algorithm have a great influence on the decomposition effect and the accuracy of subsequent fault diagnosis. Therefore, a gear fault diagnosis method based on genetic mutation particle swarm optimization VMD and probabilistic neural network (GMPSO-VMD-PNN) algorithm is proposed in this paper. Firstly, the GMPSO algorithm is used to optimize the $[K,\alpha]$ parameter combination in the VMD algorithm, and the optimal $[K,\alpha]$ parameter combination of each gear fault vibration signal to be decomposed is selected. Then, the gear fault vibration signal is decomposed into several intrinsic mode functions (IMFs) by VMD, and the sample entropy value of each IMFs is extracted to form the feature vector of subsequent fault diagnosis. Finally, the characteristic vector of gear fault vibration signal is input into PNN model, and gear fault is accurately classified. By comparing with fixed parameter VMD algorithm, empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm, the superiority of this method in gear fault diagnosis is verified. Therefore, the GMPSO-VMD-PNN algorithm proposed in this paper has certain application value for gear fault diagnosis.

Comparisons of different deep learning-based methods on fault diagnosis for geared system

The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system. Based on the measured gear fault vibration signals and the deep learning theory, four fault diagnosis neural network models including fast Fourier transform–deep belief network model, wavelet transform–convolutional neural network model, Hilbert-Huang transform–convolutional neural network model, and comprehensive deep neural network model are developed and trained respectively. The results show that the gear fault diagnosis method based on deep learning theory can effectively identify various gear faults under real test conditions. The comprehensive deep neural network model is the most effective one in gear fault recognition.

An early fault diagnosis method of gear based on improved symplectic geometry mode decomposition

Symplectic geometry mode decomposition (SGMD) is an effective signal processing method, and it has been applied in compound fault diagnosis successfully. However, for early gear fault vibration signals, SGMD has two shortcomings. On the one hand, SGMD directly reconstructs the trajectory matrix through the original time series, which may cause the weak fault features submerged in global time series. Therefore, add a slip window to preprocess the original time series. On the other hand, the symplectic geometry components (SGCs) with low energy and fault feature information are eliminated for denoise. Therefore, variable entropy (VE) weighting is proposed to obtain the weighted symplectic geometry components (WSGCs) containing the vast majority of fault feature information. In conclusion, an improved symplectic geometry mode decomposition (ISGMD) is proposed to overcome the above two shortcomings. Simulated and experimental results indicate that ISGMD is effective for raw vibration signals.

APPLICATION OF QUANTITATIVE ANALYSIS OF GEAR FAULT IN μ-SVD NOISE REDUCTION ALGORITHM DIAGNOSIS

In order to extract the fault features of mechanical equipment submerged by strong background noise, a general subspace denoising algorithm based on Singular value decomposition (SVD) is used to process the signal, that is, μ-SVD denoising algorithm. This algorithm overcomes the disadvantage of traditional wavelet threshold denoising algorithm, which only deals with the wavelet coefficients point by point and ignores the whole structure of the wavelet coefficients. The traditional SVD denoising algorithm is a special case when Lagrange multiplier μ = 0 in μ-SVD denoising algorithm. μ-SVD denoising algorithm includes filter factor, which can suppress the contribution of singular value dominated by noise contribution to the signal after denoising. The parameter selection method of μ-SVD denoising algorithm is discussed, and the influence of denoising order and Lagrange multiplier on denoising effect is emphatically studied. The test results of gear fault simulation signal and gear fault vibration signal at the early stage show that μ-SVD denoising algorithm is better than traditional SVD denoising algorithm in denoising effect. It can extract gear fault features better under strong background noise.

Weak fault feature extraction of gear based on KVMD and singular value difference spectrum

Gearbox is an important component of many industrial applications. When the gear fault occurs, the vibration signal is characterized by multi-component, multi-frequency modulation, low signal to noise ratio, weak fault characteristics and difficult to extract. This paper proposes a gear fault feature extraction method based on improved variational mode decomposition(VMD) and singular value difference spectrum. Firstly, the method is optimized for the decomposition level K of the VMD algorithm, and an improved method of VMD decomposition layer number K for central frequency screening (KVMD) is proposed. Then, the gear fault vibration signal is decomposed into a series of bandlimited intrinsic mode functions using KVMD. Due to the interference of the noise, it is difficult to make the correct judgment of fault in the spectrum of each mode component. According to the correlation coefficient criterion, the components with larger correlation coefficients are chosen to singular value decomposition. The singular value difference spectrum is obtained, and the effective order of the reconstructed signal is determined from the difference spectrum to denoise the signal; Finally, the processed signal is analyzed by Hilbert envelope. The fault characteristic frequency can be extracted accurately from the envelope spectrum. Through the analysis of the experimental data of gear fault, the results show that the method can effectively reduce the influence of the noise, and accurately realize the extraction of gear fault feature information.

