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

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

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Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1014.501 ◽

2014 ◽

Vol 1014 ◽

pp. 501-504 ◽

Cited By ~ 2

Author(s):

Shu Guo ◽

You Cai Xu ◽

Xin Shi Li ◽

Ran Tao ◽

Kun Li ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Roller Bearing ◽

Vibration Signal ◽

Classification Performance ◽

Outer Race ◽

Mode Decomposition ◽

Diagnosis Method ◽

Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

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Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM

Computational Intelligence and Neuroscience ◽

10.1155/2016/7657054 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Jian Xiong ◽

Shulin Tian ◽

Chenglin Yang

Keyword(s):

Fault Diagnosis ◽

Relative Entropy ◽

Analog Circuits ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Mode Decomposition ◽

Diagnosis Method ◽

Eemd Method ◽

Learning Machine ◽

Elm Classifier

This paper presents a novel fault diagnosis method for analog circuits using ensemble empirical mode decomposition (EEMD), relative entropy, and extreme learning machine (ELM). First, nominal and faulty response waveforms of a circuit are measured, respectively, and then are decomposed into intrinsic mode functions (IMFs) with the EEMD method. Second, through comparing the nominal IMFs with the faulty IMFs, kurtosis and relative entropy are calculated for each IMF. Next, a feature vector is obtained for each faulty circuit. Finally, an ELM classifier is trained with these feature vectors for fault diagnosis. Via validating with two benchmark circuits, results show that the proposed method is applicable for analog fault diagnosis with acceptable levels of accuracy and time cost.

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The Research of Fault Diagnosis Method of Roller Bearing Based on EMD and VPMCD

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1014.505 ◽

2014 ◽

Vol 1014 ◽

pp. 505-509 ◽

Cited By ~ 1

Author(s):

Ran Tao ◽

You Cai Xu ◽

Xin Shi Li ◽

Shu Guo ◽

Kun Li ◽

...

Keyword(s):

Fault Diagnosis ◽

Roller Bearing ◽

Classification Performance ◽

Fault Models ◽

Time Frequency ◽

Bearing Fault Diagnosis ◽

Mode Decomposition ◽

Diagnosis Method ◽

Nonlinear Signal ◽

Stable Classification

Empirical mode decomposition (EMD) can extract real time-frequency characteristics from the non-stationary and nonlinear signal. Variable prediction model based class discriminate (VPMCD) is introduced into roller bearing fault diagnosis in this paper. Therefore, a fault diagnosis method based on EMD and VPMCD is put forward in the paper. Firstly, the different feature vectors in the signal are extracted by EMD. Then, different fault models of roller bearing are distinguished by using VPMCD. Finally, an simulation example based on EMD and VPMCD is shown in this paper. The results show that this method can gain very stable classification performance and good computational efficiency.

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A Novel Wheelset Bearing Fault Diagnosis Method Integrated CEEMDAN, Periodic Segment Matrix, and SVD

Shock and Vibration ◽

10.1155/2018/1382726 ◽

2018 ◽

Vol 2018 ◽

pp. 1-18 ◽

Cited By ~ 2

Author(s):

Chenguang Huang ◽

Jianhui Lin ◽

Jianming Ding ◽

Yan Huang

Keyword(s):

Fault Diagnosis ◽

Singular Values ◽

Ensemble Empirical Mode Decomposition ◽

Singular Value ◽

Bearing Fault Diagnosis ◽

Mode Decomposition ◽

Diagnosis Method ◽

Adaptive Noise ◽

Periodic Impulse ◽

Value Decomposition

A novel fault diagnosis method, named CPS, is proposed based on the combination of CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), PSM (periodic segment matrix), and SVD (singular value decomposition). Firstly, the collected vibration signals are decomposed into a set of IMFs using CEEMDAN. Secondly, the PSM of the selected IMFs is constructed. Thirdly, singular values are obtained by SVD conducted on the space of PSM. Fourthly, the impulse components are enhanced by the singular value reconstruction with the first maximal singular value. Finally, the squared envelope spectra of the reconstructed signals are used to diagnose the wheelset bearing faults. The effectiveness of the proposed CPS has been verified by simulations and experiments. Compared to the well-known Hankel-based SVD, the proposed CPS performs better at extracting the weak periodic impulse responses from the measured signals with strong noise and interferences.

