scholarly journals Multi-fault Diagnosis of Rolling Bearing using Two-dimensional Feature Vector of WP-VMD and PSO-KELM Algorithm

2021 ◽  
Author(s):  
tingyu jiang ◽  
Sheng Hong ◽  
Hao Liu
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Random Noise ◽  
Rolling Bearing ◽  
Two Dimensional ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Stable Performance ◽  
Learning Machine

Abstract In order to achieve accurate fault diagnosis of rolling bearing under random noise, a new fault diagnosis method based on wavelet packet-variational mode decomposition (WP-VMD) and kernel extreme learning machine (KELM) optimized by particle swarm optimization (PSO) is proposed in this paper. Firstly, the time-frequency domain feature vectors of the original rolling bearing fault signals are effectively obtained by preprocessing of WMD and decomposition and reconstruction of VMD. Then, the extracted two-dimensional feature vector is input into the KELM neural network for fault identification, and combined with PSO, KELM parameters were optimized. The experimental results show that the proposed method can effectively diagnose the rolling bearing under random noise, with the features of fast speed, stable performance and high accuracy. By comparison, this paper obtains better accuracy and real-time performance with fewer features, which provides a simple and efficient solution for fault diagnosis of rolling bearings.

Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery

2020 ◽  
pp. 107754632094971 ◽  
Author(s):  
Shoucong Xiong ◽  
Shuai He ◽  
Jianping Xuan ◽  
Qi Xia ◽  
Tielin Shi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Learning Algorithms ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Performance ◽  
Residual Network ◽  
Time Frequency ◽  
Correlation Information

Modern machinery becomes more precious with the advance of science, and fault diagnosis is vital for avoiding economical losses or casualties. Among massive diagnosis methods, deep learning algorithms stand out to open an era of intelligent fault diagnosis. Deep residual networks are the state-of-the-art deep learning models which can continuously improve performance by deepening the network structures. However, in vibration-based fault diagnosis, the transient property instability of vibration signal usually calls for time–frequency analysis methods, and the characters of time–frequency matrices are distinct from standard images, which brings some natural limitations for the diagnosis performance of deep learning algorithms. To handle this issue, an enhanced deep residual network named the multilevel correlation stack-deep residual network is proposed in this article. Wavelet packet transform is used to preprocess the sensor signal, and then the proposed multilevel correlation stack-deep residual network uses kernels with different shapes to fully dig various kinds of useful information from any local regions of the processed input. Experiments on two rolling bearing datasets are carried out. Test results show that the multilevel correlation stack-deep residual network exhibits a more satisfactory classification performance than original deep residual networks and other similar methods, revealing significant potentials for realistic fault diagnosis applications.

scholarly journals Research on Multi-Domain Fault Diagnosis of Gearbox of Wind Turbine Based on Adaptive Variational Mode Decomposition and Extreme Learning Machine Algorithms

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1375 ◽  
Author(s):  
Hui Li ◽  
Bangji Fan ◽  
Rong Jia ◽  
Fang Zhai ◽  
Liang Bai ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Grey Wolf Optimizer ◽  
Mechanical Equipment ◽  
Variational Mode Decomposition ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Fault Features ◽  
Frequency Domains ◽  
Learning Machine

Since variational mode decomposition (VMD) was proposed, it has been widely used in condition monitoring and fault diagnosis of mechanical equipment. However, the parameters K and α in the VMD algorithm need to be set before decomposition, which causes VMD to be unable to decompose adaptively and obtain the best result for signal decomposition. Therefore, this paper optimizes the VMD algorithm. On this basis, this paper also proposes a method of multi-domain feature extraction of signals and combines an extreme learning machine (ELM) to realize comprehensive and accurate fault diagnosis. First, VMD is optimized according to the improved grey wolf optimizer; second, the feature vectors of the time, frequency, and time-frequency domains are calculated, which are synthesized after dimensionality reduction; ultimately, the synthesized vectors are input into the ELM for training and classification. The experimental results show that the proposed method can decompose the signal adaptively, which produces the best decomposition parameters and results. Moreover, this method can extract the fault features of the signal more completely to realize accurate fault identification.

Fault Diagnosis of Rolling Bearing Based on S Transform and Image of Invariant Moments

2013 ◽  
Vol 706-708 ◽  
pp. 798-802 ◽  
Author(s):  
Jun Feng Guo ◽  
Bao Zeng Wang ◽  
Peng Wei ◽  
Xing Chun Wei
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Frequency Function ◽  
Time Signal ◽  
Two Dimensional ◽  
Moment Invariant ◽  
Time Frequency ◽  
Bearing Fault ◽  
Two Dimensional Image ◽  
Invariant Moments

According the characteristics of rolling bearing fault information are nonlinear and non-stationary, the method of time-frequency is often used to make the one dimensional time signal map into two dimensional time and frequency function, and describe the energy density of signal at different times and frequency simultaneously. A method of fault diagnosis based on S transformation and image Hu of invariant moments was put forward in this paper. First of all the measured rolling bearing signals have been S transformed, and time-frequency spectrum which is got is expressed as two dimensional image, then Hu geometric moment invariant of the S transformation spectrum is calculated and the simulation research is carried out using invariant moment principle in image processing. The results show that this method can distinguish the inner ring, outer ring and bearing roller fault intuitively and accurately and measure rolling bearing fault diagnosis efficiently.