Vibration Signature Analysis Of A Chipped Spur Gear Tooth

Signal processing is a widely applied tool for condition monitoring of rotating machinery. These techniques are thus utilized extensively to process experimental signals. However, hypothesis about data and computational efforts often restrict the application of some techniques. The empirical mode decomposition (EMD) and Hilbert spectrum allows to overcome these limitations. This paper applies this method to vibration signal analysis for localised gear fault diagnosis. Considering that the gear fault vibration signal generate both the amplitude and frequency demodulated signals, the EMD could exactly decompose these demodulated signals into a number of intrinsic mode functions (IMFs), each of which can be amplitude-demodulated or frequency-demodulated component, the frequency families could be separated effectively from the gear vibration signal by applying EMD to the gear vibration signal. Furthermore, when fault occurs in gears, the energy of the gear vibration signal would change correspondingly, whilst the local Hilbert energy spectrum can exactly provide the energy distribution of the signal in certain frequency range with the change of the time and frequency. Thus, the fault information of the gear vibration signal can be extracted effectively from the local Hilbert energy spectrum.

Envelope demodulation based on variational mode decomposition for gear fault diagnosis

A new time–frequency analysis method, based on variational mode decomposition, was investigated. When a gear fault occurs, its vibration signal is nonstationary, nonlinear, and exhibits complex modulation performance. According to the modulation characteristics of the gear vibration signal arising from faults therein, a gear fault diagnosis method based on variational mode decomposition and envelope analysis was proposed. The variational mode decomposition method can decompose a complex signal into several stable components. The obtained components were analyzed by envelope demodulation. According to the envelope spectrum, gear faults can be diagnosed. In essence, the variational mode decomposition method can decompose a multi-component signal into a number of single component amplitude modulation–frequency modulation signals. The method is suited to the handling of multi-component amplitude modulation–frequency modulation signals. The simulated signal and the actual gear fault vibration signals were analyzed. The results showed that the method can be effectively applied to gear fault diagnosis.

Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM

Aiming at the nonstationary characteristic of a gear fault vibration signal, a recognition method based on permutation entropy of ensemble local characteristic-scale decomposition (ELCD) and relevance vector machine (RVM) is proposed. First, the vibration signal was decomposed by ELCD; then a series of intrinsic scale components (ISCs) were obtained. Second, according to the kurtosis of ISCs, principal ISCs were selected and then the permutation entropy of principal ISCs was calculated and they were combined into a feature vector. Finally, the feature vectors were input in RVM classifier to train and test and identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnose four kinds of working condition, and the effect is better than local characteristic-scale decomposition (LCD) method.

Fault Diagnosis Method of Gear Based on Local Mean Decomposition and Least Squares Support Vector Machine

For differences of time-domain energy distribution of different gear fault vibration signal, an analytical method based on local mean decomposition (LMD) and least squares support vector machine (LS-SVM) is proposed to apply to gear fault diagnosis. First vibrational signal of gear is decomposed into a series of product functions (PF) by LMD method. Then extracting energy characteristic parameters of PF components which contain main fault information to constitute a fault feature vectors, which is considered as input sample of well-trained LS-SVM, and then identifying working state and fault type of different gear can be identified accurately and effectively by diagnostic method based on LMD and LS-SVM.

An Investigation of Tooth Wear Diagnosis of Gear Based on Process Systems Modeling

Targeting the advantages of local wave analysis(LWA) and the characteristics of gear fault vibration signals, LWA is introduced into gear fault diagnosis. The concept of the instantaneous energy in time- frequency analysis, based on local wave time-frequency spectrum, was used to measure the energy distribution of the signal in time-frequency domain. Furthermore, when tooth wear occurs in gear, the energy of the gear vibration signal would change correspondingly, whilst local wave time-frequency spectrum can exactly provide the instantaneous energy distribution of the signal with the change of the time and frequency. Thus, the fault information of the gear vibration signal can be extracted effectively from the local wave time-frequency spectrum. The analysis results from the experimental signals show that local wave time-frequency analysis could extract the characteristics information of the gear fault vibration signal effectively.

