Gear Fault Diagnosis Research Articles

Gear Fault Diagnosis based on Variational Mode Decomposition and Envelope Spectrum

Gear Fault Diagnosis based on Variational Mode Decomposition and Envelope Spectrum

Diagnosis of gear tooth fault in a bevel gearbox using discrete wavelet transform and autoregressive modeling

Vibration signals from any dynamic system are measured with respect to time. Different options are available to analyze these measured signals due to the advancement in computing and signal processing techniques. In the present work, a methodology comprising of discrete wavelet transform and autoregressive model has been proposed for detection of single tooth fault in single stage reduction bevel gearbox. An autoregressive model is constructed using detailed coefficients of discrete wavelet transform to highlight the presence of the fault in gearbox. The results show that variance of autoregressive coefficients obtained for faulty signal is more than the variance of autoregressive coefficients extracted from healthy signal. Based on the results, it is concluded that the proposed methodology can be used as health condition indicator of the gearbox system.

Data-driven time-frequency analysis method based on variational mode decomposition and its application to gear fault diagnosis in variable working conditions

The data-driven time-frequency analysis (DDTFA)-method-based initial phase selection directly influences the convergence and calculation results of the algorithm. The central frequency accuracy of each component by means of variational mode decomposition (VMD) is high, the calculation speed of VMD is fast, and the method shows satisfactory noise resistance, but the decomposed components are sensitive to noise. A VMD method that is complementary to the DDTFA method is introduced in this paper to estimate the initial phase of DDTFA, and the VMD-DDTFA method is proposed for time-varying non-stationary signals. This method first analyzes the time-varying non-stationary signal through VMD and estimates the initial phase of each signal component, then uses DDTFA to sparsely decompose the signal after phase smoothing. To examine the analytical ability of VMD-DDTFA on time-varying non-stationary signals, the five aspects of the VMD-DDTFA method, accuracy, noise resistance, efficiency, applicability and anti-mode-mixing ability, are analyzed. The VMD-DDTFA method compares with the current commonly used signal analysis methods of VMD and ensemble empirical mode decomposition (EEMD) and the comparison results confirm that VMD-DDTFA has a superior decomposition accuracy, satisfactory noise resistance and efficiency. In addition, VMD-DDTFA features strong anti-mode mixing and applicability even under strong noise. The VMD-DDTFA method is applied to the fault diagnosis of measured gear crack and broken tooth in variable working conditions. In addition, the results of the VMD-DDTFA method are compared with those obtained by VMD and EEMD; the results verify the effectiveness and superiority of the method.

Gear Fault Diagnosis Based on Dual Parameter Optimized Resonance-Based Sparse Signal Decomposition of Motor Current

Motor current signature analysis (MCSA) provides a nondestructive and remote approach for a gear fault diagnosis. However, in addition to the fault-related components, motor current in the faulty gear system also contains the eccentricity-related components and gear meshing-related components, which contaminate the fault features and increase the difficulty of fault diagnosis. To extract fault features from these interferences, this paper proposes the dual parameters optimized resonance-based sparse signal decomposition (RSSD) method, which can decompose a complex signal into a high- and low-resonance component with two sets of overcomplete wavelet bases. After the decomposition, the fault-related components, which have short duration, will exist in low-resonance component. The novelty is that the wavelet bases related parameters, Q-factors, and decomposition levels are chosen automatically based on artificial bee colony algorithm to obtain the optimal decomposition results instead of chosen subjectively. Kurtosis of the low-resonance component is employed as optimization index. The proposed method is then verified on the gear fault-diagnosis platform, which consists of two permanent magnet synchronous motors and a pair of gears with transmission ratio of 3:2, and its effectiveness over some existing methods under different operating conditions is also validated.

Gear fault diagnosis based on recurrence network

Gear fault diagnosis based on recurrence network

Planetary Gear Fault Diagnosis via Feature Image Extraction Based on Multi Central Frequencies and Vibration Signal Frequency Spectrum.