scholarly journals Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis

2020 ◽  
Vol 10 (15) ◽  
pp. 5078
Author(s):  
Wenxiao Guo ◽  
Ruiqin Li ◽  
Yanfei Kou ◽  
Jianwei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Fault Simulation ◽  
Random Noise ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Mining Machinery

The feature extraction of composite fault of gearbox in mining machinery has always been a difficulty in the field of fault diagnosis. Especially in strong background noise, the frequency of each fault feature is different, so an adaptive time-frequency analysis method is urgently needed to extract different types of faults. Considering that the signal after complementary ensemble empirical mode decomposition (CEEMD) contains a lot of pseudo components, which further leads to misdiagnosis. The article proposes a new method for actively removing noise components. Firstly, the best scale factor of multi-scale sample entropy (MSE) is determined by signals with different signal to noise ratios (SNRs); secondly, the minimum value of a large number of random noise MSE is extracted and used as the threshold of CEEMD; then, the effective Intrinsic mode functions(IMFs) component is reconstructed, and the reconstructed signal is CEEMD decomposed again; finally, after multiple iterations, the MSE values of the component signal that are less than the threshold are obtained, and the iteration is terminated. The proposed method is applied to the composite fault simulation signal and mining machinery vibration signal, and the composite fault feature is accurately extracted.

scholarly journals Fault Diagnosis of Ball Bearing Elements: A Generic Procedure based on Time-Frequency Analysis

2019 ◽  
Vol 19 (4) ◽  
pp. 185-194 ◽  
Author(s):  
Meng-Kun Liu ◽  
Peng-Yi Weng
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Ball Bearing ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Support Vector ◽  
Time Frequency Analysis ◽  
Bearing Faults ◽  
Time Frequency ◽  
Low Efficiency

Abstract Motor-driven machines, such as water pumps, air compressors, and fans, are prone to fatigue failures after long operating hours, resulting in catastrophic breakdown. The failures are preceded by faults under which the machines continue to function, but with low efficiency. Most failures that occur frequently in the motor-driven machines are caused by rolling bearing faults, which could be detected by the noise and vibrations during operation. The incipient faults, however, are difficult to identify because of their low signal-to-noise ratio, vulnerability to external disturbances, and non-stationarity. The conventional Fourier spectrum is insufficient for analyzing the transient and non-stationary signals generated by these faults, and hence a novel approach based on wavelet packet decomposition and support vector machine is proposed to distinguish between various types of bearing faults. By using wavelet and statistical methods to extract the features of bearing faults based on time-frequency analysis, the proposed fault diagnosis procedure could identify ball bearing faults successfully.

scholarly journals A Novel Method of Fault Diagnosis for Rolling Bearing Based on Dual Tree Complex Wavelet Packet Transform and Improved Multiscale Permutation Entropy

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Guiji Tang ◽  
Xiaolong Wang ◽  
Yuling He
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Transform ◽  
Permutation Entropy ◽  
Feature Vectors ◽  
Novel Method ◽  
Complex Wavelet ◽  
Learning Machine ◽  
Low Dimensional

A novel method of fault diagnosis for rolling bearing, which combines the dual tree complex wavelet packet transform (DTCWPT), the improved multiscale permutation entropy (IMPE), and the linear local tangent space alignment (LLTSA) with the extreme learning machine (ELM), is put forward in this paper. In this method, in order to effectively discover the underlying feature information, DTCWPT, which has the attractive properties as nearly shift invariance and reduced aliasing, is firstly utilized to decompose the original signal into a set of subband signals. Then, IMPE, which is designed to reduce the variability of entropy measures, is applied to characterize the properties of each obtained subband signal at different scales. Furthermore, the feature vectors are constructed by combining IMPE of each subband signal. After the feature vectors construction, LLTSA is employed to compress the high dimensional vectors of the training and the testing samples into the low dimensional vectors with better distinguishability. Finally, the ELM classifier is used to automatically accomplish the condition identification with the low dimensional feature vectors. The experimental data analysis results validate the effectiveness of the presented diagnosis method and demonstrate that this method can be applied to distinguish the different fault types and fault degrees of rolling bearings.

Rolling Bearing Fault Feature Extraction Based on SVD-EEMD

2013 ◽  
Vol 411-414 ◽  
pp. 1067-1071 ◽  
Author(s):  
Cheng Wen ◽  
Chuan De Zhou
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Random Noise ◽  
Implementation Process ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Outer Race ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Mode Mixing

The novel method that singular value decomposition (SVD) is combined with ensemble empirical mode decomposition (EEMD) is proposed because of the mode mixing in empirical mode decomposition (EMD). The first step of this method is to reduce the random noise in fault signal by the SVD, and then does EEMD to restrain the mode mixing effectively. Finally, the intrinsic mode function (IMF) is done for envelope demodulation and as a result, the fault feature is extracted successfully. The implementation process was analyzed by simulation signal and this method has been successfully applied to in inner race and outer race of rolling bearing fault diagnosis. The results show that this method can extract the fault information of rolling bearing effectively and realize the precise fault diagnosis.

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