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A fault diagnosis method of rolling element bearings based on CEEMDAN

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406215624126 ◽

2015 ◽

Vol 231 (10) ◽

pp. 1804-1815 ◽

Cited By ~ 22

Author(s):

Yaguo Lei ◽

Zongyao Liu ◽

Julien Ouazri ◽

Jing Lin

Keyword(s):

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Rolling Element Bearings ◽

Intrinsic Mode Functions ◽

Residual Noise ◽

Mode Decomposition ◽

Rolling Element ◽

Noisy Signals ◽

Diagnosis Method

Ensemble empirical mode decomposition (EEMD) represents a valuable aid in empirical mode decomposition (EMD) and has been widely used in fault diagnosis of rolling element bearings. However, the intrinsic mode functions (IMFs) generated by EEMD often contain residual noise. In addition, adding different white Gaussian noise to the signal to be analyzed probably produces a different number of IMFs, and different number of IMFs makes difficult the averaging. To alleviate these two drawbacks, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was previously presented. Utilizing the advantages of CEEMDAN in extracting weak characteristics from noisy signals, a new fault diagnosis method of rolling element bearings based on CEEMDAN is proposed. With this method, a particular noise is added at each stage and after each IMF extraction, a unique residue is computed. In this way, this method solves the problem of the final averaging and obtains IMFs with less noise. A simulated signal is used to illustrate the effectiveness of the proposed method, and the decomposition results show that the method obtains more accurate IMFs than the EEMD. To further demonstrate the proposed method, it is applied to fault diagnosis of locomotive rolling element bearings. The diagnosis results prove that the method based on CEEMDAN may reveal the fault characteristic information of rolling element bearings better.

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Roller Bearing Fault Diagnosis Based on IMF Kurtosis and SVM

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.694-697.1160 ◽

2013 ◽

Vol 694-697 ◽

pp. 1160-1166

Author(s):

Ke Heng Zhu ◽

Xi Geng Song ◽

Dong Xin Xue

Keyword(s):

Fault Diagnosis ◽

Roller Bearing ◽

Fault Classification ◽

Support Vector ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Mode Decomposition ◽

Diagnosis Method ◽

Bearing Vibration ◽

The Imf

This paper presents a fault diagnosis method of roller bearings based on intrinsic mode function (IMF) kurtosis and support vector machine (SVM). In order to improve the performance of kurtosis under strong levels of background noise, the empirical mode decomposition (EMD) method is used to decompose the bearing vibration signals into a number of IMFs. The IMF kurtosis is then calculated because of its sensitivity of impulses caused by faults. Subsequently, the IMF kurtosis values are treated as fault feature vectors and input into SVM for fault classification. The experimental results show the effectiveness of the proposed approach in roller bearing fault diagnosis.

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A New Rotor Fault Diagnosis Method Based on EEMD Sample Entropy and Grey Relation Degree

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.426 ◽

2013 ◽

Vol 347-350 ◽

pp. 426-429 ◽

Cited By ~ 2

Author(s):

Wen Bin Zhang ◽

Yan Jie Zhou ◽

Jia Xing Zhu ◽

Ya Song Pu

Keyword(s):

Fault Diagnosis ◽

Rank Order ◽

Ensemble Empirical Mode Decomposition ◽

Sample Entropy ◽

Morphological Filter ◽

Sampled Data ◽

Intrinsic Mode Functions ◽

Mode Decomposition ◽

Diagnosis Method ◽

Grey Relation

In this paper, a new rotor fault diagnosis method was proposed based on rank-order morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey relation degree. Firstly, the sampled data was de-noised by rank-order morphological filter. Secondly, the de-noised signal was decomposed into a finite number of stationary intrinsic mode functions (IMFs). Thirdly, some IMFs containing the most dominant fault information were calculated the sample entropy for four rotor conditions. Finally, the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in rotor fault diagnosis. Its suitable for on-line monitoring and diagnosis of rotating machinery.

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The Application of EMD and ARMA Bi-Cepstrum Fault Diagnosis Method in Gearbox of Overhead Traveling Crane

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.308-310.88 ◽

2011 ◽

Vol 308-310 ◽

pp. 88-91

Author(s):

Hong Bo Xu ◽

Guo Hua Chen ◽

Xin Hua Wang ◽

Jun Liang

Keyword(s):

Fault Diagnosis ◽

Evaluation Method ◽

Moving Average ◽

Arma Model ◽

Time Varying ◽

Stability Evaluation ◽

Test Results ◽

Mode Decomposition ◽

Diagnosis Method ◽

Auto Regressive

For the time varying of signals, empirical mode decomposition (EMD) is occupied to modulate signals; auto-regressive moving average (ARMA) of higher accuracy is used to establish model for the signal principal components; then parametric bi-cepstrum estimation is implemented and fault feature is extracted. The test results about gearbox of overhead traveling crane indicate: the feature quefrency can be obtained through method of EMD and ARMA model parametric bi-cepstrum estimation.It is a kind of effective fault diagnosis and stability evaluation method.

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