Poor working environment leads to frequent failures of planetary gear trains. However, complex structure and variable transmission make the vibration signal strongly non-linear and non-stationary, which brings big problems to fault diagnosis. A method of planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum is proposed. The original vibration signal is decomposed by variational mode decomposition (VMD), and four components with narrow bands and independent central frequencies are decomposed. In order to retain the feature spectrum of the original vibration signal as far as possible, the corresponding feature bands are intercepted from the frequency spectrum of original vibration signal based on the central frequency of each component. Then, the feature images of fault signals are constructed as the inputs of the convolution neural network (CNN), and the parameters of the neural network are optimized by sample training. Finally, the optimized CNN is used to identify fault signals. The overall fault recognition rate is up to 98.75%. Compared with the feature bands extracted directly from the component spectrums, the extraction method of the feature bands proposed in this paper needs fewer iterations under the same network structure. The method of planetary gear fault diagnosis proposed in this paper is effective.

Multiple information fusion based on single component feature and diagnosis of gear’s early fault

A method of multiple information fusion based on single component feature (MIFSCF) is proposed and applied to diagnose early fault of gear. Firstly, meshing frequency component is separated by Fractional Fourier Transform (FRFT) as single component signal, whose order envelop demodulation spectrum (OEDS) is calculated next, and the value of OEDS at feature order of gear’s fault is selected as feature parameter. Feature parameters of different sensors are fused by radar graph and analysis results show that MIFSCF is more effective and accurate than the method based on single sensor in gear fault diagnosis.

Novel gear fault diagnosis approach using native Bayes uncertain classification based on PSO algorithm

Traditionally, gear faults can be classified with the ignorance of the sample uncertainty. In this paper, a novel approach is proposed for the problem diagnosis of uncertain gear interval faults. First, a statistical property interval feature vector composed of mean, standard deviation, skewness, kurtosis, etc. is proposed. Then, the native Bayes uncertain classification (NBU) is used for the diagnostics of these uncertain gear interval faults. Conventionally, the NBU utilizes all the attributes to distinguish fault types. However, each fault type has its own distinct classification accuracy for different feature vector attributes. Thus, the particle swarm optimization (PSO) is used to select the optimal feature vector attributes for each fault type in the NBU (NBU_PSO_EACH). The experimental results show: (1) the accuracy of the proposed method is better than that of NBU1, NBU2 or FBC; (2) in terms of accuracy, the proposed method is also more advanced than the method which selects the same optimal attributes for all fault types based on the PSO (NBU_PSO); (3) the proposed method can reduce the physical size of feature vectors.

An image recognition method for gear fault diagnosis in the manufacturing line of short filament fibres

The Key Laboratory of Expressway Construction Machinery of Shaanxi Province (No 310825161123), NSFC (No 51505475), Yingcai Project of CUMT (YC2017001), Priority Academic Program Development of Jiangsu Higher Education Institutions and the UOW Vice-Chancellor’s Postdoctoral Research Fellowship.

Fault Diagnosis of Rotating Machinery Based on the Multiscale Local Projection Method and Diagonal Slice Spectrum

The vibration signals of bearings and gears measured from rotating machinery usually have nonlinear, nonstationary characteristics. The local projection algorithm cannot only reduce the noise of the nonlinear system, but can also preserve the nonlinear deterministic structure of the signal. The influence of centroid selection on the performance of noise reduction methods is analyzed, and the multiscale local projection method of centroid was proposed in this paper. This method considers both the geometrical shape and statistical error of the signal in high dimensional phase space, which can effectively eliminate the noise and preserve the complete geometric structure of the attractors. The diagonal slice spectrum can identify the frequency components of quadratic phase coupling and enlarge the coupled frequency component in the nonlinear signal. Therefore, the proposed method based on the above two algorithms can achieve more accurate results of fault diagnosis of gears and rolling bearings. The simulated signal is used to verify its effectiveness in a numerical simulation. Then, the proposed method is conducted for fault diagnosis of gears and rolling bearings in application researches. The fault characteristics of faulty bearings and gears can be extracted successfully in the researches. The experimental results indicate the effectiveness of the novel proposed method.