An order tracking technique for the gear fault diagnosis using local mean decomposition method

Local mean decomposition (LMD) is a new self-adaptive time–frequency analysis method, which is particularly suitable for the processing of multi-component amplitude-modulated and frequency-modulated (AM–FM) signals. By using LMD, any complicated signal can be decomposed into a number of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated signal from which physically meaningful instantaneous frequencies can be obtained. Theoretically, each PF is exactly a mono-component AM–FM signal. Therefore, the procedure of LMD can be regarded as the process of demodulation. While fault occurs in gear, the vibration signals would exactly present AM–FM characteristics. Therefore, targeting the modulation feature of gear fault vibration signal in run-ups and run-downs and the fact that fault characteristics found in gear vibration signal could often be related to revolution of the shaft in the transient process, a gear fault diagnosis method in which order tracking technique and local mean decomposition is put forward. The analysis results from the practical gearbox vibration signal demonstrate that the proposed algorithm is effective in gear fault feature extraction.

A gear fault diagnosis using Hilbert spectrum based on MODWPT and a comparison with EMD approach

When gear fault occurs, the vibration signals always display non-stationary behavior. Therefore time-frequency analysis has become the well-accepted technique for vibration-based gear fault diagnosis. This paper presents the application of a new time-frequency signal processing technique, the Hilbert spectrum based on the maximal overlap discrete wavelet packet transform (MODWPT), to the analysis of simulation signals and gear fault vibration signals measured by the acceleration sensor fixed on the bearing house. As long as the decomposition scale and disjoint dyadic decomposition are chosen suitably, the original signal could be decomposed into a set of monocomponent signals whose instantaneous amplitude and instantaneous frequency own physical meaning. After the instantaneous amplitude and instantaneous frequency of each monocomponent signal are calculated by using MODWPT, the corresponding Hilbert spectrum could be obtained by assembling the instantaneous amplitude and instantaneous frequency. The simulation and practical application examples show that the Hilbert spectrum base on the MODWPT is superior to another competing method, namely, EMD (empirical mode decomposition)-based method, which has been widely used in the gear fault diagnosis.

A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM

An accurate autoregressive (AR) model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system, which are experienced by the time-frequency analysis methods. However, AR model can only be applied to stationary signals, while the gear fault vibration signals usually present nonstationary characteristics. Therefore, empirical mode decomposition (EMD), which can decompose the vibration signal into a finite number of intrinsic mode functions (IMFs), is introduced into feature extraction of gear vibration signals as a preprocessor before AR models are generated. On the other hand, by targeting the difficulties of obtaining sufficient fault samples in practice, support vector machine (SVM) is introduced into gear fault pattern recognition. In the proposed method in this paper, firstly, vibration signals are decomposed into a finite number of intrinsic mode functions, then the AR model of each IMF component is established; finally, the corresponding autoregressive parameters and the variance of remnant are regarded as the fault characteristic vectors and used as input parameters of SVM classifier to classify the working condition of gears. The experimental analysis results show that the proposed approach, in which IMF AR model and SVM are combined, can identify working condition of gears with a success rate of 100% even in the case of smaller number of samples.

Application of frequency family separation method based upon EMD and local Hilbert energy spectrum method to gear fault diagnosis

Targeting the advantages of Hilbert–Huang transform (HHT) and the characteristics of gear fault vibration signals, HHT is introduced into gear fault diagnosis. The concept of local Hilbert energy spectrum is proposed and two gear fault diagnosis approaches, namely, frequency family separation method based on EMD (empirical mode decomposition) and local Hilbert energy spectrum method, are put forward, which are applied to gear fault diagnosis. Considering that the gear fault vibration signal is a multi-component amplitude-demodulated and frequency-demodulated (AM–FM) signal and EMD could exactly decompose the AM–FM signal into a number of intrinsic mode functions (IMFs), each of which can be amplitude-demodulated or frequency-demodulated component, the frequency families could be separated effectively from the gear vibration signal by applying EMD to the gear vibration signal. Furthermore, when faults occur in gear, the energy of the gear vibration signal would change correspondingly, whilst the local Hilbert energy spectrum can exactly provide the energy distribution of the signal in certain frequency with the change of the time and frequency. Thus, the fault information of the gear vibration signal can be extracted effectively from the local Hilbert energy spectrum. The analysis results from the experimental signals show that both frequency family separation method based on EMD and local Hilbert energy spectrum method could extract the characteristics information of the gear fault vibration signal effectively.