A Comparative Study of Motor Stator Current Analysis and Instantaneous Power Analysis Techniques for the Diagnosis of Gear Faults under Medium Load Conditions

A Comparative Study of Motor Stator Current Analysis and Instantaneous Power Analysis Techniques for the Diagnosis of Gear Faults under Medium Load Conditions

Measurement method of torsional vibration signal to extract gear meshing characteristics

A technique in measuring torsional vibration signal based on an optical encoder and a discrete wavelet transform is proposed for the extraction of gear meshing characteristics. The method measures the rotation angles of the input and output shafts of a gear pair by using two optical encoders and obtains the time interval sequences of the two shafts. By spline interpolation, the time interval sequences based on uniform angle sampling can be converted into angle interval sequences on the basis of uniform time sampling. The curve of the relative displacement of the gear pair on the meshing line (initial torsional vibration signal) can then be obtained by comparing the rotation angles of the input and output shafts at the interpolated time series. The initial torsional vibration signal is often disturbed by noise. Therefore, a discrete wavelet transform is used to decompose the signal at certain scales; the torsional vibration signal of the gear can then be obtained after filtering. The proposed method was verified by simulation and experimentation, and the results showed that the method could successfully obtain the torsional vibration signal of the gear at a high frequency. The waveforms of the torsional vibration could reflect the meshing characteristics of the teeth. These findings could provide a basis for fault diagnosis of gears.

Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS.

For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.

Gear Fault Diagnosis Based on BP Neural Network

Gear transmission is more complex, widely used in machinery fields, which form of fault has some nonlinear characteristics. This paper uses BP neural network to train the gear of four typical failure modes, and achieves satisfactory results. Tested by using test data, test results have an agreement with the actual results. The results show that the BP neural network can effectively solve the complex state of gear fault in the gear fault diagnosis.

LCD denoise and the vector mutual information method in the application of the gear fault diagnosis under different working conditions

In this paper, the full vector LCD method is proposed to solve the misjudgment problem caused by the change of the working condition. First, the signal from different working condition is decomposed by LCD, to obtain the Intrinsic Scale Component (ISC)whose instantaneous frequency with physical significance. Then, calculate of the cross correlation coefficient between ISC and the original signal, signal denoising based on the principle of mutual information minimum. At last, calculate the sum of absolute Vector mutual information of the sample under different working condition and the denoised ISC as the characteristics to classify by use of Support vector machine (SVM). The wind turbines vibration platform gear box experiment proves that this method can identify fault characteristics under different working conditions. The advantage of this method is that it reduce dependence of man’s subjective experience, identify fault directly from the original data of vibration signal. It will has high engineering value.

Comparison of wavelet based denoising schemes for gear condition monitoring: An Artificial Neural Network based Approach

Vibration Analysis has been extensively used in recent past for gear fault diagnosis. The vibration signals extracted is usually contaminated with noise and may lead to wrong interpretation of results. The denoising of extracted vibration signals helps the fault diagnosis by giving meaningful results. Wavelet Transform (WT) increases signal to noise ratio (SNR), reduces root mean square error (RMSE) and is effective to denoise the gear vibration signals. The extracted signals have to be denoised by selecting a proper denoising scheme in order to prevent the loss of signal information along with noise. An approach has been made in this work to show the effectiveness of Principal Component Analysis (PCA) to denoise gear vibration signal. In this regard three selected wavelet based denoising schemes namely PCA, Empirical Mode Decomposition (EMD), Neighcoeff Coefficient (NC), has been compared with Adaptive Threshold (AT) an extensively used wavelet based denoising scheme for gear vibration signal. The vibration signals acquired from a customized gear test rig were denoised by above mentioned four denoising schemes. The fault identification capability as well as SNR, Kurtosis and RMSE for the four denoising schemes have been compared. Features extracted from the denoised signals have been used to train and test artificial neural network (ANN) models. The performances of the four denoising schemes have been evaluated based on the performance of the ANN models. The best denoising scheme has been identified, based on the classification accuracy results. PCA is effective in all the regards as a best denoising scheme.

Diagnosis of Gear Faults by Using the Linear Canonical Transform in Acceleration and Deceleration Processes

The vibration signals’ analysis is a very effective and reliable approach for diagnosing the gear faults. Since the vibration signals obtained from the gear in the acceleration and deceleration processes can often contain more beneficial fault features, the analysis of the gear vibration signals during the acceleration and deceleration processes has been a hot research topic. However, the vibration signals acquired from the gear in the acceleration and deceleration processes are nonstationary, which make the current Fourier transform (FT)-based vibration signal analysis methods invalidly. Meanwhile, the linear canonical transform (LCT) as a novel nonstationary signal analysis tool has received wide attention. Therefore, this paper proposes to use the LCT instead of the FT-based methods to diagnose the gear faults in the acceleration and deceleration processes. The simulations and the experimental evaluations are all presented to prove the validity of the LCT method in gear fault diagnosis.

Gear Fault Diagnosis in Variable Speed Condition Based on Multiscale Chirplet Path Pursuit and Linear Canonical Transform

The vibration signals analysis is a very effective and reliable method for detecting the gear failures. Because the vibration signals acquired from the gear in the variable speed condition often contain more useful fault information, the analysis of the gear vibration signals during the variable speed condition has been a hot research topic. In this paper, a method based on the multiscale chirplet path pursuit (MSCPP) and the linear canonical transform (LCT) has been applied to diagnose the gear fault in the variable speed condition for the first time. First, by using the MSCPP method to estimate the instantaneous meshing frequency, the suitable signal segment approximation to the acceleration or deceleration process can be selected. Then, because the LCT is a novel and efficient nonstationary signals analysis tool, the optimal LCT spectrum of the selected signal has been attainted to diagnose the gear faults based on the properties of the LCT. In addition, the simulations and the experimental evaluation are provided to verify the effectiveness of the proposed method.

A Novel Detrended Fluctuation Analysis Method for Gear Fault Diagnosis Based on Variational Mode Decomposition

The vibration signal of heavy gearbox has the nonlinear and nonstationary characteristic, which makes the gear fault diagnosis difficult. Moreover, the useful fault information is mainly focused on the high‐frequency components of the raw signal, which also affects the fault feature extraction from vibration signal. For this reason, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to diagnose the gear faults of heavy gearbox. Since high‐frequency component contains more fault information, the raw vibration signal is decomposed several mode components by VMD, which can remove the low‐frequency component to retain the high‐frequency component. Moreover, the most sensitive mode component is selected in these high‐frequency components by a maximal indicator, which is composed of kurtosis and correlation coefficient. The most sensitive mode component is calculated by DFA to obtain bi‐logarithmic map, and the sliding windowing algorithm is employed to capture turning point of the bi‐logarithmic map, thus extracting the fault feature of small time scale to identify gear faults. The effectiveness of the proposed method for fault diagnosis is validated by experimental data analysis, and the comparison results demonstrate that the recognition rate of gear faults condition have marked improvement by proposed method than the DFA of small time scale (STS‐DFA) and EMD‐DFA.

Weak Fault Signal Detection of Rotating Machinery Based on Multistable Stochastic Resonance and VMD‐AMD

For solving detection problems of multifrequency weak signals in noisy background, a novel weak signal detection method based on variational mode decomposition (VMD) and rescaling frequency‐shifted multistable stochastic resonance (RFMSR) with analytical mode decomposition (AMD) is proposed. In this method, different signal frequency bands are processed by rescaling subsampling compression to make each frequency band meet the conditions of stochastic resonance. Before the enhanced signal components are synthesized, they are processed to achieve the enhanced signal by means of AMD, leaving only the enhanced sections of the signal. The processed signal is decomposed into intrinsic mode functions (IMF) by VMD, in order to require the detection of weak multifrequency signals. The experimental analysis of the rolling bearing inner ring fault and gear fault diagnosis demonstrate that the proposed method can not only enhance signal amplitude, reduce false components, and improve the VMD algorithm’s accuracy, but also effectively detect weak multifrequency signals submerged by noise.